Today’s fastest-growing, most successful and most innovative companies are fueled by data.
In the modern information economy, the enterprises best able to harness data’s near-limitless power will become the most efficient, results-driven and future-focused organizations. They’re the ones that will be prepared to win in tomorrow’s fast-paced business climate.
To become data-driven, however, your enterprise will need the right combination of tools, processes and practices to collect, integrate and analyze large volumes of information.
You’ll also need the right technologies to present the results of your analyses to business decision-makers so the revealed insights become actionable.
Both data analytics and business intelligence are among these essential tools and technologies. Some stakeholders use the two terms as if they’re interchangeable. We argue that while both data analytics and business intelligence are valuable, there are important differences between them.
Let’s take a closer look at what these are.
What is Business Intelligence?
Business intelligence (BI) is a comprehensive set of tools and processes that gather historical data from across the enterprise for the purpose of creating descriptive and diagnostic reports. It enables stakeholders and decision-makers to understand what the organization has accomplished at a summary level.
Business intelligence tools work by aggregating data to enable various statistical analyses. The more sophisticated the tool, the more granular the data and queries it can support. A user might, for example, notice the current quarter’s sales results seem lower than usual; drilling down into the data with a BI tool would allow them to see how regional variability contributed to that outcome.
The popularization of enterprise BI tools has been enabled by the recent proliferation of inexpensive storage. Emerging BI solutions now support more types of analysis at greater scale than was ever possible in the past.
They’re also easier to use than ever before. Not only do today’s BI tools no longer require their users to know specialized programming languages, such as Python or R, to organize, aggregate or visualize data, but some even make it possible to create dashboards using natural language voice commands.
Because of their ease of use, these tools allow growing numbers of employees — including those without extensive technical skills — to access the insights they reveal. Today’s BI solutions are thus democratizing access to data intelligence.
What is Data Analytics?
Data analytics is a much broader disciplinary area than business intelligence. In fact, BI can be understood as a subset of data analytics.
Whereas BI concerns itself largely with historical data and thus is limited to providing a retrospective view of what’s taken place in the past, data analytics can be applied in any inquiry involving large or complex data sets, including predicting what will occur in the future.
Data analytics involves using scientific methods such as mathematical modeling, artificial intelligence (AI)- and machine learning (ML)-based simulations and algorithms and other techniques from data science to draw conclusions from the available data. It’s possible to conduct more complex and multifaceted analyses than can be performed with enterprise BI tools, but more expertise is typically required.
While Business Intelligence Enables Top-Down Analyses, Data Analytics Enables Bottom-Up Approaches
Business intelligence solutions are primarily descriptive in nature. They enable non-specialist stakeholders to understand what’s taken place in the past and present the results to a broad audience. This supports the large-scale adoption of data investigation across the whole of the organization.
The market leading BI tool, Microsoft Power BI, originally emerged as an add-on to Microsoft Excel. Many of its data visualization and querying capabilities were expressly designed to be intuitive for proficient spreadsheet users to master. Though its visualizations enable users to see what happened and to form conjectures about how and why, it — and other popular BI solutions — lack the powerful forecasting capabilities and predictive modeling capabilities that are available to data scientists with more specialized skills.
For instance, the users of a BI tool might speculate that there is some relationship between weather patterns and sales cycles. They’d then collect data on sales transactions from the business as well as on the local weather conditions from a third-party source. Using this information, they’d determine that, say, sales go up whenever it’s warm and sunny outside. On the basis of that information, they’d build a model to instruct the business to send more of its products to the Northern hemisphere in the summertime. This is a top-down approach.
In contrast, data scientists would start their analysis of the company’s transaction data by sifting through it all with the goal of uncovering interesting relationships within it. While they might reach the same conclusions that the BI users did, they might also discover that product sales peak when it’s 82 degrees outside, and again begin to decline when the mercury goes above 94, and that there’s a surprising increase in purchases on rainy days in Sun Belt states. This bottom-up method of analysis is unstructured and exploratory in nature. Inherently, it involves more complexity and requires more skill, but may reveal patterns that are valuable precisely because they’re unexpected.
As business intelligence tools continue to evolve, their capabilities are converging with those of specialized data analytics solutions. Having access to operational BI is now table stakes for major enterprises, and adoption among small and midsized organizations is on the rise.
But organizations of all sizes are still challenged to build the data infrastructures and pipelines that can transform all of their data — including semi-structured and unstructured data — into fuel for insights. Many are still working to create organizational cultures that embrace this kind of decision-making. And many still struggle to hire and retain the necessary talent.
If your organization needs help unlocking your business data’s full potential for use in an ever-growing array of BI and analytics applications, contact a member of our data analytics team today to learn how Cloudreach can help you make better decisions and gain an edge over the competition.