Yesterday the Gartner Analytics Community held our first Twitter Chat or Tweet-Up (if you prefer) to discuss ideas around collaborative analytics, BI for “big data” and defining & aligning enterprise metrics. Over a half dozen Gartner analysts participated. Hey, where else can you get access to that many Gartner analysts in one place at the same time? Many individuals from other organizations also shared their perspectives and questions.
Metrics Development and Alignment
First we discussed the notion that organizations need to do a better job at defining and aligning metrics throughout their various BI/analytic initiatives. We shared one of Gartner’s strategic planning assumptions (SPAs) for 2012, that through 2014 fewer than 30% of enterprises will align metrics completely with enterprise business drivers. Some departments may focus on growth-oriented metrics, while others on profit-oriented or customer service-oriented.
Rowing in the same direction is important, but analyst Bill Gassman (@BGassman) noted that there may be metrics specific to a business unit that help it optimize, but are not necessarily tied directly to overall business drivers. Bill also advised that organizations should learn the difference between metrics, measures and KPIs.
Other participants suggested that metrics developers should “read the annual report and start there [for] clarity and focus,” (analyst @AdamSarner) and cautioned that “metrics alignment drive behavior, and usually does [but] not only in expected ways.” (Bill Gassman). Participant @Bob_Hetu warned about too many metrics–“focus should be on those particularly those not tied to driving revenue, profit and corporate strategies,” and participant @lorita observed that “BI sometimes builds walls.” Analyst Carol Rozwell (@CRozwell) noted that people sometimes go beyond or against established metrics if they think it’s the right thing to do. “Culture rules” she wrote. Analysts @IanABertram and @Doug_Laney advising that instituting a BI Competency Center is one way to help achieve collaboration, sharing and common understanding.
BI Challenges and Opportunities for Big Data
Next we chatted about the convergence of analytics and big data–how each one is both an enabler and limiting factor of the other.
Participant @Brett2point0 wrote that the biggest opportunity is “drilling down to unforseen insights, the ‘unknown unknowns.'” Bill Gassman observed that big data techniques are important and “at the leading edge, but still a long way to go with analyzing the data we have.” @Kalido_CEO suggested a particular technology, but Doug Laney countered, “Are tools the answer? We’re told by clients that finding people to model business probs is the bigger issue,” after which the conversation side-tracked a bit into the need for and role of the “data scientist.”
Analyst Jenny Sussin (@JSussin) observed that the big problem with analyzing big data is dealing with real-time data, and Ian Bertram questioned “Doesn’t it really depend on the speed of decision making within an org?” Doug Laney agreed, sharing that he’s “seen some orgs want data feeds an order of magnitude more frequent than they or their systems make decisions!” Adam Sarner encapsulated it by writing, “Real time can be elusive. However, just in time or right time can be much more obtainable, relevant and effective.”
Analyst Rita Sallam (@rsallam) brought up that data bigness is about more than volume: “I think the biggest opportunities come from being able to apply analytics to diverse data.”
Participant @My_AYons highlighted the potential of RFID to “reflect price for a consumer in real time based on the basket.” Others agreed that retail is a fertile ground for big data analytics, and @pstonier suggested that it’s “possible to effectively make offers in real time…personalized in-store promos could be very effective.” Bill Gassman advised that real-time offers “don’t have to be perfect, just move the needle.”
Finally we chatted about what collaborative analytics means and what to look for in technologies and implementations.
Bill Gassman tweeted about benefits first: “A collaborative analytic process encourages transparency and provides an audit trail for how decisions were made,” and Carol Rozwell noted that collective intelligence is one promise of collaborative analytics, and a way to “fight low employee engagement.”
Doug Laney mentioned that he’d seen some BI tools that simply allow for report/chart annotation and sharing via email or shared workspaces, but that “other tools recommend users to collaborate with based on their role and analytic history” while you’re analyzing.
Rita Sallam advised that collaborative decision making (CDM) works “when there is a specific high-value line-of-business decision, e.g. portfolio optimization, vendor selection, collaborative forecasting.” And seeing Rita’s perspective Doug added that “collaborative analysis (technology) isn’t the same as collaborative decision-making (process).”
@Armennajarian agreed with Rita that “the next wave of analytics will include an embedded social layer.” And regarding enabling collaborative analytics, Doug recommended that “Collaborative decision making needs a ‘peer review’ kind of mentality/culture/process.”
Finally, analyst Jenny Sussin concluded, “I feel sick…it’s either this #GartnerChat or the bag of sour gummy worms I have eaten during it.”
Looking forward to having you join our next #GartnerChat on BI/analytics. BYO gummies.
Follow Doug on Twitter: @Doug_Laney
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