Big data is no longer an emerging technology; it has officially established itself in the mainstream. Today, almost every organization is on its big data journey. With big data adoption growing, analytics is becoming the natural next thing to make sense of the accumulated data efficiently and ethically. With data analytics technologies maturing, they are shaping the markets across sectors.
Emerging Technologies in Data Analytics
Many emerging data analytics technologies are impacting organizational performance. And with businesses moving to a data-driven business model, integration of advanced data analytics is becoming necessary to survive the cut-throat competition in the digital age. In this evolution of data, the biggest impediment for organizations is the adoption of cultural challenges, including organizational resistance, operational alignment, and lack of understanding. The ability to employ data for intelligent business decisions is becoming the key differentiator for a successful and unsuccessful enterprise. Intelligent data-based workflows and seamless operational interactions are becoming the standard.
Read more: An Ultimate Guide: Business Intelligence in Data Analytics
Establishing a Technology-driven Data Analytics Strategy
Today, big data analytics plays a vital role in organizational efficiency. The upcoming data analytics technologies combine several techniques and processing methods to obtain relevant insights for strategic data management and implementation. However, to leverage the power of data and transform the enterprise, organizations are still struggling to establish a data-driven culture.
The benefits of data strategies are enabling organizations to gain a competitive advantage, thereby assisting with enhanced awareness of workforce gains by employing analytics as the basis for decision-making. How can organizations benefit by deploying a strong data analytics strategy –
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Reduce organizational costs
Data analytics solutions help in establishing efficient manufacturing processes with demand-driven production. Automation and the growing use of artificial intelligence (AI) are enabling businesses to reduce manual work to achieve cost efficiency in operations. Further insights help managers in designing frameworks that promote agile work environments, thereby reducing overall costs.
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Enhance workforce efficiency
Data-driven decision-making enables businesses to boost confidence in the processes as well as the employees. With employees becoming more proactive and productive with decision-making, quantifiable data helps in making insights-driven decisions. This further helps in increasing the efficiency of the organization as a whole.
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Establish competitive pricing
By creating differentiated pricing strategies, businesses can develop competitive pricing and associate revenue benefits. Organizations are also able to tackle similar products and services by employing big data to gain a price advantage.
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Drive brand loyalty
Customers respond better to relationship-driven marketing. By integrating data analytics, organizations can leverage the existing insights of a client's requirements and expectations to provide services accordingly. This enables them to significantly improve the chances of repeat orders and establish long-term customer relationships.
Read more: Data Analytics as a Service (DAaaS): A Brief Overview of the Next Data Evolution
Data Analytics Technologies
With data emerging as an increasingly significant part of everyday operations, the need to collect, store, and analyze data has never been greater. Data analytics technologies are constantly evolving to meet these challenges, and the landscape will only become more complex in the years to come.
Let's explore some of the latest data analytics technologies businesses should be aware of:
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Predictive Analytics
One of the exceptional tools for businesses to avoid risks in decision-making, predictive analytics assists in discovering, evaluating, and deploying predictive scenarios by processing big data. Predictive analytics solutions help businesses to be prepared for future trends as well as help them identify and solve problems by analyzing them. With the data analytics market maturing, enterprises are integrating data analytics to leverage data and predict the future.
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Across industries, predictive analytics is already in use.
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Banks and financial institutions are designing analytics solutions to predict and manage risks across credit cards, insurance premiums, loans, etc.
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Marketers are undertaking data analysis to display the right advertisement and attract the right audience at the right place.
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Law enforcement agencies are employing predictive analytics for crime detection and prevention.
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The healthcare industry is integrating its operation with predictive analytics for early disease detection.
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Augmented Data Analytics
One of the biggest problems businesses face with big data is the need to cleanse and prepare the data by building analytics and dashboards. But manual processes significantly restrict the capabilities of data analytics. It is due to this reason that augmented data analytics is steadily gaining attention. Augmented data analytics involves the use of artificial intelligence (AI) and machine learning (ML) along with natural language processing (NLP) for business intelligence. And chatbots are also assisting organizations with data discovery, data cleansing, data management, categorization, etc.
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Stream Analytics
Data accumulated within the organization needs to be processed and stored on multiple platforms in different formats. Stream analytics software is highly helpful in filtering, aggregation, and analysis of the accumulated data. With stream analytics, organizations can establish connections with external data sources and integrate them into the application flow.
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Distributed Storage
Distributed files help in containing the replicated data and offer a way to counter independent node failures and loss of big data sources. The data is often replicated for low latency, enabling quick access across large computer networks. These are usually non-relational databases.
Read more: How is Data Analytics Equipping Organizations to Accelerate Diversity and Inclusion?
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Data Virtualization
Data virtualization allows applications to retrieve data without implementing any technical restrictions like data formats. It is employed by many significant distributed data stores to establish real-time or near real-time access to data stored across different platforms. Data virtualization is also considered one of the most employed big data technologies.
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Data Integration
A key challenge for many organizations is handling the accumulated big data and processing it in a way that can be useful for customer deliverables. Data integration tools enable organizations to streamline their data across different big data solutions.
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Data-as-a-Service (DAAS)
Organizations are increasingly integrating Data-as-a-Service (DAAS) software into their operations. Established on cloud computing technology, DAAS enables businesses to access, manage, as well as analyze data from any location. It also makes data storage and analysis cost-effective. In 2023, more and more companies are expected to join the bandwagon and implement DAAS tools across their framework. The market value for DAAS is estimated to grow to $10.7 billion in the year 2023.
How can Businesses Benefit from Data Analytics?
Many businesses today lack the resources to go all in on their big data investments. Therefore, they require a smarter strategy to stay updated with emerging data analytics technologies. Instead of worrying about using big or small data sets, businesses should invest in small-scale analytics and focus on utilizing data technology analytics for enterprise decision-making.
Rather than collecting all sorts of data in anticipation of future usage, they should use data sets that solve immediate problems.
Business executives today rely on personal experience instead of business data-driven results. For an organization to integrate the tech, they need to introduce the big data culture across organizational operations. Employing cloud computing is also fundamental for implementing data analytics solutions effectively.
Businesses can benefit a lot more from big data implementation if they set their goals. However, the successes of businesses in implementing big data solutions rely on - the availability of data and the implementation of the right technologies.
Read more: Integrating Data and Artificial Intelligence in Business Strategy to meet ESG Goals
Final Thoughts - Data Analytics Technologies
In today's data age, the challenge for businesses lies not just in surviving the evolving landscape but also in thriving. The rapid adoption of data analytics tools is radically encouraging many corporations to gain an edge over competitors. Developing these technologies is further helping companies augment their operational potential and reimagine business fundamentals while driving value. This rise in new innovations is further set to accelerate change and expansive growth.
While data analytics is already being used to improve operational efficiency, it is also equipping organizations with the ability to make informed decisions and stay updated with the latest up-to-the-moment information.
And there is no doubt that data analytics will continue to play a vital part in many different industries. But, in order to reap more benefits, it is equally important for organizations to train their employees in evolving data analytics management. With proper management, businesses can become more productive and efficient.
About SG Analytics
With a presence in New York, San Francisco, Austin, Seattle, Toronto, London, Zurich, Pune, Bengaluru, and Hyderabad, SG Analytics, a pioneer in Research and Analytics, offers tailor-made services to enterprises worldwide.
A leader in Data Analytics, SG Analytics focuses on leveraging data management and analytics, and data science to help businesses discover new insights and build strategies for business growth. Contact us today if you are looking to make critical data-driven decisions to prompt accelerated growth and breakthrough performance.
