Here’s the next article in the series by Julie Hunt, an accomplished software industry analyst.
Intelligence, People and Process
Master data management (MDM) is a good reflection of how an enterprise uses data, information – and content – for business purposes. The creation and management of master data touches more than the information itself. By creating data repositories and processes that reflect business functions, an enterprise should develop access to the information that is so crucial to effectively and efficiently achieving its business goals. It’s extremely important to analyze, manage and provide access to all forms of information – structured and semi-structured data, as well as “unstructured” content.
For BI and analytics solutions to provide real intelligence, they must be based on accurate and timely data. BI outputs are based on data aggregations, until recently, from structured data in data warehouses, with delivery in the form of reports or dashboards to enterprise users. Analytics add the dimension of mathematics and formulas, with variants addressing forecasting and prediction.
Neil Raden responds to the ebizQ question: How does master data management change BI?:
…to describe how organizations collect data as part of a process, but manage to make it infinitely more valuable by using it for other purposes.
What does this have to do with BI and MDM? In our research, we have found that most knowledge workers shun BI for two reasons (not performance or ease-of-use): relevance and understanding. MDM adds nothing to address these concerns because its representational framework, a relational schema, is inadequate. It’s a representation of a model. An ontology is a model. Until MDM rejects the relational model as its underlying schema, it will be unable to add the rich meaning, relationships and even reasoning that an ontology can do.
So, the point is, if you’re going to go to the massive effort and expense of an MDM solution, take some advice from 21 years ago… Make the data useful for people, not just governors and black belts.
One newer area of interest for BI and analytic solutions is the inclusion of collaborative activities to add contextual and qualitative layers to the output of BI processes. To achieve authentic intelligence, contextual / qualitative layers can provide a strong basis to test, fine tune and filter the artifacts of analytics.
Analytics can benefit greatly from human filters that bring experience, knowledge and creative thinking. Context has a big role here: context for sources, context for outcomes, context for usage with other data points, to achieve the optimal intelligence for “making better business decisions”.
The possibilities for new applications of analytics increase with collaboration. Inviting in many-to-many interactions also opens up processes to new ideas from participants. Gartner found that social venues and collaboration help to track and capture outcomes of the decisions made based on BI / analytics:
Gartner’s user surveys show that improved decision making is the key driver of BI purchases. However, most BI deployments emphasize information delivery and analysis to support fact-based decision making, but fail to link BI content with the decision itself, the decision outcome, or with the related collaboration and other decision inputs. This makes it impossible to capture decision-making best practices. Solutions are emerging that tie BI with social software and collaborative tools for higher-quality, more transparent decisions that will increase the value derived from BI applications.
With convergence, employees in the enterprise should operate more effectively, where improved data governance/MDM lead to better BI, where collaborative processes also enhance and validate BI, where a collaboration setting for BPM works to deliver more of the information that the enterprise needs.
A possible approach to marrying collaboration, data and intelligence to business processes, and, more importantly, to the way people work, can be seen in Tibco’s re-working of tibbr. Dennis Howlett provides this description of tibbr:
It intelligently marries people, process and context, delivering information the way people want to consume
Tibco connects tibbr to business processes and event-triggering that are then exposed in tibbr for taking action. While tibbr is built more for real-time information streams than archives of content and information, tibbr and Tibco have created a platform with a lot of potential for improving overall information findability that adheres to context and worker roles.
It connects to process, people/workers, collaboration venues, data and information streams, centralizing all event streams into one dashboard. Information can be organized by subject or topic, rather than by people. tibbr enables users to create, contribute to, and subscribe to the real-time event streams that matter most to them.
About the author: Julie Hunt is an accomplished software industry analyst, providing strategic market and competitive insights. Her 20+ years as a software professional range from the technical side to customer-centric work in solutions consulting, sales and marketing. Julie shares her take on the software industry via her blog Highly Competitive and on Twitter: @juliebhunt. For more information: Julie Hunt Consulting – Strategic Product & Market Intelligence Services.