Close the modern analytics gap before your next transformative initiative

The pressure for transformation is rising. While analytics can help identify your next strategic opportunity, first you need to make data-driven decision-making the norm. Here’s why.

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Pressure increases for digital transformation and strategic alignment

The 2018 State of the CIO report from IDG found that while strategic goals for CIOs have increased across the board, functional duties have all but vanished. This leaves today’s leaders to perform challenging balancing acts in order to accomplish work that’s both transformational and operational.

Though the end game to transform, modernize, and provide ongoing value may be similar, the righteous path and its drivers of transformation greatly vary from company to company; what one organization needs to simply survive may help another thrive.

There is, however, a foundational approach that can help in navigating the challenges of nearly any transformational initiative — from choosing the right strategic pursuit and effectively managing change to continuously assessing ROI — and that’s with modern analytics.

Data-driven decision making informs more impactful transformations

Data can empower a deeper understanding of the business to help strategic leaders more accurately assess requirements, risk, and ROI of large-scale projects. Simultaneously, greater clarity into the nuances of the organization’s challenges enables a more targeted, data-driven approach to addressing the needs of the business, meaning strategic initiatives are less of a gamble.

Modern analytics can help steer the organization through change management as well, from identifying centralized goals to communicating a percent complete to stakeholders. Perhaps most importantly, analytics help measure the effectiveness of transformational projects on an ongoing basis — whether internal, like engagement or overhead costs, or metrics that analytics leaders have aligned to business goals, like the impact on revenue or customer retention.

As promising as modern analytics may be, a recent article from the Harvard Business Review explained that many organizations investing in artificial intelligence still lack foundational data-driven cultures. This can ultimately lead to failure, as trust in modern analytics will remain the main barrier to success. While many have deployed modern technology solutions that promise to address their data challenges, they lack that propensity to pull data into their everyday business, weaving data-driven decision-making into the behavioral fabric of the organization's operations.

This gap has persisted for a number of years, in spite of modern analytics’ high rank on the list of strategic investments that CIOs (or perhaps chief data or analytics officers) are making to derive more value from their data. And many of the sexy, high-risk, high-reward analytics trends — such as predictive modeling or IoT integrations that rely on AI — depend on a strong data foundation to be successful.

To close this gap, analytics leaders need to consider not only how to scale the technology, but the adequate support, training, and management structure to effectively navigate change management alongside adoption, overcoming the cultural roadblocks to a data-driven enterprise. Then they’ll see how insight begets innovation; a data-driven organization is better positioned to make shiny trends more useful and capitalize on more of the benefits of their transformational efforts, like modernization and market differentiation.

Closing the modern analytics gap is a transformation itself—for both the business and IT

Data-driven decision-making is immensely transformational when secure, trusted data is embraced everywhere from the top to the front lines. Supported by the right roles and resources, modern analytics can speed up time to insights and up-level individuals with valuable data literacy skills, encouraging faster, more intelligent decisions and less emotional bias in everyday work.

This transforms the role of IT in the analytics workflow, too. While IT retains a seemingly traditional duty of providing the business with a technology solution — from evaluation and deployment to security, governance, and ongoing management — the way in which the business actually gets answers from their data no longer burdens IT on a cycle of requirements and reports.

IT-enabled, business-led analytics allows IT to focus on how the data is ingested, stored, and distributed, giving the business freedom to ask their own questions through the access and exploration of governed, trusted, and secure data. For IT execs, this shift to enable a new analytics process leaves room for more focus on strategic goals and transformative work, helps manage the great balancing act, and encourages a better working relationship between IT and the business.

It’s important to remember that the analytical transformation is not all or none—nor is it ever done. Some data flowing through the analytics pipeline may need to remain in the traditional, centrally-managed, highly governed model, while more responsibilities to author and curate content are delegated to the business as their skills grow over time. It falls on analytics leaders to work closely with and influence cross-functional teams to understand how pains, needs, and strategic goals evolve as analytics continues to inform new insights and transformational initiatives.