As enterprises seek to derive value from an ever-rising mountain of data, they increasingly rely on business intelligence (BI) tools to both visualize and analyze the performance and efficiency of their business operations. And, as visualization and data analytics become more central to the decision-making process within an organization, it follows that these capabilities are becoming more directly tied to the unique business operation they support—whether that be finance, sales, human resources, or something else.
Enabling this evolution is the emergence of two key trends: the move toward self-service data and the emergence of pervasive analytics—a capability that embeds analytics into the operational and transactional applications needed to run the business.
I can do it myself: the rise of self-service data
Just as the industry as a whole moves in the direction of a self-service model, so too goes the world of BI. Last spring, analyst firm Gartner noted that the BI industry had hit a tipping point in 2016, stating that “the balance of power had shifted from IT to the business.” In other words, the BI world is rapidly transitioning away from IT-led reporting in favor of business user-owned analytics. As a result, Gartner has elected to break away from how it has traditionally categorized BI and analytics platforms.
Once upon a time, there was just BI—a blanket term that Gartner used to describe the applications; infrastructure and tools; and best practices that enabled access to, and analysis of, information to improve and optimize decisions and performance. Yet, as the ownership of BI has moved away from the IT department and into the hands of the business user, Gartner sees a division in how it defines the BI vendor landscape overall. This has prompted a division of vendors and tools into two categories—traditional and modern.
Gartner identifies traditional BI solutions as typically IT-owned and managed, and used to peer into centrally managed data warehouses. Modern BI tools, on the other hand, allow the business users to go from raw data all the way to powerful visualizations all on their own. This drastically reduces the time it takes to build insightful analytics and visualizations.
Whether modern or traditional, data integrity issues remain
But there are downsides to the standalone modern BI tool approach, as the underlying integrity of the data can present a challenge. Business users are often left to their own devices to obtain, cleanse, and load data for analysis. Take the planning process, for example. This has traditionally been an entirely Microsoft Excel-based process—analysts build elaborate models to consolidate departmental forecasts, merge actuals with plan, and then generate reports and perform variance analysis. Among the many problems with this approach is that gaining confidence in the underlying data isn’t just hard and time-consuming, it can be virtually impossible.
According to a recent survey, CFOs, who are among the most voracious consumers of analytics in the enterprise, report that their organizations face significant challenges with respect to the accuracy and timeliness of data. The majority of CFOs reported that keeping data siloed (69%) and having inaccurate data for forecasting and planning (40%) were among the top financial mistakes most companies make.
So while modern BI tools give business users the autonomy to analyze and visualize data on their own, the fact that the data is often gathered from disparate spreadsheets and other flat files—a highly error-prone approach—has the potential to negate the value of the analytics.
As a result, there is a move toward making analytics pervasive, so that users never have to leave their primary tool to access analytics. Market analyst firm Nucleus Research predicts that in the next five years, 90% of analytics solutions for business users will be embedded in other core applications. Across many functions—finance, sales, human resources—we are already seeing this convergence, as BI, planning, and predictive modeling solutions merge.
It’s often said that necessity is the mother of invention, and the convergence of BI, planning, and analytics is without question being driven by user demand. Users need to be able to see their plan in the context of historic performance—in one place. They want to be able to run simulations to understand alternate courses of action and conduct scenario planning. They want systems to suggest trends based on historic data and to find anomalies. And they want to be able to visualize and analyze all of their data in real time.
Because that is what users need, that is where the technology is headed. Integrated BI tools that are directly tied to the planning environment provide a much clearer view of the business and enable users to glean the insights needed in real time. And that’s what I call coming together.
This article originally ran in insideBIGDATA.