Do you prefer the broad or the narrow definition of BI, and how should we differentiate BI and analytics? Read on for my "extended jam" on these and other FAQs about business intelligence.
My Forrester colleagues and I will be doing a TweetJam on Thursday May 13, from 2-3pm (eastern), on the topic of business intelligence (BI).
If that topic sounds global, all-encompassing, and a tad nebulous to you, then we've succeeded. One of the favorite pastimes of BI analysts everywhere -- not just at Forrester -- is defining and redefining this uber-category known as BI. What exactly is it? Or rather -- considering that almost every data management technology has been swept into BI's gravitational orbit at one time or another by somebody somewhere -- what is BI not? What's analytics? What are decision support systems? How does this relate to knowledge management, content management, social media, and so forth. I even had a consulting client spontaneously grill me on all that yesterday in a face-to-face...um...talkjam (?).We, the Forrester brain trust on all things BI and BI-ish, are going to jam our Twitterfingers silly on that very topic for an hour or so on that mid-May day. On that jam, in addition to @jameskobielus, you should be following Boris Evelson (@bevelson), Rob Karel (@rbkarel), Noel Yuhanna (@nyuhanna), Holger Kisker (@hkisker), Leslie Owens (@owensleslie), and Gene Leganza (@gleganza). You'll also be able to roll up all of our tweets by searching on hashtag #dmjam (assuming we all remember to use it in all tweets -- sometimes I'm remiss).
Rest assured that we're all going to drill down to specifics very quickly. We'll have a lively back-and-forth tweet-volley on this topic. It's ongoing, of course, so you might have noticed that Boris already blogged his high-level perspective on the topic yesterday.
I don't necessarily disagree with anything Boris put into his post, but he and I sometimes take different approaches to the topic of BI. Take what I'm about to say as a contrapuntal riff in Jim's jam. To make this a quick read for you, I'm simply responding to the high-level questions we've put out there to organize our jam around clear sub-topics:
Do you prefer the broad or the narrow definition of BI? Should ETL, DQ, DW, MDM be considered part of BI?
Jim's extended jam: I'd recommend that BI be kept scoped narrowly to what specifically "touches" the consumer of intelligence. In other words, BI refers to whatever supports access, delivery, presentation, visualization, and exploration of information. Consequently, it's the core of what people normally identify as BI: reporting, query, online analytical processing clients, dashboarding, portal integration, Excel integration, mashup, and the like. I prefer that we distinguish all of these front-end applications, platforms, clients, and technologies from the mid-tier "data storage and persistence" approaches (i.e., data warehousing, data marts, cubes, operational data stores, staging nodes, distributed cache, etc.) that aggregate, store, and deliver the intelligence to BI. And I'd prefer that we keep "data integration and governance" approaches (i.e., extract transform load, event stream processing, data cleansing, master data management, etc.) in their own separate tier, which feeds intelligence either into the data storage/persistence layer or directly into the BI layer. That's a much cleaner, tighter way of modeling such a diverse interlinked set of technologies. Essentially, hold that in your mind as a "pipeline" that conveys intelligence from sources (e.g., your ERP and CRM systems) to consumers (e.g., you).
How should we differentiate BI and analytics?
Jim's extended jam: My feeling is that the broader paradigm is "decision support systems." See my triptych (1-2-3) of February blog posts on this very topic, which keys on the notion that high-quality analysis is what a decision-support infrastructure must support. A decision support infrastructure allows information workers to analyze a wide range of relevant options and leverage deep collections of current, correct, and comprehensive information. Fundamentally, decision support infrastructures deliver intelligence to analytic applications, to support analysis by human beings as well as by rules engines and other automated decision agents. "BI" is simply a catchall term for the underlying platforms and tools (reporting, ad hoc query, OLAP, dashboarding, etc.) that support analytic applications. Hence, analytics is the superset, because its supporting infrastructure includes not only BI, but also DW, ETL, and the like.
What's the difference between business intelligence and other forms of "intelligence" like competitive intelligence, market intelligence?
Jim's extended jam: No difference. "Business" intelligence is a non-domain-specific catchall for all the types of analytic data that can be delivered to users in reports, dashboards, and the like. When you specify the subject domain for this intelligence, then you can refer to "competitive intelligence," "market intelligence," "social intelligence," "financial intelligence," "HR intelligence," "supply chain intelligence," and the like. You can substitute "analytics" for "intelligence" in all of these phrases without any change of meaning. In fact, I prefer to refer to "BI" as "business analytics." It's more to the point. It even sounds better.
Is convergence of structured and unstructured information hype or reality?
Jim's extended jam: Definitely not hype. In fact, it's at the very heart of social network analysis, social media monitoring, listening platforms, and other analytics platforms that are driving sales, marketing, customer service, and CRM generally in this new economy. Stay tuned for future Forrester coverage in these areas from yours truly. Even more important, please read the exceptional research into all that from our Marketing & Strategy colleagues Suresh Vittal and Zach Hofer-Shall.
Is BI looking only through the rear-view mirror, or should historical and predictive BI be one and the same?
Jim's extended jam: Traditional BI (reporting, dashboards, OLAP) is rear-view mirror, for sure. But advanced analytics is all about predictive modeling, forecasting, what-if analysis, and other future-facing decision-support applications. It also includes complex event processing for truly real-time analytics. I like to say that a truly comprehensive analytics environment allows you to mine the deep past, deep present, and deep future. So you can get answers to questions across all time-horizons.
How will social media impact traditional BI?
Jim's extended jam: Social media are new information sources, hence social media monitoring -- both for CEP/real-time and predictive/future analyses -- is a key theme in advanced analytics. Social media are also a huge pool of behavioral information (posts, tweets, clickstreams, transactions, etc.) that is being mined to look for influencers, net promoters, and other patterns relevant to customer experience optimization, churn analysis, and other highly predictive analytics. That's why I'm focusing increasingly on social network analysis as a key new theme in advanced analytics. Oh, by the way, I'll be speaking on this very topic in my May Forrester teleconference, at the Forrester IT Forum in Las Vegas in the last week of May, at Netezza's late June conference in Boston, and at Teradata's conference in San Diego in late October. I would love to touch base with all of you in any of those venues to discuss further.
What's not BI? Well, I clearly have my positions mapped out. So I'm primed for the jam. But, please, jam us right back!Do you prefer the broad or the narrow definition of BI, and how should we differentiate BI and analytics? Read on for my "extended jam" on these and other FAQs about business intelligence.
The Agile ArchiveWhen it comes to managing data, donít look at backup and archiving systems as burdens and cost centers. A well-designed archive can enhance data protection and restores, ease search and e-discovery efforts, and save money by intelligently moving data from expensive primary storage systems.
2014 Analytics, BI, and Information Management SurveyITís tried for years to simplify data analytics and business intelligence efforts. Have visual analysis tools and Hadoop and NoSQL databases helped? Respondents to our 2014 InformationWeek Analytics, Business Intelligence, and Information Management Survey have a mixed outlook.