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How can we transition from treating BI as an IT project to treating BI as a cultural transformation?
What are some ways to best transition from treating BI as another IT project to treating BI as more of a cultural transformation?
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5 Answers
Until recently, BI has mostly derived from practices and technology that address structured data and business processes inside the firewall. BI will now have to include unstructured or content sources in analytics, to add in missing subtleties, context, and insight that cannot be pulled from structured data sources.
Some of the significant changes to analytics solutions relate to how final artifacts are “formatted”. There has been a growing transition from traditional report formats to interactive visualization tools, and to collaborative processes that enhance the final artifacts. More companies want BI to help with current and future needs and goals, rather than measuring the past. And these companies should be inviting in more and more individuals (via collaboration) to help refine the accuracy and contributions of analytics outputs. Newer on-demand BI / analytics solutions are providing “right now” intelligence, though still these are still works-in-progress, for the most part.
One newer area of interest for BI / analytics 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 strong basis to test, fine tune and filter the artifacts of analytics. Analytics can benefit greatly from human filters that bring experience, knowledge, creative thinking. Context has a big role here: context for sources, context for outcomes, context for usage with other data points to achieve the best Intelligence for “making better business decisions”.
Collaboration is far more than distributing and sharing documents. It is interactive, inclusive, non-silo’d.
If collaboration is to be part of analytics, then iterative collaborative processes should be established throughout analytics cycles. Collaboration for analytics means bringing in disparate people to test assumptions, validity of data sources, accuracy and relevance of the outputs. These additional participants should provide a wealth of experience, complementary concepts and other essential data and perspectives. Having to prove the relevance and accuracy of analytics results to sympathetic as well as less sympathetic individuals should strengthen BI / analytic processes and projects.
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. Social venues and collaboration can help track and capture outcomes of the decisions made based on BI / analytics:
While there are very interesting analytics solutions that are “friendly” to business users, it is essential that safeguards are in place to ensure that business users understand what they are doing with analytic models and whether the resulting “intelligence” artifacts are correct, meaningful and useful. Collaboration with others who know the data, understand analytics, visualize the big picture, and so on, is one safeguard to ensure reliable analytics outcomes and correct usages. In a particular enterprise, do enough people know what to do with analytics, both to start processes in meaningful ways and to audit outcomes to validate accuracy and relevance? Are users chasing the right problems or questions, providing the right data sources, including enough pieces? Do they have the understanding to work in a “big-picture” sense?
My goodness, this is THE question! Thanks, Peter. If BI is seen as another IT project, it will probably be almost immediately regarded as another mental masturbation invented by Engineers and similar geeks, I think that the key factor is fighting final users' fear, which is nearly always the main failure-leading reason. We (I mean we BI professionals) must never forget that there exist a lot of people who have gone a long way by using good sense, intuition and sometimes sheer dumb luck: all these persons are usually at least suspicious that their role might be limited, endangered or even canceled by computer based BI; as such, their normal, maybe inconsciuos reaction is sabotaging the project. So, we have to show them the enormous added value that BI puts in their everyday work, and to do that, we have to be humble, and ask, ask, ask, ask at the risk of getting unbearably annoying; explain the users that we're not there to get them fired, but to gove them the chance to make better decisions, integrating intuition and good sense with facts that can be correlated and analyzed to gain more knowledge of the business, and thus, so to speak, "business wisdom".
BI becomes culturally transformative when it is used throughout an organization in some way, shape, or form.
Many traditional BI implementations actually touch very few people in an organization - they go to the most senior executives or to ivory tower business analysts. So front line employees and middle managers may not even be aware that BI is in their organization, and they certainly aren't using it to make decisions or understand the state of the business.
When BI is actually available to multiple levels in the organization - front line employees get weekly reports critical to their part of the business, middle level managers can explore dashboards relevant to their quarterly and annual goals, for example -- the value of BI will become obvious. And it will become part of "how things get done."
I work for Birst, a SaaS BI company (www.birst.com) and we see the positive culture change when Bi becomes more accessible, easier to use, and part of everyday work. For example, at one Birst customer, the front line financial advisors use Birst a few times a week to determine which of their client base needs outreach from them. Each advisor could have hundreds of clients that they are advising, so determining which to speak with each week is critical to their success. With Birst, each advisor has a custom dashboard of their clients, which makes it easy to see which need retirement plan advice, 529 plan advice, etc. Since the dashboards make their jobs easier and more productive, they are an essential part of everyday business.
People are generally afraid of change. Using computer-based systems is much more accepted today then at anytime in the past, but all the concerns about being replaced by a machine, having your job de-skilled, etc. persist. Trying to sell BI as a cultural transformation may be even harder than selling it as an IT project. As part of an implementation plan for a specific data-driven decision support project to provide BI, we must look at change management strategies and consider how to get "buy in" from potential users.
Moving to fact-based decision making using a data-driven system is a major change for some managers, but many managers try to get and use facts now and are frustrated by the delays, poor data and lack of data.
Leadership matters! Any cultural shift must start with, be led by, and evaluated by leadershlip. What defines success will have to be changed to reflect the new data centric culture with success factors reflecting the proper use of data to drive competitive advantage. There must be a "business champion" who can help steer and drive the organization to use the data. Leaders must ask questions that can only be answered by analytics. The users of the data must be equipped to use the BI tools and interpretation of data. It is a way of doing business and that has to be instilled and lived by everyone. The data centric culture must permeate the entire organization so all must be educated to how data will be used to steer the organization, how it will be used to drive strategy, and how it will be used to define success of the strategy and resultant tactical steps. Not creating a data culture is why many BI initiatives have not flourished or even failed in many organizations.
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