Over the past decade, the practice of data analytics has skyrocketed. No matter what industry you work in, data analytics likely plays a key role in crafting your strategy.
In response, the market for data analytics software has climbed rapidly. According to IDC, worldwide spending on big data and business analytics solutions will climb 10.1 percent in 2021 to reach $215.7 billion.
And it’s worth noting that investment in data analytics did not drop off even in the darkest days of the global coronavirus pandemic. “Unlike many other areas of the IT services market, big data and analytics services continued to grow in 2020 as organizations relied on data insights and intelligent automation solutions to survive the COVID-19 pandemic,” said Jennifer Hamel, a research manager at IDC. “The next phase of digital resiliency will spur increased investment in services to address both lingering and new challenges related to enterprise intelligence initiatives.”
Researchers at Gartner came to similar conclusions: “Increasingly, data and analytics has become a primary driver of business strategy, and the potential for data-driven business strategies and information products is greater than ever. This is particularly in response to the continuing situation with COVID-19, which has been an accelerant for digital transformation and data-driven business.”
By 2023, overall analytics adoption will increase from 35% to 50%, driven by vertical and domain-specific data mining techniques, according to the research firm.
Given the prominent role that data analytics trends play in the modern enterprise, it makes sense to examine, “What is data analytics?” as well as to consider why it is important and what its future might hold.
- What Is Data Analytics?
- Data Science vs. Data Analytics
- Types of Data Analytics
- Why Is Data Analytics Important?
- Benefits of Data Analytics
- The Future of Data Analytics
Data analytics is the process of analyzing data trends to gain knowledge and insight for better decision making.
To be sure, definitions of data analytics vary less than some other technology definitions, primarily because experts agree that data analytics encompasses just about anything you might do to data. For example, Gartner defines data analytics as, “The management of data for all uses (operational and analytical) and the analysis of data to drive business processes and improve business outcomes through more effective decision making and enhanced customer experiences.”
These wide definitions can encompass a wide range of activities that are common in modern enterprises. For example, data analytics can include many of the following:
- Data mining
- Text analytics
- Data visualization
- Business intelligence
- Data Catalogs
- Data warehouses
- Data lakes
- Data fabric
- Data modeling
- Artificial intelligence (AI)
- Machine learning (ML)
- Deep learning
In addition, a wide range of disciplines make use of data analytics and assorted big data trends, from finance to accounting to product management to manufacturing. Data analytics is integral to research and development, engineering, and strategic planning. And of course it is the very heart of logistics and supply chain management. With every year, analytics plays a larger role in information technology and cybersecurity. In sum, there is hardly an industry that isn’t driven by data analytics.
Today, many organizations have a chief data officer whose job it is to oversee all aspects of data management within the organization, including data analytics and data science.
Although they are similar and closely related—and often confused—data science and data analytics are not the same thing.
In a nutshell, data analytics is a business discipline, while data science is a technological discipline. The goal of data analytics is to answer a particular business question, while the goal of data science is to prepare, transform and organize data so that it is useful. Data analytics requires deep knowledge of a particular business domain, like finance or marketing, while data science requires deep knowledge of mathematic and technological disciplines, like statistical modeling and programming.
Harvard Business Review explains, “Data analytics refers to the process and practice of analyzing data to answer questions, extract insights, and identify trends…Data science is centered on building, cleaning, and organizing datasets.”
Northeastern University adds detail to that definition, noting, “Data analysts examine large data sets to identify trends, develop charts, and create visual presentations to help businesses make more strategic decisions. Data scientists, on the other hand, design and construct new processes for data modeling and production using prototypes, algorithms, predictive models, and custom analysis.”
In practice, data scientists and data analysts often work together very closely and may even be part of the same team within an organization.
Not all data analytics are the same. Most experts divide data analytics into four key types:
Descriptive analytics describes what happened in the past or what is currently happening. This type of analytics answers questions like who, what, where, when and how. For example, a sales report that shows your monthly sales over the past four quarters is an example of descriptive analytics. This is the easiest type of analysis to perform, but it has only limited value to the organization. You can’t leave it out, however, because descriptive analytics is a necessary foundation for the more advanced types of analytics.
Diagnostic analytics tells you why something happened. For example, if your descriptive analytics informed you that sales dropped last quarter, diagnostic analytics would help you figure out what went wrong. This type of analytics usually involves combining multiple data sets to create a more full and accurate assessment of your situation. Maybe your sales drop happened because of supply chain problems or bad weather or because you lost a key account after hiring a new salesperson. Diagnostic analytics can help you figure that out.
Predictive analytics helps you understand what is likely to happen next. It takes a look at historical trends, looking for patterns that will offer insights into the future. Often predictive analytics tools rely on advanced data models and machine learning technology that can distill the important factors that impacted past performance and apply those to the current situation. This is a much more advanced and speculative form of analytics with a high potential value. It is becoming a very common tool, particularly for large enterprises.
Prescriptive analytics attempts to tell you what you should do about what is likely to happen in the future. For example, if your predictive analytics forecasts lower sales for next quarter, prescriptive analytics can help you see how that might change if you lower your prices or change your marketing strategy or source product from a different supplier. Obviously, the potential benefit with prescriptive analytics is extremely high, but it is also very difficult to do prescriptive analytics well. Currently few organizations have the resources and capabilities to do prescriptive analytics at scale.
Most organizations start their data analytics journey with descriptive analytics. Over time, they expand into diagnostic analytics, then predictive analytics. Many aspire to eventually have a successful prescriptive analytics program to better inform their business decision-making.
Each of the four main types of analytics plays an integral role in an overall data analytics practice.
Most experts agree that data analytics is tremendously important for modern organizations because it helps them become more competitive. Forrester puts it this way: “Data holds the key to improving customer experience and operational efficiency, which in turn fuels company success. Unlocking data’s full potential relies on sound data analysis.”
Organizations undertake data analytics initiatives for a large number of reasons. Some of the most common things you can do with data analytics include the following:
Better understand your customers. Most organizations have access to a wide variety of data about their customers, including demographics, order history, customer service interactions, social media, browsing history, survey responses and more. Analyzing this data can help companies create a fuller picture of each individual customer as well as an aggregate picture of their customers as a whole. In addition, it might highlight opportunities to better meet customer needs or reach new groups of buyers.
Streamline business operations. Many of the processes within your organization, from order taking, to fulfillment, to supply chain management, to customer service, to IT operations and more are measurable. And anything you can measure, you can improve. Data analytics can help you track progress towards key performance indicators (KPIs) and help you identify bottlenecks that might be slowing your organization today.
Identify new opportunities. One of the more interesting areas of data analytics is the discipline of whitespace analytics. This practice helps organizations identify business that they aren’t doing today that they could be doing. It can help you find new customers, new products and new partnerships to pursue that could increase revenue and margins.
Capitalize on existing trends. Even the most basic data visualizations make it easy to see which direction KPIs are moving and at what rate. By identifying these trends, you can do more of the things that are working well and attempt to correct things that are heading the wrong way.
Market more effectively. Marketing is one the business disciplines that has been most transformed by data analytics. Because so much marketing takes place digitally, marketing teams have a wealth of data available that can help them identify which targets are most likely to become customers, which customers are likely to buy again, which customers are in danger of defecting to a competitor and much more.
Improve your pricing strategy. What if improving your prices by just 1 percent can increase your organization’s overall margins by as much as 10 percent? Analytics can help you analyze the variables. Data analytics can help pricing teams identify where they should increase prices (and where they should decrease them) in order to maximize profitability.
Make better decisions. Humans are always tempted to make decisions for emotional reasons, often based on preconceived notions that may or may not be true. Data analytics provides a strong check to this instinct so that business leaders can see whether their gut reactions are likely to result in success or not. In a very broad sense, data analytics can help businesses improve their decision-making across the entire organization.
The ultimate result of all these activities enabled by data analytics is often visible in the bottom line of the organization. Business leaders say that data analytics helps them:
Reduce costs by streamlining business operations, rightsizing technology spending, improve inventory management and better negotiate with suppliers.
Speed time-to-market by quickly identifying new product opportunities, enhancing development processes, enabling faster testing and improving overall quality.
Improve customer satisfaction by better meeting customer needs and giving customer service agents the tools, training and support they need.
Increase sales by improving product offerings, enhancing marketing efforts and empowering salespeople.
Increase margins by reducing costs and optimizing prices.
Improve the accuracy of forecasts by analyzing historical data and using machine learning to enable predictive and prescriptive analytics.
In the next few years, the use of data analytics will almost certainly continue to grow dramatically. However, not all organizations will succeed with their analytics efforts.
In short, analytics are now essential. Gartner warns, “Through 2025, 80% of organizations seeking to scale digital business will fail because they do not take a modern approach to data and analytics governance.”
In addition to data governance, other key trends to watch include the following:
Cloud computing. Today, most data analytics is happening in the cloud, and that trend is likely to increase. Because organizations store much of their data with cloud providers, it makes sense to analyze where it is stored to minimize costs and take advantage of the scalability and reliability of cloud services.
Artificial intelligence and machine learning. Many of the most complex forms of data analytics, including predictive and prescriptive analytics, rely on artificial intelligence and machine learning capabilities. As these technologies advance, analytics will become even more powerful.
Synthetic data. Privacy regulations often limit the amount of analytics that organizations can perform directly on customer data. One of the ways to get around this is with synthetic data, which is anonymous and usually generated by data models and algorithms.
Multiple analytics solutions and hubs. Most large enterprises find that no single analytics solution meets all their needs across the entire organization. Experts say that the most successful companies are likely to be those who find innovative ways to combine their various analytics solutions and data repositories.
Organizations that stay on top of these trends—and others identified by their data analytics efforts—are likely to be the most successful over the long term.
Also see: Guide to Data Pipelines