C-level executives and managers can all agree with the notion that business intelligence is critical to strategic, data-first decision-making. It seems obvious: smarter is better.
Yet many struggle to quantify the performance of BI efforts. They wonder why their investment in technology systems and people hasn’t provided the performance boost they anticipated. They fear that changing market dynamics could destroy their viability without warning. Or they worry that they are leaving money and opportunity on the table every day, unnecessarily.
Executives want insight into their own BI programs, a real handle on the impact they’re making, and a way to focus them on their most pressing objectives. Like any important function, business analytics needs to be accountable and to demonstrate ROI and yet, there are major obstacles to optimizing the return on investment. Let’s explore these five obstacles to business intelligence performance.
Obstacle 1: The strategy and the outcomes aren’t aligned
Most BI initiatives fail because strategies and specific business outcomes do not drive them. And of the successful ones, many don’t realize the full extent of their potential value because the organizational culture, IT infrastructure, or both thwart the development of trustworthy, holistic, insights. Those, when put into action, can transform companies.
Obstacle 2: KPI confusion reigns
Sometimes companies can’t identify which key performance indicators to measure, or ID the wrong ones. In some cases, organizations manage to define appropriate KPIs but:
- The data used to support them isn’t accurate.
- They don’t monitor their evolution or changes.
They don’t take the time to review or update their KPIs as their business changes.
Obstacle 3: There are silos and shortcomings in IT
A multitude of issues can cause an IT department to struggle to integrate and deliver the most complete and relevant data for context and analysis. As a result, this leads to erroneous or incomplete operational and analytical data.
Sometimes, less-than-ideal data quality and Master Data Management (MDM) practices and technology architectures are in place, like: lack of an optimized data warehouse, the improper identification of data stewardship, and restriction of unstructured data integration with transactional data.
Or IT doesn’t address (or can’t adapt to) unanticipated events, like M&A activity, new competition, your company’s organic growth and market transformations, new sources of Big Data, new technologies for data collection and analysis, or migration to the cloud.
Obstacle 4: Corporate culture wars break out
Sometimes there’s reluctance—even intransigence—within the organization to embrace data analysis or BI as a strategic function. It’s impossible to challenge conventional wisdom and traditional ways of doing business. BI analysts can’t evangelize the concepts of a data-first culture among their executives, or even among information customers.
Data may not be seen as the most valuable asset, or – when it is – it’s used as a weapon to win turf battles or settle political disputes.
Obstacle 5: Companies are just too slow to adapt
A company’s slowness might not be because of lack or urgency, but rather by the inability to establish a repeatable business intelligence process and analytical models that can be continuously improved. It may also be because of an inflexibility that hinders adjusting or refining tactics in real-time, or by failing to understand and account for the dynamism and brief lifespans of BI strategies and data models.
How to Right the Ship
Despite these obstacles, your organization can show clear ROI from your business analytics function. Start by conceiving a practical and pragmatic three-step solution.
- Develop an information strategy. Business, IT and data analyst stakeholders should drive this strategy and tie it to business cases. Through collaboration, this multi-functional team can determine goals, identify KPIs, review architecture and technical infrastructure, and examine the benefits of a move to the cloud.
- Develop a data strategy to improve data quality. Implement a data governance strategy, review the sources, processes and technologies that enable you to integrate and analyze big data, and develop predictive analytics capabilities.
- Develop an implementation and lifecycle optimization strategy. Create a roadmap for implementation. Train your users to optimize BI tools and data. Develop a practical plan for corporate change management to drive a data-first, business-driven, customer-driven culture. Improve your agility and ability to pivot BI strategies to address emerging challenges and opportunities.
Companies should assess BI initiatives’ ROI in the context of both a strategic business vision and discrete initiatives that execute against that strategic vision that must leverage data to succeed. For example:
- Increasing top-line financial performance
- Identifying organic growth opportunities
- Increasing market penetration
- Reducing costs
- Eliminating fraud
- Achieving greater uplift on marketing performance.
After agreement on the specific business goals for your BI project, consider IT and data analytics strategies such as:
- Increasing the accessibility and integration of your data sources, to monetize your IT infrastructure’s impact to operational outcomes.
- Improving data management and reducing costs through cloud-based deployments, embedded BI, and dashboarding.
- Creating a sustainable blueprint to improve your analytics maturity level and realize a greater value and adoption in the execution of your roadmap.
- Operationalizing and monetizing the value of your data.
- Blending old-school business intelligence with new-world big data and cloud-based analytics to drive both their legacy and their emerging IT strategies with their business strategies.
Are any of these obstacles standing in the way of your BI initiatives? Do you need assistance in identifying and fixing any or all of these root causes? Can we help you design your own roadmap to better BI?
If so, click the button below and contact us today.