4 Imperatives For Making Business Intelligence Work

By Michael Li, Greg Steffine February 27, 2019

RESULTS-BASED LEADERS RELY ON HAVING the right information at the right time in order to support operational decision-making. That’s why decision-makers consider business intelligence their top technology priority. They recognize the instrumental role data plays in creating value and see information as the lifeblood of the organization. They then use actionable insights to confidently and consistently lead by delivering results that count.

The business intelligence (BI) and data science industries have spent the last couple decades making data access easier, analytic capability more comprehensive and platforms more scalable. Yet, despite pouring billions of dollars into BI initiatives, executives often come up emptyhanded when they reach for the information they need to make wellinformed decisions. Executives fail to fully capitalize on BI’s promise of turning actionable insights into real business value when BI efforts aren’t planned or executed effectively. These problems are further compounded as companies move to adopt more sophisticated data science and AI. To achieve the results that leaders are looking for, organizations must create a coherent BI strategy that aligns data collection and analytics with the general business strategy.

Our experience shows that by focusing on four actionable steps, or imperatives, we can empower business leaders to adequately address planning and execution challenges to build a decision-support competency that works.

Step 1: Unify

What we believe influences how we behave, and unifying your organization begins with aligning many unique and often divergent perspectives across different business divisions on business intelligence and analytics. Senior leaders across an organization must collaborate efficiently for BI to be successful.

All too often, requests for information from the business go unanswered, as different siloed departments trip over themselves to coordinate interdepartmental cooperation. Technical nuances around data and data wrangling are often misunderstood and miscommunicated because practitioners routinely fail to understand key business requirements. Business leaders need to look for data science candidates with keen technical, analytic and business acumen to unify their BI efforts between technical and non-technical parts of the business.

Business intelligence is a business initiative, not a tech project. It’s an ongoing effort across an entire organization to improve its decision-making ability to create and maximize value. There is no finish line. Adopting this attitude across every business division in your organization is a prerequisite for effective collaboration and a necessity for creating the kind of cross-functional alignment needed for BI success.

Step 2: Simplify

Complexity is wreaking havoc on businesses and making it increasingly difficult for decision-makers to create value. Analytics works best when the process of moving from great idea to actionable insight is fast, focused and uncomplicated.

To simplify your BI efforts, start by building key alliances with critical stakeholders in different lines of business within your organization. Now more than ever, CEOs rely on CIOs and CDOs to drive an organization’s value-creation agenda, and that makes effective collaboration between business and IT absolutely critical to BI success. The days of an ivory-towered BI detached from real business operations are over. It is vital that business leaders work overtime to bridge the all-too-common communication, trust and understanding gaps.

Then, secure executive buy-in and the financial resources you need for your efforts by building your capability one incremental step at a time and demonstrating real value every step of the way. When building your BI capability, always start with the existing technology you already have. Most organizations have already made significant investments in tools and infrastructure and have built important intellectual capital that only comes with experience and time. Prove that it can’t or won’t work before requesting additional funds for new technology.

Finally, when it comes to providing decision-makers with the information they need to do their jobs, minimizing time-to-results is critical. This means striking the right balance between governing and enabling the business to perform without hindering innovation and creativity.

"To simplify your BI efforts, start by building key alliances with critical stakeholders in different lines of business."

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Step 3: Amplify

Skeptics and naysayers exist in every organization. They prefer the status quo, resist change, and make comments like, “We’ve been down this road before,” and “I’ll believe it when I see it.” At best, they’re stubborn demanders of proof willing to believe only when presented with concrete results. At worst, they’re obstructionists—preventing BI initiatives from realizing their full potential.

As BI evolves from traditional reporting and descriptive analytics toward data science and AI, many practitioners fear that new capabilities will make their skill sets obsolete. Fighting new initiatives is, perhaps, a natural preservation instinct. The prevalence of naysayers may also be symptomatic of cultural biases in the institution. Deloitte refers to it as the “inertia of good intentions”—personal behavior created by institutional routines, obligations and pressures that actually hold many back (unsuspectingly) from delivering the kind of value their organizations need. Left unattended, the culture of most organizations can marginalize BI initiatives to the point of limited and unacceptable return.

You can avoid the negative impact of skeptics and naysayers as well as a culture of mistrust by establishing organizational awareness and building excitement around BI, analytics and data science initiatives. To amplify means to evangelize.

For instance, large enterprises often create a Data Science Competency Center or AI Center of Excellence, which helps lead the effort to modernize analytics. These evangelists define the data science and AI practices for the firm and are responsible for elevating the general analytical skill level of the entire organization. Fortune 500 Data Science Centers of Excellence are hosting in-depth trainings in data and AI to help bridge the skills gap between the advanced data science practitioners of their organizations and the typical rank-and-file analysts.

Step 4: Qualify

Business intelligence is a journey—a process of continuous improvement meant to adapt and evolve so that business leaders can give intelligent responses to an ever-changing and dynamic business environment. After all, what decision-makers need to monitor and evaluate the business today will change tomorrow. The only way for a business to keep pace is for its reporting and analytics capabilities to keep pace as well.

Today, few firms qualify success properly. They don’t proactively monitor and measure BI performance against end-user expectations and real business outcomes, so they can’t effectively evolve.

Ensure that you focus adequate attention on active monitoring, evaluation and adjustment of your organization’s BI capabilities so they’re always aligned with the business’ needs and always responsive to stakeholder expectations.

As companies are looking toward growing their BI, analytics and data science departments, management is demanding results. All too often, analytics projects fall short because leaders fail to understand the key elements of a successful analytics strategy while creating one. In order to plan and execute successful BI efforts, leaders in this area must adopt these imperatives. By focusing your organization’s BI initiative around simplifying, unifying, amplifying and qualifying business intelligence within the whole organization, you’ll be able to make smarter business decisions, deliver successful results and keep your firm ahead of the competition.

This article was originally published on oreilly.com

Michael Li

Michael Li

Michael Li is the founder of The Data Incubator (now part of Pragmatic Institute), a data science training and placement firm. As a data scientist, he has worked at NASA, Foursquare and Andreessen Horowitz. Michael is a regular contributor to VentureBeat, The Next Web and Harvard Business Review. He earned a master’s degree from Cambridge and a Ph.D. from Princeton.

Greg Steffine

Greg Steffine

Greg Steffine leads the Business Intelligence & Analytics Competency Center for KeyBank, one of the nation’s largest bank-based financial services companies. He is the author of the award-winning book Hyper: Changing the Way You Think About, Plan, and Execute Business Intelligence for Real Results, Real Fast! Greg’s new book on self-service analytics, Think Fast!, was just released.

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