When preparing for our upcoming Forrester Data Management Tweet Jam (May 13th, 2-3pm ET) -“What BI is Not!”- I got together with a few of Forrester’s data management and BI analysts to discuss some of today’s key BI questions.
The question on the table was, “How will social media impact traditional BI?”
Here’s a snapshot of what we talked about:
Jim Kobielus: (Twitter: @jameskobielus)
Social media will spur dramatic evolutionary shifts in traditional BI architectures in several ways. For starters, vendors will bring the Wikipedia and Facebook models into the heart of their user experience, converging traditional BI with social networking, knowledge management, and collaboration architectures. Under this new “social BI” paradigm, vendors will provide information workers with tools for collecting vast pools of user-generated, subject-oriented, multimedia content, thereby supplementing and extending traditional data marts. By encouraging user-centric development of multimedia content stores, social media will accelerate the evolution of enterprise data warehouses into comprehensive “content warehouses.” By enabling applications to monitor and mine growing streams of social media content, the new generation of social BI platforms will accelerate the convergence of data mining, content analytics, and complex event processing. And this new BI platform paradigm will enable powerful social network analysis, sifting through continuing streams of transaction, behavioral, and sentiment data to identify influencers, net promoters, brand ambassadors, and other key relationships in online communities of all shapes and sizes.
Rob Karel: (Twitter: @rbkarel)
In the immediate term, social media won’t influence major shifts to the BI efforts of the majority of large organizations – many still struggle to simply implement and derive value from departmental historical data consolidation and reporting initiatives. As we see fairly often today, some organizations may choose to leverage text analytics to monitor and analyze customer sentiment based on comments from blogs, Twitter, Yelp, and other social media channels, along with sentiment analysis already being done from internally managed customer service and call center applications. But as social media continues to become the norm for how large organizations engage with their customers, partners, shareholders, etc., these channels will become even more strategically critical to include in their broader business intelligence strategy. In turn, the dependent integration architectures, information models, classification and taxonomy management exercises, and master data/information management initiatives will need to consider these channels as yet another source to be mapped and integrated into their analytic infrastructure.
Gene Leganza: (Twitter: @gleganza)
Corporate interest in mining social media information will be the catalyst that motivates organizations to finally get serious about including unstructured and semi-structured content into their information architectures. But the promise of dramatic business benefit from the evolutionary shifts in BI architectures that Jim discusses will encounter the ponderous pace of most large organizations’ data efforts that Rob describes.
When you ask people today about their information architecture programs, they’ll tell you their efforts are moving along for structured data and they intend to get to the content side “soon.” But they’ve been saying that for a couple years now, and it’s business demand for analyzing information from social media — at least to some extent spurred on by BI vendors’ trumpeting their new capabilities — that will finally make it happen.
Unfortunately, for many organizations, the drag-your-heels-then-deliver-ASAP scenario will cause the initial forays into social media-related BI to be the kind of siloed, one-off projects that provide some business benefit but don’t materially evolve the enterprise further down the path towards coherent information architectures. However, some enlightened organizations will utilize the projects to develop their processes around stewardship, metadata definition, and governance for the content side of their enterprise information. These are the organizations that we’ll be writing case studies about in a couple years to highlight best practices in information architecture practices.
Noel Yuhanna: (Twitter: @nyuhanna)
As social media adoption grows in organizations, there will be a strong need to extend information management frameworks, especially to overcome limitations from the use of existing technologies and architectures such as limitations found in relational models. The need to support a flexible data and information structure requirement will grow, including support for complex data relationship mapping and unpredictable performance requirements that would have to be approached differently. In addition, the need for dynamic provisioning, data discovery and classification, integration with new data types and structures, and integration with dynamic local and external sources would become critical to support these new initiatives. The need for real-time data quality, integration, and governance would become more important to support these new social media driven BI requirements. The next-generation social BI platform would have to be more flexible, faster to service dynamic user requests, support real-time data, and be more adaptable to changes. Although, architectural frameworks will gradually extend, enterprises should start looking at ways of how social media will be leveraged internally, and review gaps and limitations if any that would need to be addressed as and when such an initiative becomes of strategic importance.
Holger Kisker: (Twitter: @hkisker)
Social media today is still at a very early stage regarding the active usage for business purpose. Besides the many benefits as a collaborative communication tool, companies are beginning to understand that social media offers the possibility to tap into the ‘market opinion’ with significant sample size like never before. However, it requires intelligent business tools to turn the noise of the masses into valuable information that can be further processed.
Traditional BI tools won’t be sufficient to unfold the full potential of this new source of unstructured data, as simple analytics of buzz words will only scratch on the surface of this new gold mine of information. The combination of advanced text analytics to interpret complex context with predictive forecasts and simulations of customer behavior, for some business scenarios in near-real time processing, has the potential to catapult business intelligence to a new level of sophistication and quality. But this won’t happen overnight, as Rob and Gene mentioned. Companies interested in using social media and BI vendors that start to understand the new opportunity still have to learn their lessons to increase the business value of social media step by step.
We’d love to hear your thoughts on this intriguing topic. Share them here, and, if you’d like to hear more about this and other important BI questions, join the discussion on Twitter this Thursday. We’ll be using the #dmjam hashtag.
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