Data analytics is assisting businesses in a number of ways. By understanding data analytics, businesses can make better decisions while allocating their resources as well as their products or services. Additionally, data analytics also helps businesses in identifying trends and understanding their customer base. Data analytics software further assists in tracking and analyzing data, allowing them to create actionable reports and dashboards. Let's explore some of the best data analytics tools and software available today.
Data Analytics Tools - Introduction
In today's digital world, data is everywhere. With the sheer volume of data growing at an unprecedented rate, organizations are turning to effective big data analytics tools to make sense of the vast amount of information. A recent market research survey highlighted that the global big data and analytics market is expected to be worth $274 billion. Almost 2.5 quintillion bytes of data are generated daily, due to which selecting the right data analytic tool has become more critical than ever.
Data - a powerful tool available to every organization at a staggering scale - if harnessed correctly, holds the potential to drive decision-making, influence data strategy formulation, and enhance organizational performance.
Some of the other benefits of data analytics include:
-
Enhanced efficiency and productivity
-
Better financial performance
-
Creation of new products and services
-
Improved customer acquisition as well as retention
-
Enhanced customer experiences
-
Competitive advantage
Analyzing data effectively can enable businesses to draw conclusions, predictions, and actionable insights and further drive impactful decision-making.
But how can businesses harness the power of data and experience these benefits?
Read more: Exploring the Latest Data Analytics Trends: Industry Insights
Understanding the Importance of Data Analytics
Data analysis is being used in tandem to design a full picture of the story data and make informed decisions. To understand a company’s current situation, organizations are employing descriptive analytics to identify how a company got there and leverage diagnostic analytics. Predictive analytics is further used to determine the trajectory of a situation. Data analytics further helps in considering all aspects of current as well as future scenarios and planning actionable strategies.
Depending on the problem, they can try to solve and achieve the goals or use data analytics tools in sequential order to gain a deeper understanding of the data. Strengthening the organization's analytics skills can further help take advantage of the insights the data has to offer.
Types of Data Analytics
-
Descriptive Analytics
The simplest type of analytics, descriptive analytics, is the foundation on which other types of data analytics are built. It allows businesses to pull trends from raw data and describe what is currently happening. Descriptive analytics helps in answering the question, “What happened?” Data visualization is a realistic fit for communicating descriptive analysis as charts, graphs, and maps help depict the trends in data in a clear and more understandable way.
-
Diagnostic Analytics
Diagnostic analytics helps in addressing the next logical question, “Why does this happen?” Taking data analysis a step further, it includes comparing coexisting trends, uncovering correlations between variables, and distinguishing causal connections. Diagnostic analytics is further used to get to the root of an organizational issue.
-
Predictive Analytics
Predictive analytics tools are employed by businesses to make predictions about future trends or events and find the question, “What can happen in the future?” By analyzing historical data in tandem with current industry trends, businesses can make informed predictions about the future of the company. Making predictions for the future can further help organizations in formulating strategies based on likely scenarios.
-
Prescriptive Analytics
Prescriptive analytics helps businesses find answers to the question, “What to do next?” Prescriptive analytics considers all possible factors in a scenario and suggests actionable takeaways. This type of analytics is useful for making data-driven decisions. Machine-learning algorithms are employed to help parse through large volumes of data and present the optimal next step.
Read more: 2023 Outlook: Top Data Analytics Technologies Transforming Organizational Operations
Key Features to Look for in Data Analytics Tools
The significance of making informed decisions when choosing the right data analytics tools to manage and analyze data. Data analytics tools enable businesses to gain insights into customer behavior, streamline business operations, and optimize business processes for informed decisions. Let's dive in and discover some of the key features to consider when selecting data analytics tools.
Feature #1: Data Exploration
Data exploration is crucial when selecting big data analytics tools. It is a vital first step in making sense of the vast amounts of data that businesses generate on a daily basis. With the sheer volume and variety of data available, data teams explore the data and identify patterns and insights that can be valuable to the business. By exploring data thoroughly and deriving meaningful insights, data analytics tools help streamline the process, reduce the effort in testing new hypotheses about the data, and identify useful connections in the data. In addition, strong data visualization capabilities are critical in the data exploration process. Visualizations help in identifying patterns and insights that are not apparent from raw data. They assist in interacting with data in a more intuitive way, making it easier to surface useful insights.
Feature #2: Version Control
In any big data analytics, numerous data scientists and other users work together to adjust the parameters of analytics models. While these changes initially look promising, they often create unexpected issues when further tested or pushed into production. This is where version control becomes an essential factor of consideration. Version control helps improve the ability to track ongoing changes, making it easier to roll back an analytics model to a previous version. This feature ensures that changes made by multiple users can be tracked and saved, authorizing for easy identification and accountability.
Feature #3: Data Management
With the magnitude of data increasing rapidly in big data environments, there is a growing need for data analytics tools to manage data sets that include large numbers of inconsistencies and different data formats. To ensure continuity as well as standardization across all deliverables, big data analytics tools require a robust and effective data management platform as a foundation. Data management solutions are critical for data analytics tools as they help in efficiently managing, storing, and processing large and complex data sets. They also ensure data consistency and eliminate redundancies, equipping businesses to maintain a single source of truth. Data management features also provide visibility into data sets, making them accessible for users to draw insights and make informed decisions.
Feature #4: Embeddable Outcomes for Real-Time Analytics
Insights generated from analytics models are used to support ad hoc business decisions, making big data analytics tools significantly more useful for enterprises. Incorporating these insights into a decision-making process in real time is of the utmost priority. With the right big data analytics tools, businesses can incorporate generated insights into real-time decision-making processes. Data analytics tools provide features that facilitate the creation of insights in an easily embeddable format, allowing businesses to apply insights in a real-time stream of data. Equipped with robust reporting capabilities, data analytics tools help monitor business initiatives' progress. These reporting capabilities can be further embeddable into existing business applications, offering businesses real-time decision-making.
Feature #5: Data Wrangling and Preparation
Data wrangling and preparation are critical aspects of big data analytics that include cleaning, labeling, as well as organizing data to make it suited for analytics purposes. Data wrangling encompasses different steps, including data collection, profiling, transformation, and validation, to integrate data from disparate sources. The process of data wrangling is time-consuming and often takes up a significant portion of the time. However, with the right data analytics tools, the process can be streamlined, freeing up the time to focus on more value-added tasks.
Read more: Employing Data and Analytics to Drive a Strong Marketing Strategy
Popular Data Analytics Tools
Today, data analytics has become more pervasive, democratized, and composable. With analytics being already omnipresent across all major business sectors, the demand for insights is challenging and continuing to challenge analytics leaders to keep up with the demand. This is compelling institutions to build systems that can help expand and shrink with the cycles.
Data analytics tools help in examining data from different angles and create visualizations that help illuminate the brand story. Algorithms and machine learning (ML) fall into the data analytics category and can be employed to gather, sort, and analyze higher volumes of data. To keep pace with the analytics adoption across organizational operations, businesses are turning to emerging data analytic tools.
Some of the popular data analytics tools that organizations can keep an eye on include:
-
Microsoft Power BI: Best suitable for data visualization
-
Tableau: Best for Business Intelligence (BI)
-
Qlik Sense: Best for Machine Learning (ML)
-
Looker: Best for data exploration
-
Klipfolio: Best for instant metrics
-
Zoho Analytics: Best for robust insights
-
Domo: Best for streamlining workflows
Read more: Data Analytics as a Service (DAaaS): A Brief Overview of the Next Data Evolution
Future Outlook of Data Analytics Tools
Data analytics in business is a thriving field offering tremendous potential for growth. With the field of data analytics experiencing a bullish trend in the market, an increasing number of companies are investing in data. This optimism is making data companies’ stock valuable and boosting stakeholder confidence. Businesses are relying on data-driven insights, and due to this reason, data analysts are becoming increasingly essential in meeting their needs.
A modus operandi for many businesses, there is an increasing vitality of data-driven decision-making as well as the emergence of innovative technologies like AI and machine learning, enabling more advanced and insightful data analysis. Furthermore, staying updated on emerging technologies and industry trends is vital for businesses to stay relevant in this field.
SG Analytics, recognized by the Financial Times as one of APAC's fastest-growing firms, is a prominent insights and analytics company specializing in data-centric research and contextual analytics. Operating globally across the US, UK, Poland, Switzerland, and India, we expertly guide data from inception to transform it into invaluable insights using our knowledge-driven ecosystem, results-focused solutions, and advanced technology platform. Our distinguished clientele, including Fortune 500 giants, attests to our mastery of harnessing data with purpose, merging content and context to overcome business challenges. With our Brand Promise of "Life's Possible," we consistently deliver enduring value, ensuring the utmost client delight.
A leading enterprise in Data Analytics, SG Analytics focuses on leveraging data management, 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.
Frequently Asked Questions (FAQs)
A. What is the role of data analytics in business?
Data analytics enables businesses to optimize their performance, perform efficiently, maximize profit, and make strategically guided decisions. The techniques and processes of data analytics can be automated into mechanical processes and algorithms that work over raw data.
B. How do I choose the right data analytics tool for my organization?
To choose the right data analytics tool, search for specific capabilities based on business needs, like data visualization, predictive analytics, or integration with existing software. A data analytics tool should align with the requirements while providing the most value.
C. What are the ethical considerations in data analytics?
Ethical data analysis demands a commitment to data accuracy and integrity. Ensuring that data is of high quality and accurate helps in preventing incorrect conclusions or misleading results.
D. Can small businesses benefit from data analytics?
Data analytics is not exclusive to big corporations. It's a powerful tool that allows small businesses to function effectively, build strong brands, enhance customer experiences, and keep a competitive advantage in the marketplace.
E. What are the prerequisites for learning data analytics?
Data analyst prerequisites enclose a solid foundation in basic mathematics, such as algebra, calculus, and probability. In addition to this, an understanding of Microsoft Excel or any spreadsheet software is essential.
About SG Analytics
SG Analytics is an industry-leading global insights and analytics firm providing data-centric research and contextual analytics services to its clients, including Fortune 500 companies, across BFSI, Technology, Media and entertainment, and Healthcare sectors. Established in 2007, SG Analytics is a Great Place to Work® (GPTW) certified company and has a team of over 1100 employees and has presence across the U.S.A., the U.K., Switzerland, Canada, and India.
Apart from being recognized by reputed firms such as Analytics India Magazine, Everest Group, and ISG, SG Analytics has been recently awarded as the top ESG consultancy of the year 2022 and Idea Awards 2023 by Entrepreneur India in the “Best Use of Data” category.
