TDWI Upside - Where Data Means Business

Meet the New BI, Not the Same as the Old BI

The priorities of business intelligence and analytics buyers have changed drastically over the last few years. The BI/analytics shopping lists of today look almost nothing like they used to.

The priorities of business intelligence (BI) and analytics buyers have changed drastically over the last few years. Just how drastically? It isn't a stretch to say that the BI/analytics shopping lists of BI buyers today look almost nothing like those of 2012 or even 2013.

According to a new report from Gartner, "Survey Analysis: BI and Analytics Spending Intentions, 2017," organizations this year will pour money into data mining, predictive analytics, location and geospatial intelligence, and an assortment of other advanced analytics technologies.

"As users demand broader access to insights from advanced analytics ... companies are prioritizing investments that support advanced analytics on large and complex data. A higher percentage of customers plan to extend their BI platforms by investing in advanced data analytics techniques in 2017 than in 2016," the Gartner report indicates.

The difference between BI-present and BI-past is best illustrated by a substantial uptick in investment in advanced analytics. "Data mining [and] predictive analytics followed by location and geospatial intelligence are ranked highest in terms of percentages for total use, while [natural language processing] ... technologies such as search and analytics on unstructured data ... experienced double-digit increases in total use," according to the report.

Organizations continue to invest in data warehouse-like systems; increasingly, however, they're doing so in new contexts (such as cloud) or for specialized use cases, such as what Gartner calls "high-capability" (massively parallel processing, high-concurrency, mixed-workload) data warehousing. At the same time, uptake of Hadoop, Spark, and other alternatives to data warehouse platforms continues to increase.

Speaking of the cloud, even though uptake of cloud BI remains flat, active subscribers continue to expand their use of cloud BI and analytics -- even as some traditional BI/analytics workloads, such as data warehousing, are starting to shift to the cloud at a rapid rate.

"[S]treaming analytics ... cloud data warehousing, high-capability data warehousing and Hadoop/Spark had the highest percentage-point increases in total use," the report says.

BI: Then and Now

Organizations are not just supplementing their existing BI investments with cutting-edge advanced analytics technologies. Many are also spinning up new investigative computing or data science practices, and some are even experimenting with deep learning, artificial intelligence (AI), and other highly complex forms of advanced analytics.

Half a decade ago, none of these technologies or practices was anywhere close to mainstream in BI. Reports, dashboards, scorecards, and ad hoc, OLAP-powered analysis still predominated. Now, in 2017, machine learning, predictive analytics -- i.e., human-directed machine learning -- graph and network analysis, etc. are seen (by Gartner and others) as integral components of the modern data BI and analytics platform.

It's as if there's a complete disjunction between the "modern" BI paradigm and the paradigm that preceded it. It's as if the very concept of "BI" has changed almost overnight.

Long Time Coming

The change wasn't so sudden (nor so disjunctive) as this, of course.

Last year, Gartner revised the criteria it uses to vet products for inclusion in its "Magic Quadrant for Business Intelligence and Analytics Platforms."

The market landscape had changed fundamentally, Gartner said -- and with it, customer buying behavior. "We really said in response to a multi-year shift of where the market is going, it's not useful to buyers for us to compare something like [Microsoft's SQL Server] Reporting Services with Tableau," Gartner analyst Cindi Howson told Upside last year.

The 2017 spending survey report, coauthored by Howson, Gartner analyst Rita Sallam, and six other analysts, underscores the extent to which the BI and analytics space has changed.

Gartner distinguishes three core components of what it calls the "modern" BI and analytics platform. These are: an information portal, a self-service analyst workbench, and a lab tool -- presumably chock-a-block with self-service features -- for data scientists.

The components that were core to the BI and analytics platforms of the past -- viz., the data warehouse, a built-in and/or third-party OLAP engine/optimization layer, and one or more end-user-oriented BI reporting tools -- aren't explicitly indicated in Gartner's definition.

They're implied, to be sure, as sources for the information portal, for example, or as one of several feeder sources for self-serving analysts and data scientists. However, it's hard to avoid the conclusion that Gartner's modern BI and analytics platform looks nothing like BI as we knew it. Is this true? Is it really the case that the lay of the BI and analytics landscape has changed so drastically -- and so quickly?

Yes and no.

Gartner's report distinguishes between "Magic Quadrant Vendors" and so-called "Market Guide Vendors." The former are primarily user-oriented, self-service BI and analytics offerings; the latter, traditional, data warehouse-driven, enterprise reporting offerings. Products from Magic Quadrant Vendors are candidates for Gartner's modern BI and analytics platform; products from Market Guide Vendors are not.

According to Gartner, most organizations are increasing their investments in modern BI and analytics tools. The same cannot be said about (let's go ahead and say it) "legacy" tools, however. "More than half of the organizations surveyed across all vendor types are growing business-user-authored and centrally managed modern content, while more than two-thirds are maintaining or reducing traditional enterprise reporting," the Gartner report indicates.

At the same time, the Gartner report says, traditional -- i.e., "legacy" -- BI and reporting products continue to endure as systems of record in many accounts. In some cases, users are actually expanding their use of these products. "Market Guide vendors ... have the highest percentage of customers increasing their use of traditional reporting and the lowest percentage of customers reducing it. This suggests their continued importance."

New Investments in Self-Service, Advanced Analytics

Gartner's report is based on a survey of almost 2,000 enterprise BI and analytics customers. It's surely right that both BI product offerings and buying habits have changed. It's likely right that traditional BI priorities -- such as reports and dashboards -- aren't so much diminishing in importance as being assimilated into and exposed by new interfaces, user experiences, and delivery contexts. At the same time, Gartner recognizes that traditional BI technologies, such as enterprise reporting, will continue to address business-critical needs.

The reality is that the market is saturated with traditional BI. Because basically everybody already has it, BI has ceased to function as a key driver for value and competitive differentiation. Core BI technologies are arguably as necessary as they ever were; it's just that organizations are managing them in a fundamentally different way.

Instead of investing new money into traditional BI, they're focusing on containing costs.

Self-service analytics discovery and advanced analytics technologies are seen as potential drivers for value and competitive difference. Not surprisingly, they're focal points for new investment. Gartner's modern BI and analytics platform criteria reflect this phenomenon.

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