The Insight Story

Unlocking M&A Potential: The Impact of Fed’s Rate Strategy

The Fed’s recent decision to cut rates, its first reduction since 2020, signals a positive revival in M&A activity by reducing borrowing costs and boosting dealmaker confidence. With inflation nearing the Fed’s target, further cuts are expected, though the timing remains uncertain, adding complexity to the M&A outlook.  Policymakers had anticipated a cut of 25 bps, but the Fed opted for a more aggressive stance by reducing the rate cut by 50 bps. The cut was imminent as inflation in the US eased towards the central bank’s target of 2% and unemployment rose to a three-year high. The trajectory of the future rate cut is evident; however, the timing and extent of reduction are still unclear. J.P. Morgan Research expects the Fed to cut rates by another 50 bps at its next meeting in early November.  For the past couple of months, there was a divergence between the Fed’s and other central bank policies. The European Central Bank (ECB), which started reducing its interest rates in June 2024, temporarily benefitted the European sponsors with lower debt costs and boosted M&A activity. Consequently, the gap in deal-making between Europe and the US started to close. The impact of monetary easing is already evident in Europe, where the ECB, Swiss National Bank, and Sweden’s Riksbank reduced interest rates in 2Q24. This policy shift contributed to a 17% increase in European M&A deal value compared to the previous quarter, as reported by PitchBook.    Figure 1. US Deal Volume and Value  Source: PwC   Note: M&A excludes deals that target assets or products. *2Q24 is for April/May only  The total US deal value for the first five months of 2024 reached $535 billion, marking an increase of nearly 30% compared to $412 billion during the same period last year, according to PwC. In the 1H24, the technology services sector in the US dominated the M&A activity, both in terms of number of deals and transaction value.   Read more: US Merger and Acquisition (M&A) 2H24 Outlook        Strategic buyers use leverage more conservatively than PE, focusing on unlocking greater synergies from the combined entity, especially in the case of mergers of equals, where they can avoid significant cash or debt financing. Historically, corporate US M&A deal volumes have been relatively stable, with around 1,000 deals exceeding $100 million annually. The PE-related deal flow, on the other hand, being rate-sensitive and mostly debt-driven, lagged in dealmaking owing to costly debt-financing and fundraising challenges until 2Q24. General Partners (GPs) used several strategies to overcome the exit bottleneck, such as outright sales with minority roll-overs, continuation funds, and partial exits, according to Pitchbook. Despite some recovery, with the US M&A deal involving financial buyers reaching its highest level since May 2022 in April 2024, further rate cuts are expected to provide additional momentum, helping financial buyers emerge from their two-year dealmaking slump.  Significant dry powder is expected to ramp up the M&A activity for strategic and financial buyers, according to Pitchbook. As of early May, PE firms in the US hold over $1 trillion in dry powder, according to Preqin. Strategic buyers also have significant cash reserves for growth and are well-positioned to act decisively when market conditions are attractive.  Valuation Multiples and Leverage Dynamics  Figure 2: Annual Averages for LBO Valuation Multiples and Leverage Ratios in the U.S.  Source: Baird  Statistically, there is a strong relationship between valuation multiples (for example, EV/EBITDA) and leverage ratios (for example, Total Debt/EBITDA), i.e., higher debt levels are associated with a rise in valuation multiples. Consequently, the new loan yields are expected to fall dramatically as the Fed reduces interest rates. High purchase multiples indicate robust earnings potential and the company’s ability to service large amounts of debt. On the flip side, acquirers using high leverage will offer more competitive bids without increasing the amount of equity they contribute, driving the valuation of the target company.  Read more: Impact Fund Opportunity: Spotlight on Emerging Managers          Macroeconomic Landscape  Although Fed rate cuts typically trigger a surge in M&A, a looming fear of recession could curb this expected uptick. Historically, rate cuts often precede recessions; some would take years to follow, and others occur immediately after the cut. The Fed has had six cutting cycles since 1990, excluding the rate cuts in the pandemic. On average, the economy has entered a recession 18 months after the Fed began cutting interest rates. In such a case, companies will be reluctant to indulge in M&A activity and prefer organic growth to safeguard themselves from economic downturns.  The economy would experience inflated asset prices, and some companies would overpay for the targets, which would hamper their returns in the long run. On the other hand, consumer-driven industries would face liquidity issues in a slowing economy with higher unemployment due to low consumer demands and reduced disposable income. They would seem unattractive as targets, but financially stronger firms will see this as an opportunity and buy these distressed firms at a discounted valuation.   Conclusion  In the foreseeable future, companies with significant debt will pursue deleveraging before pushing acquisitions. Additionally, there will be a flux in M&A in the technology sector, especially AI, as it will create opportunities, bring innovation, and pace the M&A activity. In the long term, cheaper financing will lead to strategic expansions as companies focus on acquiring the right capabilities and divesting the non-core assets for the sustainability of their business, leading to a surge in M&A deals as economic uncertainty declines. Finally, higher corporate valuations are anticipated following the overall economic recovery after the rate cuts.  Partner of choice for lower middle market-focused investment banks and private equity firms, SG Analytics provides offshore analysts with support across the deal life cycle. Our complimentary access to a full back-office research ecosystem (database access, graphics team, sector & and domain experts, and technology-driven automation of tactical processes) positions our clients to win more deal mandates and execute these deals in the most efficient manner.    About SG Analytics            SG Analytics (SGA) is an industry-leading global data solutions firm providing data-centric research and contextual analytics services to its clients, including Fortune 500 companies, across BFSI, Technology, Media & Entertainment, and Healthcare sectors. Established in 2007, SG Analytics is a Great Place to Work® (GPTW) certified company with a team of over 1200 employees and a presence across the U.S.A., the UK, Switzerland, Poland, and India.        Apart from being recognized by reputed firms such as Gartner, Everest Group, and ISG, SGA has been featured in the elite Deloitte Technology Fast 50 India 2023 and APAC 2024 High Growth Companies by the Financial Times & Statista.                             

Healthcare Market Research in the Era of New Technology

Not long ago, traditional healthcare market research was the mainstay, if not the only method, to gain insights into the healthcare market. However, this has changed, with stakeholders in the healthcare industry increasingly trusting technology and data-driven market research. Several significant factors have contributed to this shift, such as technological innovations, changes in regulations, evolving patient and brand expectations, and the limitations of traditional methods. Given this scenario, the move towards modern techniques is inevitable, raising questions about the future efficacy of conventional research.   Over time, the research landscape has also changed, bringing certain clear requirements to the forefront across all segments of healthcare research :   Infusion of technology    Demand for customization   From the obvious to the unknown   Ready to listen and act   Limitations of Traditional Healthcare Market Research   Traditional research alone was unable to address the above, mainly due to:   Time/Resource Intensive and Cost  Methods such as surveys, interviews, and focus groups often require significant time to gather and analyze data, which can delay the availability of insights. The process is expensive, considering participant recruitment, data collection, and analysis.   Lack of Real-time Insights  Traditional approaches generally capture data at a single point of time/moment. As a result, it may not always reflect ongoing changes in healthcare market dynamics or patient behaviors.   Small Sample Sizes and Risk of Bias  Traditional processes work with a limited sample size, which may not statistically represent the larger audience. Also, various biases, such as selection of data, external influences, personal choice, etc., may skew insights.   Challenges with Longitudinal Data   Healthcare research data is ever-changing. So, tracking changes over time through traditional approaches can be challenging and costly. Additionally, insights produced from the fragmented data may not lead to any real actionable plans.      Inflexibility  Traditional methods are not flexible enough, or only partially so. Once data is collected, it is difficult to modify, update or refine. As a result, the outcomes are often not aligned with emerging trends and changes.    Introduction of Technology in Healthcare Market Research   While the early part of the millennium saw the emergence of online panels, and digital research continued to spread its wings, it was around 2010 when researchers saw merit in joining the deep-tech bandwagon in search of real-time and passive data. Somewhere around 2009, when Fitbit was launched, a new era for the healthcare market research industry dawned in response to rapid advances in technology. AI, machine learning, and the cloud became ubiquitous, and the literal explosion of mobile/portable devices such as smartphones, tablets, and wearable devices created an opportunity that never existed earlier.    From traditional techniques such as data collection through interviews, journals, interview transcripts, and medical literature, healthcare market research started moving to online telehealth platforms to conduct interviews and discussions virtually, collect large volumes of data from different sources such as social media platforms and wearable devices, and use artificial intelligence for data analytics.    It's Now or Never!   Most traditional methods would capture retrospective and stated data for research. For example, a patient’s heart rate during a treadmill test and its significance or a patient’s opinion via a survey on how a particular drug helped them. Data was hardly real-time, and hence, decision-making had to account for such scenarios, which were often inconclusive.   Enter wearables and IoT!   Eyeglasses were the very first wearable device to be developed in 1286, Fitbit in 2009, Apple Watch in 2014, and Oculus Rift in 2015. Wearable technology has grown significantly. The gradual adoption of wearables in research seemed par for the course, with researchers thoroughly encouraged by prospects of direct access to real-time data and lots of it!   Smart glasses and watches, health trackers, smart clothing, virtual reality, etc., all have immense potential to convincingly alter the way real-world data is collected and interpreted. Just the way mobile/handheld devices helped evolve qualitative research, the integration of wearables offers the ability to capture in-the-moment, in-depth data with relative ease. With applications from remote patient monitoring to chronic disease management, adherence to medication, physiological monitoring, and smart implants, IoT is a game changer in healthcare research through continuous, real-time data that enhances patient care and drives medical advancements.    How Big is Actually Big?    With the proliferation of unstructured data from various sources such as journals, biometric data, electronic medical records (EMR), Internet of Things (IoT), social media, payer claims and records, and data banks, there's an abundance of real-time data (big data) readily available, uncovering patterns and trends previously inaccessible due to limitation of sample size. When data elements are readily scalable, results are also equally accurate and reliable. The transition from volume to value-based care is driving the demand for big data analytics, as healthcare providers are seeking new innovations to improve patient outcomes while parallelly managing costs.   Precision medicine is a key area where the application of big data has been critical, enabling medical professionals to customize treatments and procedures to improve outcomes. In addition, big data has also been effective in identifying disease risk and providing early detection systems applicable to large population segments, including predicting patient admission rates and managing available resources effectively. For example, SOPHiA GENETICS’ platform analyses and generates insights from genomic data, helping researchers create drugs tailored to patients’ needs and clinicians to offer more effective treatments.      Tempus and Flatiron Health are making good utilization of big data in oncology research. Tempus is creating diverse groups of molecular and clinical data, which will help physicians with customized case scenarios. Flatiron Health uses billions of cancer patients' data points to discover new insights and raise standards of care.    Read more: Importance of Data Analytics in the Healthcare Industry  Move Aside Millennials! It’s the age of Gen AI!     With the advent of Artificial Intelligence (AI) & Machine Learning (ML) technologies, vast datasets can now be processed in real-time to predict market shifts, patient behaviors, and the development of new products and services. New AI/ML methods would capture real-time data that would be more credible and scalable. It can be used to answer the pertinent questions of “Where Should You Go”, “Who Should You Target?” and “How and When Should You Target?”     AI makes the identification of potential markets much faster, analyzing large data sets in real-time from research papers, industry reports, and social networks and providing the analyst with opportunities. Moreover, there are segments where using traditional segmentation tools is rarely possible, for instance, rare diseases or patients with hard-to-reach access to products, and where the application of AI is highly beneficial.    While AI has long been a valuable tool in market research, the current buzz centers around GenAI powered by large language models (LLMs). Unlike traditional analytical AI, these LLMs are trained on massive datasets, enabling them to perform language tasks with near-human accuracy at scale. This advancement fuels innovative GenAI applications, including chatbots and interactive survey experiences, revolutionizing healthcare market research.   For example – In big and global pharma companies, smaller markets were neglected as they were too costly to research manually. However, Gen AI can generate synthetic data from these markets, which are also free of bias and can be collected at significantly lower costs.   The Rise of the Patients   Patients played a reactive role in contributing to healthcare market research, and their roles would be restricted to interviews and discussions.    Read more: The Ethics of Healthcare Technology: Balancing Innovation and Patient Privacy  Now, in the new technology era, social media monitoring platforms and specialized health forums offer insights into patient sentiments and discussions in real-time. Being unsolicited and user-generated content, this data has tremendous value. Technology and social media have enabled patients to proactively provide inputs in the form of feedback, reviews, and ratings in various channels. For example, various social media and internet forums and websites collect feedback on hospitals, drugs, and health insurers. Such platforms are veritable goldmines for healthcare research agencies because, unlike in the past, they would find large volumes of readily available first-party patient data.    For example – Listening to social media conversations can help in identifying and tracking HCPs in a specific therapy area and identify oncologists in the field of immuno-oncology based on the number of posts and the topics they have covered.   Say Hello to Synthetic Data!   While still an extremely sensitive and debatable topic, synthetic data has, in the last few years, been the focus of discussions at top market research events. Its impact on research is still unverified; there seem to be quite a few areas to which it can contribute. Synthetic data has been used to simulate the spread of infectious diseases like COVID-19. This helps researchers understand potential outbreak scenarios and evaluate the effectiveness of public health interventions without compromising real patient data.      The generation and integration of synthetic datasets can potentially mimic the statistical properties of real-world data and address many access, privacy, and confidentiality barriers. For example, the Synthea project generated synthetic patient data that mirrors real-world healthcare scenarios, allowing developers to test new health IT solutions in a risk-free environment.     Apart from being fast and cost-effective, the accuracy of synthetic data in creating personas of interest and engaging in interactive dialogues with these is of active interest to the fraternity. Not only does it accelerate insight generation and, subsequently, product development, but it substantially reduces risks of failure.      One such excellent application is digital twin technology, creating virtual replicas of physical healthcare systems or processes. This technology is useful in simulating and predicting, in real-time, patients' behaviors for the design of patient-specific treatment plans and optimized operations. In that regard, digital twin models of hospital operations will improve efficiency, cut costs, and provide better care through better allocation and management of resources.   From Regulations to Revelations    Blockchain technology has the potential to revolutionize market research by enhancing data security and privacy, which can build trust and increase participation rates. This technology ensures that data is tamper-resistant and decentralized, making it more secure against attacks. Blockchain also encrypts and anonymizes data, ensuring personal information remains private. Providing transparent consent mechanisms and rewarding participants with blockchain tokens can encourage more honest and diverse responses, ultimately improving the quality of market research insights. However, challenges such as the complexity of the technology, regulatory compliance, and integration with existing systems need to be addressed for widespread adoption.   Read more: Prognosis 2024: Unveiling Healthcare Trends and Strategies  So, what’s ahead?   As per ESOMAR, 35% of market research is still being conducted through traditional online surveys and discussions, while over 40% of researchers admit to using at least some part of AI in their research processes. While the fundamental framework of healthcare market research remains largely undiluted despite the technology onslaught, we would probably see a steady increase in the adoption of agile and more incisive methodologies. Also, while the patient side of research has the potential to move completely deep tech, the physician insights process will continue to remain traditional for at least a while.   The below report from GlobalData probably summarizes the sentiment clearly and the fact that researchers will utilize this opportunity to deploy more such methods in the near future. It is all about finding the sweet spot where traditional methods can be augmented with passive learning to generate next-level analysis.    It is, however, the scale of adoption that will determine the efficacy of such agile methods and will only be of avid consequence when researchers train and equip themselves adequately.  About SG Analytics     SG Analytics (SGA) is an industry-leading global data solutions firm providing data-centric research and contextual analytics services to its clients, including Fortune 500 companies, across BFSI, Technology, Media & Entertainment, and Healthcare sectors. Established in 2007, SG Analytics is a Great Place to Work® (GPTW) certified company with a team of over 1200 employees and a presence across the U.S.A., the UK, Switzerland, Poland, and India.            A leader in the healthcare research domain, SG Analytics assists healthcare companies in leveraging the power of information. Contact us today if you are in search of efficient Healthcare solutions to make sound business decisions.              Apart from being recognized by reputed firms such as Gartner, Everest Group, and ISG, SGA has been featured in the elite Deloitte Technology Fast 50 India 2023 and APAC 2024 High Growth Companies by the Financial Times & Statista. 

The Intersection of Digital Transformation and ESG for Sustainable Innovation

Digital transformation and technology supporting ESG are the need of the hour for healthy integration of ESG across organizational digital strategies. A well-thought-out ESG strategy helps influence long-term success as well as the reputation of the enterprise across all sectors, where investors and stakeholders are increasingly looking for lower risk and long-term business sustainability.   A critical catalyst in this endeavor is digital transformation, which acts as a powerful tool for recognizing ESG goals. Leveraging technology is key to driving sustainable business practices in a world where digital innovation and corporate responsibility are intertwined.   Digital transformation presents a multitude of opportunities to improve ESG initiatives. Today, digital transformation has evolved into a universal practice, enabling organizations to fundamentally alter different aspects of their operations. By integrating advanced technologies, organizations can gain critical insights into their environmental impact while also streamlining their social governance processes and maintaining higher standards of reporting. Concurrently, environmental, social, and governance (ESG) trends have garnered significant attention and are perceived as the key components of every business strategy.    As businesses navigate technological innovation and societal imperatives, the synergy between ESG and digital transformation can be seen as a promising partnership toward sustainability.  Read more: How is AI & Climate Tech Spearheading the Race to Net Zero?     Defining Digital Transformation   Digital transformation encircles the integration of digital technology across all business areas, fundamentally changing how it operates and delivers value to customers. It is a genuine rethinking of how an organization utilizes technology, people, and processes to change business performance fundamentally.  Benefits of Digital Transformation for Organizations  Some of the significant advantages of digital transformation include:   Increased Efficiency and Productivity: Automation of routine tasks can help in freeing up employees' time, enabling them to focus on more strategic activities.  Improved Data-Driven Decision Making: Access to real-time data and analytics can help in making informed decisions.  Enhanced Customer Experience: Personalised service offerings can lead to higher customer satisfaction.  Innovation and Competitive Advantage: Adopting the latest technologies can help foster innovation and position the organization as a leader in its field.  With these benefits, embracing digital transformation is a strategic imperative for businesses. By focusing on innovation and technology, they can cultivate an ideal environment to implement these digital solutions.  Key Technologies Driving Digital Transformation  Digital transformation guarantees that technology becomes the great equalizer in the workplace. It enables employees to access the same digital tools and platforms, thus fostering an inclusive environment where performance-based opportunities exist. Technology can further help bridge gaps and dismantle long-standing biases, nurturing an equitable workplace.   Integrating digital transformation and ESG initiatives is beneficial and increasingly imperative to thrive in today’s socially conscious market. This synergistic approach drives sustainability and cultivates resilience, innovation, and long-term value creation for all. Let's explore some of the examples of technologies that lie at the forefront of digital transformation.  Artificial Intelligence (AI) and Machine Learning (ML): AI and ML are equipping organizations to analyze vast data sets and generate insights for better decision-making.  Internet of Things (IoT): It enables organizations to connect with physical devices to the internet, offering them real-time data collection and analysis.  Fintech: With digital transformation, organizations can revolutionize their financial services with transparent, efficient, and inclusive offerings. This can further help align operations with ESG.  Cloud Computing: It offers flexible and scalable resources for data storage and computing power, enabling organizations to be more agile and responsive.  Digital transformation can enable flexible work arrangements and enhance healthcare and education through e-learning platforms. These beneficial effects of digital technologies are evident and highlight that technology can improve employee productivity by up to 25%.  Read more: Carbon Offsetting: Game-Changer Against Climate Change   Challenges in ESG Implementation  Integrating ESG principles into corporate strategy has become vital to demonstrate an organization's commitment to sustainability as well as ethical practices. However, despite the growing awareness, organizations face numerous challenges in integrating ESG into their core operations. These include:   Data Collection and Analysis: Accurately collecting, measuring, and reporting on ESG criteria is difficult and requires refined data collection and analysis capabilities.  Resource Allocation: Initiating ESG-focused projects requires significant investment and resource reallocation.  Stakeholder Engagement: Aligning the diverse interests of stakeholders with ESG goals can be a challenge.  In these challenges, digital transformation acts as a crucial enabler. By leveraging technology, organizations can overcome significant barriers to effective ESG implementation and pave the way for nurturing more sustainable and ethical business practices.    The Integration of ESG and Digital Transformation   Over the last few years, carbon emissions have increased significantly, and there is a pressing need for organizations to comply with complex ESG regulations and standards. The convergence of digital transformation and ESG is creating a new framework for corporate sustainability models. By integrating advanced technologies, organizations can gain access to insights into their environmental impact and implement sustainable initiatives along with higher standards of compliance and reporting.  Today, digital technologies are equipping them to enhance their business operations as well as play a crucial role in achieving ESG objectives:  Using AI and Data Analytics for Environmental Impact and Monitoring   Advanced analytics can enable organizations to measure and manage their environmental footprint. Sensors and analytics can help track air quality and emissions and optimize environmental metrics. AI tools can further help analyze energy consumption trends and patterns, thereby equipping firms to reduce their carbon emissions.  Read more: Building a Sustainable Tomorrow: Solutions to Climate Change   Using IoT for Social Governance  IoT can be used to monitor working conditions in real time while ensuring compliance with safety regulations. IoT can help track employee health metrics and promote workplace wellness programs.   Using Robotics for Carbon Reduction  Robotics can be integrated into environmental initiatives like reducing waste and carbon-intensive activities. These frameworks can help improve the efficiency of recycling processes as well as contribute to waste reduction.  Using Automation Tools  Integration of automation tools can help simplify ESG-related data collection and real-time reporting, making it more efficient and reliable while also reducing errors and costs. Digital platforms can help with better engagement with stakeholders, offering them up-to-date ESG performance data.  Predictive Analytics and Cybersecurity  Predictive analytics can assist in measuring risk and identifying potential vulnerabilities before they escalate. It can also help detect and respond to cybersecurity threats and safeguard organizational data.   Using Blockchain for Transparency   Blockchain can be integrated to ensure transparency and accountability in the supply chain and ensure immutable records of corporate transactions. Blockchain can be integrated to track the entire supply chain and ensure that raw materials are sourced sustainably by meeting ethical standards. This can further help in reducing their environmental impact. Accurate and timely reporting is crucial to ESG compliance, and this is where digital transformation and technology can aid.  By harnessing technology, organizations can meet their ESG goals and drive innovation, attract investment, and contribute to a more sustainable society. By leveraging advanced tech such as artificial intelligence, IoT, and big data analytics, they can further streamline their operations, improve energy efficiency, and minimize environmental impact.   Read more: The Power of ESG Reporting: How Businesses are Increasing Transparency and Managing Risks to Attract Investors  In Conclusion  The interplay between digital transformation, technology, and ESG initiatives is set to become more critical. Technological innovation and advancement and a mounting emphasis on sustainability are paving the way for a new era of innovation and corporate responsibility. By recognizing the potential of digital transformation and technology in supporting ESG, organizations can mutually reinforce and develop personalized ESG solutions for unique customer challenges.   As organizations navigate the complexities of integrating ESG with digital strategies, these advancements will help foster a culture of innovation, and new investments in cutting-edge technologies will further lead the way.  A leader in ESG Services, SG Analytics offers bespoke sustainability consulting services and research support for informed decision-making. Contact us today if you are searching for an efficient ESG (Environmental, Social, and Governance) integration and management solution provider to boost your sustainable performance.        About SG Analytics        SG Analytics (SGA) is an industry-leading global data solutions firm providing data-centric research and contextual analytics services to its clients, including Fortune 500 companies, across BFSI, Technology, Media & Entertainment, and Healthcare sectors. Established in 2007, SG Analytics is a Great Place to Work® (GPTW) certified company with a team of over 1200 employees and a presence across the U.S.A., the UK, Switzerland, Poland, and India.        Apart from being recognized by reputed firms such as Gartner, Everest Group, and ISG, SGA has been featured in the elite Deloitte Technology Fast 50 India 2023 and APAC 2024 High Growth Companies by the Financial Times & Statist. 

Digital Transformation in Pharmaceutical Industry

The global digital health industry is set to grow to $258.30 billion by 2029, driven by a CAGR of 8.49% starting in 2024. The revenue per user is estimated to be $85.56. Investments in data analytics by big pharma are also projected to grow to $1.2 billion by 2030, marking a 27% rise. Additionally, investments in cloud computing, a crucial technology for enhancing sales and operational efficiency in the industry, are forecast to reach $59.3 billion by 2030.   Digital Transformation in the Pharma Industry Despite these impressive forecasts, McKinsey’s research reveals that many pharmaceutical companies are not fully capitalizing on digital technologies. Companies that have successfully integrated digital solutions into their operations have achieved remarkable results, including a 20% improvement in product delivery speed, a 50% reduction in testing time, and a 50% boost in customer satisfaction.   However, many pharmaceutical firms face challenges in achieving similar outcomes, highlighting the complexities and barriers to effective digital transformation in the industry.   Common Challenges in the Pharma Industry   Rigid Operating Models   The pharmaceutical sector has been slow to adapt to change, hindered by stringent regulations and extensive bureaucracy that reinforce established processes. For a long time, digitalization was perceived as a costly and risky endeavor, leading to reluctance among leadership to fully embrace it.   However, this issue has largely been addressed since 2020, as many companies were compelled to adopt more digitized operating models due to social distancing requirements, and they began to see the benefits of these changes.   Lack of High-Quality Data Sources   The increasing interest in digital data and analytics within the pharmaceutical industry is often stymied by the lack of reliable data sources. Challenges include identifying existing data, ensuring timely access, continuous monitoring, and filtering out duplicates or siloed information.   As a result, many pharmaceutical companies still rely on manual data processes, which hampers fast decision-making and advanced analytics. The industry will need widespread adoption of standardized data algorithms to fully transition to digital methods.   Talent Shortages   Effective digital transformation requires the right talent. Despite offering compelling projects and competitive salaries, pharmaceutical companies struggle to attract and retain the necessary tech expertise.   To address this, companies must rethink their hiring and retention strategies. Approaches such as “win rooms” and strategic hiring through acquisitions can be particularly effective in closing skill gaps in data science, design, product management, AI expertise, and other critical areas.   Read more: Importance of Data Analytics in the Healthcare Industry Low Adoption Rates   Many areas of the pharmaceutical industry still lack widespread deployment of digital systems. Companies often focus on developing Proofs of Concept (PoCs) without a thorough analysis of business needs and resource requirements for scaling these solutions. Even with increased leadership support, many organizations struggle to foster the mindset needed for the broad adoption of new software.   Addressing these concerns requires a multifaceted approach, including significant changes in corporate processes, the development of more reliable data sources, and the recruitment of tech-savvy talent. These elements are crucial for making the adoption of new software systems more attractive across the industry.   Question: What have been and will remain the biggest hurdles to applying digital and analytics in your primary functional area?   The average score is calculated on a scale of 1-5, with higher scores indicating more significant challenges. In healthcare, the primary obstacles to utilizing digital and analytics have shifted from issues related to strategic alignment and executive leadership to challenges concerning data quality and talent acquisition.   Common Challenges for Scaling Digital and Analytics in Healthcare: 2020 vs. 2022 Comparison   Digital Companion Apps   Digital companions are increasingly valuable in supporting medications, therapies, and medical devices. Since 2020, both Software-as-a-Medical-Device (SaMD) solutions and standalone companion apps have seen steady growth in popularity. These tools enhance patient engagement and support, proving their effectiveness in complementing traditional treatment methods.   Software as a Medical Device (SaaMD)   SaaMD (Software-as-a-Medical-Device) is gaining prominence in the pharmaceutical sector, with several notable applications developed recently.   AstraZeneca has been at the forefront, creating several SaaMD applications, including the AI-assisted cancer patient support platform HAYA, developed with Neosperience Health, and the FINZI app, designed for patients with unresectable stage 3 lung cancer, developed with IPG Health Frankfurt. Both platforms gather patient information, facilitate communication with healthcare providers, and aim to alleviate concerns during therapy.   Sanofi is also advancing in this area, collaborating with BrightInsight to develop a digital companion mobile app that offers seamless access to therapy and chronic disease management services. This app integrates with third-party services to create a connected and accessible medical ecosystem for patients.   Earlier this year, Roche and Startup Creashere introduced their cobas® pulse platform. This platform integrates third-party digital health apps to support clinical decision-making, digital biomarker collection, and vital measurements.   Standalone Companion Apps   Standalone companion apps, including therapy support apps, quiz-based apps, and medication access apps, are gaining increasing popularity.   Pfizer’s IUdo app, launched last year in Qatar, exemplifies this trend. It streamlines medication access for patients and healthcare providers by facilitating program enrollment, qualifying for support treatment plans, and managing the patient’s medical journey through a mobile device.   Similarly, GSK has ventured into quiz apps with the release of Shine. This mobile app engages users with hormone-related questions, serving as both an educational tool and a survey platform. Shine helps medical professionals understand the concerns and needs of potential future clients while informing women about the effects of hormones on their bodies.   Read more: The Ethics of Healthcare Technology: Balancing Innovation and Patient Privacy Patient Online Course Platforms   Online platforms offering educational courses and resources for patients and caregivers are gaining significant traction and proving effective. A prime example is the 12-week Online Course for Chronic Pain, developed by Pfizer and Selfrapy. Patients who completed the course have reported noticeable reductions in pain intensity and improvements in their quality of life. These outcomes underscore the potential of digital education tools to enhance patient well-being and provide valuable support for those managing chronic conditions.   Diagnosis-Specific EHR Add-Ons   An increasing number of diagnosis-specific add-ons for Electronic Health Record (EHR) systems are becoming available. A notable recent example is Lupus Advisor, launched in 2022. This EHR add-on is designed to improve lupus care by aggregating patient data, providing evidence-based treatment guidelines, streamlining documentation, and enhancing care coordination for individuals with the condition.   AI-Based Predictive Modeling   The ongoing AI revolution has significantly accelerated the adoption of predictive modeling technology across various sectors. In the pharmaceutical industry, AI-based predictive modeling has been extensively utilized for years to analyze complex datasets and make informed predictions in drug discovery, development, and other processes.   Disease Prediction   AI-based medical analysis is making significant strides in disease prediction. In March, Bristol Myers Squibb and Viz.ai announced their collaboration on a predictive AI algorithm that uses neural networks to detect potential cases of hypertrophic cardiomyopathy by analyzing routine electrocardiograms.   Audio analysis is also emerging as a valuable tool in disease prediction. Merck & Co. has partnered with Hyfe, a leader in cough analysis technology, to develop an AI-powered smartphone app designed to assist the estimated 1.2 million U.S. adults with chronic cough by analyzing their coughing patterns.   Disease prediction apps. Cough Tracker by Merck & Co   Earlier this year, AstraZeneca and Bellvitge University Hospital launched a pilot program for Cordio Medical’s HearO app. This app monitors patients' speech patterns to identify potential congestive heart failure complications, achieving an 82% success rate in predicting heart decompensation events up to 22 days in advance.   Drug Discovery   Drug discovery and development are being significantly accelerated through the integration of advanced AI-driven systems by companies such as Merck & Co. and AstraZeneca. Merck & Co. is leveraging the cloud computing capabilities of Saama Technologies’ Life Science Analytics Cloud (LSAC) to consolidate data, thereby reducing cycle times and R&D costs.   AstraZeneca has partnered with Benchling, a biotechnology R&D platform, to develop FRAGLER, an open-source solution that automates DNA search, fragmentation, and assembly workflows. The use of FRAGLER has resulted in algorithmic sequence searches that have achieved cost savings of 50-90% for the company.   Trial/Research Management Systems   Pharmaceutical research, heavily reliant on data analysis, is increasingly integrating AI applications. As of June 2023, Sanofi's Plai app has provided a comprehensive overview of corporate activities, enhancing its research, clinical, and manufacturing operations through AI-powered analytics.   Bristol Myers Squibb is also making strides in advanced trial and research management systems. The company implemented Veeva Vault CTMS (Clinical Trial Management System) globally in under 20 months by 2022 and has partnered with Concert AI to develop a Digital Trial SaaS solution for oncology, urology, and hematology clinical trials. This collaboration aims to revolutionize trial design, recruitment criteria, and data collection standards to make the process faster, more cost-effective, and broader in scope.   AbbVie's Pega System Request Management Tool, a joint project with Pega and Accenture, is another noteworthy advancement. This platform has automated labor-intensive processes and daily routines through Robotic Process Automation (RPA) and case management functionality. AbbVie plans to further enhance the solution by adding preclinical process management features in the near future.   Other Solutions for Operational Workflow   Calibration Management Software   AstraZeneca is leading the way in streamlining equipment calibration with their investment of $600,000 in Beamex CMX software. This transition from paper-based and manual calibration processes to a digital, automated format represents a significant advancement in operational efficiency. Other major companies are expected to adopt similar systems to enhance their calibration management and improve documentation accuracy.   Category Management for Procurement   Pharmaceutical companies continuously strive to optimize their procurement processes through advanced technologies. A notable example of this is the collaboration between Novartis and Cirtuo. Cirtuo's Guided Strategy Creation™ platform will enhance Novartis's procurement operations by automating the collection, analysis, formatting, and organization of data. This will allow Novartis to concentrate on more value-centric tasks, streamlining procurement processes and improving overall efficiency.   Read more: Prognosis 2024: Unveiling Healthcare Trends and Strategies Partner Management Systems   Novartis has set a benchmark in the pharmaceutical industry with its electronic partnership management system. Collaborating with Astera's Centerprise Data Integrator, Novartis has successfully integrated forecast and sales data from various retail partners, including major chains like CVS, Target, and Walmart.   This innovative approach is likely to inspire other pharmaceutical companies to adopt similar systems to streamline communication and coordination with suppliers, contract manufacturers, contract research organizations (CROs), regulatory agencies, distributors, and other key partners.   AR Implementation   Augmented reality (AR) is increasingly being utilized across various areas, including R&D, manufacturing, sales, marketing, and patient engagement. For example, GSK’s Hemoglobe, showcased at ASN 2022, leveraged immersive technology by combining AR with 360-degree wraparound video to create a unique and dynamic experience.   On the mobile front, Novartis and Flipside Health developed the ViaOpta Simulator app, which uses AR to simulate visual impairments on a user’s smartphone screen. The app serves as a valuable tool for gaining insight into the challenges faced by visually impaired patients.   Conclusion - Digital Transformation in the Pharmaceutical Industry The pharmaceutical industry is undergoing a crucial digital transformation fueled by significant market growth and technological advancements. Innovations like digital companion apps, AI-driven predictive modeling, and advanced trial management systems are paving the way for improved patient care, streamlined operations, and accelerated drug discovery.   However, challenges such as limited data sources, talent shortages, and uneven adoption rates remain. To overcome these, the industry must prioritize data quality, foster innovation, and invest in talent and technology infrastructure.   The ongoing evolution of digital health technologies promises to revolutionize pharmaceutical operations and patient outcomes globally. Companies that proactively embrace these changes will lead the industry into a new era of efficiency, innovation, and healthcare excellence. As digital transformation continues, it is essential for industry leaders to stay agile and committed to maximizing these technologies' potential. About SG Analytics    SG Analytics (SGA) is an industry-leading global data solutions firm providing data-centric research and contextual analytics services to its clients, including Fortune 500 companies, across BFSI, Technology, Media & Entertainment, and Healthcare sectors. Established in 2007, SG Analytics is a Great Place to Work® (GPTW) certified company with a team of over 1200 employees and a presence across the U.S.A., the UK, Switzerland, Poland, and India.           A leader in the healthcare domain, SG Analytics assists healthcare companies in leveraging the power of information. Contact us today if you are in search of efficient Healthcare solutions to make sound business decisions.             Apart from being recognized by reputed firms such as Gartner, Everest Group, and ISG, SGA has been featured in the elite Deloitte Technology Fast 50 India 2023 and APAC 2024 High Growth Companies by the Financial Times & Statista. 

Generative AI is Increasing Employee Productivity and Expanding Capabilities. How?

Today, the integration of generative AI into the workplace has sparked intense debate. While it is being perceived as a game-changer for productivity and knowledge enhancement, it is also raising concerns over potential downsides such as burnout and information overload.   With these growing technological advances, a new type of knowledge worker is entering the talent pool. Augmented with generative AI, this employee can perform tasks faster, create personalized marketing content, and summarize hundreds of documents in seconds. While these are impressive productivity gains of generative AI, employees also need to expand their current capabilities to work in unison.  How Generative AI Can Equip Employees?  The power of generative AI is as strong as people's willingness to integrate it into their operations. Oftentimes, organizations are focused on how generative AI can help simplify and automate everyday processes. While such measures can increase productivity, executives are overlooking the rare opportunities to revolutionize the potential of their employees and pioneer a new future for their organization.  Read more: The Future of AI Content Generation: Recognizing the Full Potential of GPT-4   Generative AI is the new future that will encircle the evolving landscape of work, shape the changing workforce, and boost advancements in technology. Its ability to rapidly take on new types of work with generative AI will be a game-changer for employees and companies alike. Due to this reason, organizations need to envision the technology not as a standalone but in the context of their workforce. It can help organizations reimagine different job roles and drive improved outcomes along with enhanced business value. However, leaders need to effectively manage the risks to reap significant rewards.  Generative AI can be used to help reveal untapped employee potential and reinvent workflows. When leveraged strategically by integrating the systems, data, and people with tech, generative AI can help bolster a future of work strategy and even forecast employee potential.  Managing the Transition  Generative AI tools enable employees to streamline their operational workflows, automate repetitive tasks, and offer quick access to insights that might otherwise take hours to discover. A digital co-pilot, AI offers guidance, automating everyday tasks and enabling employees to focus on higher-order thinking. It has become an essential productivity booster, offering employees a competitive edge in knowledge and work. This ability to augment human intellect backed with AI-driven knowledge indicates that today's employees are empowered to make smarter decisions faster.  The new augmented workforce will be able to perform tasks and utilize the tool's capabilities skillfully. Let's understand the core implications for company leaders.   Read more: Data Dominion: Taking Charge of Your Business Intelligence     Learning and Development  Employees seeking paths to senior roles or leadership can successfully advance their careers in a GenAI-augmented workforce. It can offer them the flexibility to take on different roles and cultivate new skills for long-term retention.  While GenAI has an immediate aptitude-expansion effect, learning and development are still most significant for cultivating advanced skills as well as supporting every employee’s professional trajectory. When specifically used for learning, Gen AI is an effective personalized training tool. Therefore, leaders must ensure that their employees have time to learn. Organizations can gain a competitive advantage from developing tools and processes that help in precisely assessing the capabilities of GenAI models for their use cases.   Talent Acquisition   When augmented by GenAI, employees can perform their operations with ease. This also suggests that the talent pool for a skilled workforce is expanding. Recruiters can incorporate GenAI into the interview process to get a complete picture of what a prospective employee should be capable of when augmented by the technology.   Teaming and Performance Management  With the integration of Generative AI, it will be crucial for teams to manage their performance as well as ensure the quality of their output. This implies designing cross-functional teams to provide generalists with easy access to an expert when they need help and establish regular output review checkpoints. Leaders must run pilots to ensure their team configurations help in generating the best outcomes. This will further offer opportunities to break silos and integrate teams with experts from different centers of excellence.  Read more: Bias Problem in ChatGPT: Challenges and Limitations  Professional Identity  The impact of GenAI on professional identity is a critical aspect to consider. Organizations need to mitigate the negative impacts, ensuring that employees feel supported by their employers. While generative AI can help enhance an employee's problem-solving skills, equipping them to achieve faster outputs, it can sometimes pose as an identity theft for them. This suggests that highly skilled employees can enjoy using the tool, allowing them to feel more confident in their roles. Mandating the use of AI can improve employee perception of AI. However, this is only true if employees believe AI is being deployed for their benefit.    Strategic Workforce Planning  With generative AI, organizations reinvent their specialized expert tracks as well as the structure of their workforce. Considering the ongoing job transformation and technological advancements, they can incorporate strategic workforce planning for knowledge work. Due to the emergence of generative AI, workforce planning will no longer focus on finding a certain number of people with specific knowledge and skills. Instead, organizations can focus on behavioral skills and enablers to support a more flexible workforce.   Key Takeaways   GenAI is not just a tool for increasing productivity and broadening the range of tasks employees can perform.  For industry leaders, transitioning to a GenAI-augmented future is likely to have profound implications for talent acquisition and internal mobility, along with employee learning and development.  Generative AI can be beneficial for teaming and performance management, as well as strategic workforce planning.  Read more: A Friend or a Foe: Advanced Conversational AI & its Implications  Final Thoughts  Beyond just augmenting employees with AI, it is important to take into consideration how AI tools are enhanced by the human element. Generative AI is still in its learning phase, and while it can process vast data sets, it relies on human interaction to refine the outputs. Employees offer valuable feedback loops, enabling AI frameworks to evolve and improve. In this sense, it is not just how employees use AI but how employees assist in shaping and augmenting AI tools.   While the benefits of AI augmentation are undeniable, organizations need to take the information overload side of generative AI into consideration. GenAI tools are capable of boosting knowledge and productivity, but there is a tipping point with AI. While these AI tools enable employees to stay informed and efficient, overreliance can lead to cognitive burnout, making it necessary for organizations to implement strategies that help in balancing AI-driven inputs. Frequent use of AI tools can overwhelm employees with data. This phenomenon can lead to mental fatigue, making it challenging for employees to process and recover. The sheer volume of data AI provides can also blur focus, leaving employees feeling more drained than productive.  A leading enterprise in Generative AI solutions, SG Analytics focuses on unlocking unparalleled efficiency, customer satisfaction, and innovation for the client with end-to-end AI solutions. Contact us today to harness the immense power of artificial intelligence and set new benchmarks in operational efficiency, customer satisfaction, and revenue generation.        About SG Analytics           SG Analytics (SGA) is an industry-leading global data solutions firm providing data-centric research and contextual analytics services to its clients, including Fortune 500 companies, across BFSI, Technology, Media & Entertainment, and Healthcare sectors. Established in 2007, SG Analytics is a Great Place to Work® (GPTW) certified company with a team of over 1200 employees and a presence across the U.S.A., the UK, Switzerland, Poland, and India.       Apart from being recognized by reputed firms such as Gartner, Everest Group, and ISG, SGA has been featured in the elite Deloitte Technology Fast 50 India 2023 and APAC 2024 High Growth Companies by the Financial Times & Statista. 

Why Are Big Corporations Abandoning their Climate Commitments Quietly?

Once positioned as leaders in sustainability and setting ambitious net-zero goals that align with global environmental efforts, organizations are taking a turn on their climate and carbon reduction commitments. However, this rapid rise of energy-hungry artificial intelligence is forcing companies to reconsider or abandon their commitments as they struggle to balance environmental responsibility.    Energy consumption: AI vs. Blockchain  The debate over energy consumption across sectors is not new. Blockchain technology, especially cryptocurrencies like Bitcoin, has faced heavy criticism for its excessive energy use, leading to calls for regulation. In contrast, AI—despite having greater energy demands—has escaped such scrutiny. Data centers account for almost 1-1.3% of global electricity consumption.  This disparity in sentiment between the energy consumption of blockchain and AI is perplexing. However, when considered, it makes sense that AI is a helpful tool for incumbent industry players.  Unlike blockchain, which is a disruptive force challenging the status quo, AI’s ability to drive economic growth and reinforce existing power structures makes it a preferred technology among tech giants. Given that investments in AI are expected to reach $200 billion by 2025, the impact of AI on the environment is often downplayed, fitting the commercial and broader agendas of large corporations.  Read more: The Power of ESG Reporting: How Businesses are Increasing Transparency and Managing Risks to Attract Investors   In contrast, blockchain’s decentralized nature threatens traditional centralized systems, leading to more scrutiny, criticism, and calls for regulation of energy use. The silence around AI’s environmental impact could, therefore, be more about the selective emphasis on technologies that support or challenge its existing power dynamics.  Yet, major tech companies are shifting away from purchasing carbon offsets due to the burgeoning energy consumption of their AI operations. There is also a growing emphasis on the need for genuine emissions reductions and greater transparency in energy use.  The Role of Blockchain in the AI Revolution  With AI expansion costs soaring, new doors for blockchain-based marketplaces are also opening. These platforms present a decentralized privacy-focused solution where user data is not stored or used to train the AI models. However, to truly compete with centralized systems, these decentralized models need to prove that they can deliver high-quality outputs, not just privacy.  While centralized AI is better quality as user data is used to train the models faster, decentralized AI is exploring new ways to enhance performance by training AI models using data that cannot be seen in raw form   AI models are trained across multiple devices, leveraging local computational resources without compromising on accuracy and data privacy. Distributed training approaches integrate diverse data sources to build more robust and scalable AI systems.  In harnessing this vast, distributed network of mobile devices, the solution taps into a more energy-efficient computing resource that doesn't require the cooling systems needed in data centers but also democratizes access to computational power.  Read more: How is AI & Climate Tech Spearheading the Race to Net Zero?   How Is AI Affecting Companies’ Goals?  Training and running large AI models requires substantial computational power typically sourced from data centers in regions with lower energy costs. This is because renewable energy sources are not always available around the clock, making them less reliable for constant computing needs. Due to the rising demands of AI and manufacturing sectors, enterprises are rushing to expand their generating capacity to meet their energy transition goals.  Further research indicates that the electricity consumption of AI and global data centers is set to double by 2026. Regional transmission capacity needs to double by 2035 to accommodate this growing demand, and interregional capacity will need to increase over five-fold. This expanded capacity is critical to link wind and solar energy. The rapid growth in energy demand is pushing tech companies to choose between advancing AI technology as well as adhering to their net-zero climate commitments.   Reasons Excluding AI for the Rollback  Although many corporations have made ambitious pledges initially to adopt sustainable practices, these promises frequently to fall short. A major obstacle is the lack of cohesion across leadership teams. A recent research highlighted that 58% of executives face significant disagreements in balancing immediate business demands with environmental, social, and governance (ESG) goals. This issue is further aggravated by the absence of effective measurement tools. Without robust metrics, tracking progress or linking executive bonuses to ESG performance often becomes challenging, making it difficult to stay committed to sustainability objectives.  Read more: Disclosure v. Diversity Washing: Reflecting on the Critical Components in Reporting   Another challenge is the external alignment of set goals with stakeholders. Many organizations often face internal cultural issues as a barrier to achieving their ESG goals. Decision-making and accountability are further complicated due to the shortage of essential skills and mindset within the organization. Additional challenges include insufficient progress in understanding climate-related financial risks and difficulties in integrating ESG factors into capital allocation. Despite significant investments in sustainable solutions, many companies are struggling to fulfill their sustainability promises.   Major corporations are reevaluating their sustainability strategies, especially around the use of carbon offsets. The growing concerns around the effectiveness of carbon offsets and the risk of reputational damage if commitments are not met have prompted several companies to shift focus. Many are moving away from reliance on carbon offsets and prioritizing the reduction of actual emissions within their operations. This exemplifies how innovative solutions, combined with the privacy and cost efficiency offered by decentralized infrastructure, can help address both the growing demand for computational power and more equitable access to technology at a reasonable price. Having more local compute-for-AI offerings will further help in providing a more enduring pathway for AI to expand without the flip-flopping seen by the tech giants.     Key Takeaways  Companies concerned about the sustainability of their businesses, customer base, and employees are quietly backing out of their ESG commitments.  They have discovered that meeting net-zero goals is both difficult and expensive as well as threatens their sustainability.  Due to these concerns, they are moving away from previously declared goals.  Today, only 4 percent of companies are on track.  Read more: Building a Sustainable Tomorrow: Solutions to Climate Change   Final Thoughts  Today, big corporations are pulling back on their climate commitments.   Even before the rise of the generative AI wave, software consumed a considerable amount of energy. But now, it is booming. A survey published by the International Energy Agency evaluated that a Google search query requires 0.3 watt-hours of electricity on average, while a ChatGPT request consumes 2.9 watt-hours.    Behind chatbots and AI-infused software lies a vast network of power-hungry data centers. This growing energy needs are only set to go up as competitive pressure drives companies worldwide to invest in building larger AI models and data centers. Enterprises are working on technological methods to use less power or balance their demand on the grid more efficiently. This includes squeezing more efficiency from chips and servers, laying out equipment that requires less cooling, and shifting loads across different areas based on where green energy is available. This requires greater transparency from enterprises to establish how much energy AI products consume.  A leader in ESG Services, SG Analytics offers bespoke sustainability consulting services and research support for informed decision-making. Contact us today if you are searching for an efficient ESG (Environmental, Social, and Governance) integration and management solution provider to boost your sustainable performance.        About SG Analytics        SG Analytics (SGA) is an industry-leading global data solutions firm providing data-centric research and contextual analytics services to its clients, including Fortune 500 companies, across BFSI, Technology, Media & Entertainment, and Healthcare sectors. Established in 2007, SG Analytics is a Great Place to Work® (GPTW) certified company with a team of over 1200 employees and a presence across the U.S.A., the UK, Switzerland, Poland, and India.        Apart from being recognized by reputed firms such as Gartner, Everest Group, and ISG, SGA has been featured in the elite Deloitte Technology Fast 50 India 2023 and APAC 2024 High Growth Companies by the Financial Times & Statista. 

Beyond ChatGPT: How Generative AI is Shaping the Future of Enterprises

Artificial intelligence has become a necessity for businesses to remain competitive. And with this potential continuing to unfold, it has become more apparent how this technology is set to further reshape the business landscape.   Leveraging technologies like AI equips businesses with swift decision-making, enhances customer service offerings, transforms business operations, and offers a competitive edge across industries. And the world has started to witness what the future of AI and its impact on businesses will look like.  Today, organizations are exploring new AI applications to be a part of the Gen AI transformation. It stimulates business automation, drives efficiency, and fosters AI-driven innovation. Generative AI’s ability to build synthetic data for secure and effective model training expands its utility across industries requiring high privacy levels. The impact of generative AI is significant.   Generative AI spans well beyond ChatGPT and its original applications. It has evolved into a powerful tool that is reshaping industries. From its initial role in enhancing conversational interfaces, AI has become a cornerstone of innovation. This evolution is further paving the way for the future of generative AI for enterprises, where the potential of AI stretches far beyond text generation.   Read more: Solving the Ethical Dilemma: Cybersecurity and The Age of Generative AI    Businesses are Benefiting from Generative AI. How?  Breakthroughs in generative artificial intelligence and the introduction of newer generative AI tools have established AI as a critical component of organizational decision-making, growth, and operational strategies. Consequently, a concerted effort is to propel business operations forward by harnessing the power of AI to foster innovation and drive transformation.  A Gartner report presents that by the year 2026, almost 80% of businesses will have utilized generative AI application programming interfaces (API) or will have implemented generative AI-powered applications. Generative AI APIs equip developers to access AI models easily, which can be integrated into a wide range of applications. This further helps streamline access to AI capabilities and significantly enhances project outcomes.  While initial applications were focused on text generation and language capabilities, organizations are now exploring different use cases for product development, hyper-personalization, and automation. A McKinsey research study revealed that generative AI can automate roughly half of current business activities a decade sooner than anticipated. Some of the specific capabilities of generative AI are as follows:  Domain-Specific Content Generation  Generative AI is a proficient content creation tool that is capable of generating a wide range of content, such as text, images, code, and multimedia. Gartner predicted that by 2023, almost 80% of organizations will utilize computer vision technology to analyze images and videos.  Read more: Bias Problem in ChatGPT: Challenges and Limitations   In the healthcare industry, generative AI is proving to be valuable in analyzing medical images and accurately predicting the future development of diseases. This is equipping doctors to better detect abnormalities in CT scans, leading to enhanced patient outcomes.  Product Design and Development   Generative AI models enable rapid prototyping, iteration, and customization. This is leading to a growth in the development of market-responsive products. Generative AI is capable of simplifying and streamlining coding, thereby enhancing efficiency and aiding teams in producing high-quality software.  Synthetic Data  Enterprises can benefit from the use of generative AI, which enables the generation of synthetic data to enhance existing datasets while upholding the privacy of sensitive information. Synthetic data today is becoming increasingly popular across various sectors, as it effectively supports model training data and algorithm development.  Risks Associated with Generative AI   The integration of generative AI beyond ChatGPT is reshaping the enterprise landscape, presenting vast potential for innovation and efficiency. With more and more businesses exploring the future of generative AI, they are discovering its potential to revolutionize processes, from AI-driven innovation to personalized customer interactions. However, this transformation is not without challenges.    While generative AI presents immense potential, it has its risks.   One of the major concerns of generative AI is data privacy, as AI models require vast amounts of data, including sensitive information, to operate effectively.   The risk of biased or inaccurate outputs also arises if the AI framework is trained on flawed data.   The widespread use of generative AI is raising ethical concerns, including the creation of misleading content such as deepfakes.   Businesses must implement robust governance frameworks to manage these generative AI risks. These frameworks must monitor and validate AI outputs to prevent misuse and maintain trust.  Read more: How AI is Changing the Future of Music Composition and Production     Pioneering A New Era of Business Innovation   Generative AI and other advanced AI frameworks are continually evolving through sustained research and development efforts. Researchers worldwide are exploring cutting-edge approaches to enhance model capabilities. These techniques further help improve prediction accuracy, enable efficient use of data, facilitate faster model training, and enhance adaptability to new tasks.   Today, more and more businesses are realizing enhanced operational efficiency and automation by implementing generative AI applications. Industries are optimizing their operational processes to improve product quality and increase production capacity. However, this process is not without its challenges. There are growing privacy concerns along with the existing generative AI risks associated with ensuring that the fed data accurately represents the underlying real-world scenarios. Businesses must carefully balance the benefits of AI-driven innovation while also maintaining data integrity and security. Major technology companies are engaging in collaborations with industry players across healthcare, finance, automotive, and retail sectors to co-create AI solutions and cater to specific industry requirements.  Generative AI represents a disruptive technology that can revolutionize multiple business domains. This paradigm shift in AI capabilities promotes the augmentation of human intelligence, automation of tasks, and generation of critical insights that enable data-driven decisions. As the capabilities of generative AI continue to advance, organizations need to periodically assess emerging opportunities that align with their potential business insights. Business agility will be crucial in driving adoption and shaping the future of innovation.  Read more: A Friend or a Foe: Advanced Conversational AI & its Implications   Final Thoughts  Today, AI transformation is underway, and enterprises that manage it effectively will be in charge of the new era of AI.  Enterprises exploring the implementation of generative AI should begin by identifying its specific use cases that perfectly align with their business goals. This involves automating content creation, enhancing customer support with AI-driven chatbots, and optimizing product design. However, it is important to ensure that the existing technology infrastructure is able to support the demands of AI models in terms of data processing and storage. Assembling a cross-functional team with AI expertise and the relevant industry is equally essential. This will help guide the integration of generative AI into enterprise frameworks while addressing any underlying potential risks. Continuous monitoring and regular updates will help companies stay ahead in today's AI transformation.  However, implementing AI requires careful consideration of generative AI risks. The road ahead involves balancing the new possibilities of AI applications with the growing need for robust data management strategies. By staying updated and incorporating a strategic approach, enterprises can effectively harness the power of generative AI and drive significant business growth in this increasingly AI-driven world.  A leading enterprise in Generative AI solutions, SG Analytics focuses on unlocking unparalleled efficiency, customer satisfaction, and innovation for the client with end-to-end AI solutions. Contact us today to harness the immense power of artificial intelligence and set new benchmarks in operational efficiency, customer satisfaction, and revenue generation.         About SG Analytics            SG Analytics (SGA) is an industry-leading global data solutions firm providing data-centric research and contextual analytics services to its clients, including Fortune 500 companies, across BFSI, Technology, Media & Entertainment, and Healthcare sectors. Established in 2007, SG Analytics is a Great Place to Work® (GPTW) certified company with a team of over 1200 employees and a presence across the U.S.A., the UK, Switzerland, Poland, and India.        Apart from being recognized by reputed firms such as Gartner, Everest Group, and ISG, SGA has been featured in the elite Deloitte Technology Fast 50 India 2023 and APAC 2024 High Growth Companies by the Financial Times & Statista. 

The New Data Economy: Navigating the Future of Data-Driven Value Creation

Businesses are increasingly becoming conscious of the need for good data literacy and good data practices. However, the landscape of data is undergoing a significant change.  The global rise in data volume is raising significant concerns about effectively managing its use and storage. One critical concern is dark data, which needs to be tapped and used for decision-making and insights generation. This phenomenon is a reality in the data economy that demands a solution through productization.  Data economy signifies the expanding economic activity centered around the collection, market analysis, and sale of data. With digitization and the exponential growth of the internet and social media, organizations are continuously generating and collecting vast amounts of data. This data can be valuable for companies to gain critical insights into consumer behavior, industry trends, and the performance of their business operations.  Read more: Navigating the Impact of AI in Social Media Management  Understanding the Data Economy   The data economy is emerging from past decades of data analytics and data science. A data economy is a global digital ecosystem where data is gathered, organized, and exchanged across a network of participants to derive value from the collected information.   Data inputs are collected and exchanged by different stakeholders, including search engines, social media websites, online vendors, payment gateways, financial service providers, and software-as-a-service (SaaS) companies. However, it is important to note that data by itself does not create any value. The innovative use of data supporting a business framework leads to enhanced business performance.  Recording users' actions can help in precisely understanding customer journeys.  Operationalizing data can help improve business performance and can become a defining competitive advantage.   New data infrastructures and sharing ecosystems powered by next-generation data tools can further help bring information to organizations on the front line, thereby helping with quick decisions and actions.   This data economy transcends borders and industries, enclosing everything from technology giants and startups to healthcare and government agencies. The key players in this space are large data sets, as well as organizations that leverage this data effectively. As of 2024, the data economy is marked by different key trends:  Read more: Transforming Enterprise Security with Modern Data Architecture   Data as a Strategic Asset  Organizations across sectors recognize data as a critical asset for decision-making, innovation, and customer insights. Tech giants like Google, Amazon, and Facebook are building empires by effectively harnessing the power of data.  Significance of Artificial Intelligence (AI) and Machine Learning (ML) Technology  Today, AI and ML are at the forefront of the data economy. These technologies help analyze patterns and insights from big data to further drive business efficiency, personalize experiences, and predict future trends.  Privacy and Data Regulation  With the growing importance of data comes greater scrutiny. Data regulations such as GDPR in Europe and CCPA in California are aimed at protecting personal data as well as reshaping the way organizations collect and use information.  The Growth of Data Marketplaces  Gowing online marketplaces promote the buying and selling of data, thereby enabling organizations to access a wider range of insights than they could collect on their own.  Core Challenges and Considerations of the Data Economy   Today, the payments industry is changing rapidly due to diversification, accelerated digitalization, and a rapidly evolving regulatory landscape, and it is growing beyond traditional players. Major advances in regulation and technology have been made to address issues that can arise from this situation. Working in tandem, these advances further help yield the benefits of data leveraged without causing adverse effects within the industry.  Read more: Meta’s AI Studio: How to Create Your Own Custom AI Chatbot Tool?     Regulatory Frameworks   The emergence of a data economy is having a significant influence on personal, social, and industrial lives. Regulatory frameworks are further influencing payment experiences and technology going forward. Organizations must recognize the potential of the data economy and integrate legislative measures to stimulate and protect business outcomes.  Next-generation Technologies  With the volume of data increasing and technology advancing, the data economy is being built in many ways. New technologies are being deployed to shape the new data economy and its services.  Sustainable Design  The environmental crisis is a pressing concern, and data insights are one of the greatest tools available. With the sustainable design, organizations can gain insights into their carbon emissions and further analyze their transactions.   Growing Data Awareness  Data is a valuable asset for businesses. There is a growing need to integrate a little transparency in how it is used. This will help protect businesses and affect how the data economy is shaped in the years to come. Beyond this, having data transparency in and between companies will also affect the pace and stability of the growth of the data economy.  While the data economy offers immense opportunities, it presents significant challenges:  Data Security: With more data becoming a greater risk of breaches, ensuring data security is becoming paramount.  Data Quality: Data is as valuable as its quality. However, misinformation and poor data quality can lead to inaccurate decisions.  Inequality: Due to a growing risk, the data economy landscape is set to widen the gap between those with access to data and technology.  Read more: Beyond Numbers: Unveiling the Power of Data with Visualization  Final Thoughts   Today, the data economy is considered more than just a buzzword. It is a transformative force that is reshaping the world. Data is instrumental in enabling businesses to become more efficient, make enhanced business insights, strengthen relationships, and understand more about their position in the world.  With organizations continuing to generate and leverage data, the potential for innovation, efficiency, and value creation is also on the rise. However, navigating this data landscape responsibly and ethically is crucial for sustainable growth and societal benefit. The future of the data economy is not only about technology and profits but about shaping a world where data benefits all.  A leading enterprise in Data Analytics, SG Analytics focuses on leveraging data management solutions, analytics, and data science to help businesses across industries discover new insights and craft tailored growth strategies. Contact us today to make critical data-driven decisions, prompting accelerated business expansion and breakthrough performance.          About SG Analytics   SG Analytics (SGA) is an industry-leading global data solutions firm providing data-centric research and contextual analytics services to its clients, including Fortune 500 companies, across BFSI, Technology, Media & Entertainment, and Healthcare sectors. Established in 2007, SG Analytics is a Great Place to Work® (GPTW) certified company with a team of over 1200 employees and a presence across the U.S.A., the UK, Switzerland, Poland, and India.          Apart from being recognized by reputed firms such as Gartner, Everest Group, and ISG, SGA has been featured in the elite Deloitte Technology Fast 50 India 2023 and APAC 2024 High Growth Companies by the Financial Times & Statista. 

The Impact of Digital Health Technologies on Neurological Care

The field of digital health in neurological disorders is rapidly evolving, encompassing numerous technologies and solutions aimed at enhancing neurological care. This domain encompasses a wide array of digital tools and platforms, including telemedicine, mobile health applications, wearable devices, and AI-driven diagnostics and treatment systems tailored specifically for neurological conditions. The integration of digital health technologies is profoundly impacting the diagnosis, management, and treatment of neurological disorders, ushering in improvements in accuracy, accessibility, and patient outcomes across a spectrum of conditions such as epilepsy, Parkinson’s disease, multiple sclerosis, and Alzheimer’s disease.   Below are a few factors that drive the digital health sector, particularly in neurology  Technological Advancements  Rising Prevalence of Neurological Disorders  Aging Population  Remote Patient Monitoring  Telemedicine and Virtual Consultations   Read more: The New Frontier – Intersection of Healthcare & Gen AI  Overview: How Digital Health is Transforming the Landscape for Neurological Disorders?  Wearable Devices and Remote Monitoring   Wearable devices equipped with sensors are revolutionizing the way neurological disorders are monitored. These devices provide continuous, real-time data, empowering healthcare providers and patients with informed decision-making about treatment and management strategies. Epilepsy  Seizure Detection  Devices such as the Empatica Embrace2 and the SmartWatch Inspyre by SmartMonitor monitor physiological signals, such as heart rate and electrodermal activity, to detect and alert patients and caregivers about seizures in real time. Newer wearables such as Empatica Embrace 3 and Epoc by Emotiv go beyond seizure detection to monitor sleep patterns and emotional states in epilepsy patients.  EEG Monitoring  Portable and wearable EEG devices, such as the Epilog and Zeto, provide continuous monitoring and can be used in home settings, improving the detection and diagnosis of epilepsy. In June 2024, the FDA cleared Zeto’s ONE headset, a new generation device with 21 soft-tip electrodes for convenience and user-friendly EEG monitoring.  Parkinson’s Disease   Symptom Tracking  Devices such as the Kinesia system by Great Lakes NeuroTechnologies and the Personal KinetiGraph (PKG) by Global Kinetics track motor symptoms such as tremors, bradykinesia, and dyskinesia, provide detailed reports to healthcare providers for better management.  Smartwatches  Apps integrated with smartwatches, such as the Apple Watch, use sensors to monitor movement patterns and provide data on tremor intensity and frequency. Smartwatches with fall detection algorithms are being explored to prevent head injuries in patients with Parkinson’s disease.    Artificial Intelligence and Machine Learning   Artificial intelligence today is reshaping the landscape of neurological diagnostics by analyzing complex data patterns and identifying subtle abnormalities that may indicate the presence of a neurological disorder. Machine Learning (ML) algorithms are being trained on vast datasets of neuroimaging studies, genetic profiles, and clinical data to develop more accurate diagnostic tools, leading to earlier detection and intervention.   Diagnosis and Prognosis   MRI and CT Scan Analysis  AI algorithms developed by companies including Aidoc and Qure.ai enhance the interpretation of brain scans, helping to identify anomalies associated with neurological conditions such as tumors, stroke, and traumatic brain injury.  Early Detection of Alzheimer’s  AI tools from companies such as Cognoa and Neurotrack analyze cognitive tests and behavioral data to detect early signs of Alzheimer’s and other dementias.  Predictive Analytics  Disease Progression  AI platforms use patient data to predict disease progression in conditions such as multiple sclerosis and Parkinson’s disease, enabling personalized treatment plans.  Telemedicine and Remote Consultations  Telemedicine today is a vital tool for patients with neurological disorders, particularly those living in remote areas or facing mobility challenges. Through video consultations and remote monitoring platforms, neurologists can conduct assessments, adjust medications, and provide ongoing support to patients, enhancing access to care and improving outcomes.  Read more: Data and Analytics in Healthcare: Benefits and its Importance  Access to Neurologists  Virtual Consultations  Platforms such as Teladoc and Amwell facilitate remote consultations with neurologists, providing patients in remote or underserved areas with access to specialized care.  TeleNeurology  Dedicated telemedicine services for neurology, such as TeleSpecialists, provide telemedicine solutions for quality-focused hospitals and healthcare systems.   Digital Biomarkers and Health Analytics  Digital biomarkers are quantifiable, objective, and physiological data collected and measured by digital devices. In neurological disorders, these biomarkers offer a novel approach to monitor disease progression, predict outcomes, and tailor treatments. Health analytics, which involves the use of computational techniques to analyze health data, plays a crucial role in extracting meaningful insights from digital biomarkers.  Cognitive Assessments  Tools such as Cambridge Cognition’s CANTAB and the Lumosity app provide digital cognitive assessments that help in diagnosing and monitoring conditions such as mild cognitive impairment (MCI) and Alzheimer’s disease.  Mobile Health Apps Symptom Tracking and Management  Apps such as Parkinson’s Mind Movement use gamified elements and voice coaching to help patients with Parkinson’s practice exercises and improve motor skills.  SeizAlarm integrates with Apple Watch to detect potential seizure activity through heart rate and movement data, alerting caregivers and providing emergency assistance.  Virtual Reality and Augmented Reality  Virtual reality and augmented reality (AR) are being explored for their potential applications in the diagnosis, treatment, and rehabilitation of neurological disorders.  Rehabilitation and Therapy  VR for Stroke Rehabilitation  VR platforms, including Parkinson’s VR by PhysioVR, use immersive environments that challenge balance and gait. Patients navigate virtual landscapes with obstacles and uneven terrain, practicing walking exercises in a safe and engaging way.  AR for Cognitive Training  AR tools provide interactive cognitive training exercises for patients with neurodegenerative disorders, enhancing engagement and effectiveness.  Pain Management  VR for Chronic Pain  VR applications such as those developed by AppliedVR provide immersive environments that help manage chronic pain associated with neurological disorders through distraction and relaxation techniques.  Genomic and Personalized Medicine   Genetic Testing  Companies such as Invitae and 23andMe offer genetic testing that can identify mutations associated with neurological disorders, aiding in early diagnosis and personalized treatment planning.`  CRISPR and Gene Therapy  Research and Treatment  Advances in CRISPR technology are being explored for the treatment of genetic neurological disorders such as Huntington’s disease and spinal muscular atrophy (SMA).  Electronic Health Records and Data Integration  Electronic Health Records (EHRs) and data integration are crucial for neurological disorders. They present numerous benefits, such as improved patient care, streamlined workflows, and enhanced research opportunities.  Read more: Sustainability Efforts by the Pharmaceutical Industry  Interoperability - Integrated Care  Enhanced EHR systems ensure that neurological data, including imaging, genetic, and digital health data, are integrated and accessible to healthcare providers, improving coordination and continuity of care.  WRS Health (Neurology Cloud), a cloud-based EHR platform, offers neurology-specific templates, real-time medication management, and secure messaging functionalities.  Clinical Decision Support - AI-driven Insights  AI-integrated EHR systems provide clinical decision support by analyzing patient data and offering insights into potential diagnoses and treatment options.  These advancements in digital health are significantly enhancing the management and treatment of neurological disorders. This leads to enhanced patient outcomes and improved quality of life. Today, with technology continuing to evolve, the integration of AI, wearables, telemedicine, and other digital tools will further revolutionize neurological care.  Challenges and Barriers in Digital Health for Neurological Disorders  Even though digital health offers a wide range of benefits for neurological care, there are still challenges and barriers that need to be addressed:  Data Privacy and Security: Concerns exist around protecting sensitive patient data collected by wearables, mobile apps, and other digital tools. Breaches and misuse of this data can have serious consequences.  Accessibility and Equity: Not everyone today has access to smartphones, tablets, and internet connectivity required for many digital health solutions. This can exacerbate existing disparities in healthcare access.  Cost and Reimbursement: The development, implementation, and maintenance of digital health technologies can be expensive. Reimbursement policies may not adequately cover these costs, limiting their widespread adoption.  Integration with Traditional Healthcare: Seamless integration of digital health tools with EHRs and existing healthcare workflows is crucial for optimal patient care. This can be challenging due to technical and logistical hurdles.  Validation and Standardization: The effectiveness and reliability of digital health interventions need to be rigorously evaluated and standardized to ensure quality and patient safety.  Limited Digital Literacy: A few patients, particularly older adults, may lack the digital literacy skills required to use certain digital health tools effectively.  Algorithmic Bias: AI algorithms used for diagnostics and treatment decisions can perpetuate biases present in the data they are trained on. This can lead to inaccurate diagnoses and unfair treatment recommendations.  These challenges need to be addressed to ensure that digital health technologies reach their full potential in transforming neurological care.  Read more: Winning in Implantable Medical Devices Market: Pharma’s Next Frontier  Future Outlook  The future of digital health in neurology holds tremendous promise, with ongoing advancements expected to further transform the landscape. Emerging technologies such as advanced AI algorithms, more sophisticated wearable devices, and innovative applications of VR and AR in therapy and rehabilitation will continue to enhance patient outcomes. The expansion of genomic and personalized medicine, including genetic testing and gene therapy, is poised to provide tailored treatment options for genetic neurological disorders. Integration of EHRs with AI-driven clinical decision support will improve data accessibility and streamline care coordination. As these technologies evolve, they will likely lead to more proactive and preventive approaches in neurological care, thereby improving the quality of life for patients worldwide. Continued investment in research and development, along with collaboration between technology developers, healthcare providers, and policymakers, will be crucial in identifying the full potential of digital health in neurology.  A leader in the healthcare domain, SG Analytics assists healthcare companies in leveraging the power of information. Contact us today if you are in search of efficient Healthcare solutions to make sound business decisions.            About SG Analytics   SG Analytics (SGA) is an industry-leading global data solutions firm providing data-centric research and contextual analytics services to its clients, including Fortune 500 companies, across BFSI, Technology, Media & Entertainment, and Healthcare sectors. 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