Overcoming Challenges in Scaling Data Analytics: Insights from Transformation Leaders

Lessons Learned from Real-World Experiences

The journey to scaling data analytics capabilities – the ability to general business value from data –? is fraught with challenges, as many digital transformation leaders can attest. Through their stories, we gain insights into the primary reasons that impede progress and how these obstacles can be overcome. Here, we share some of the most poignant tales that highlight three critical issues:

• A?lack of strategic alignment on data analytics and the priorities
• An ineffective data analytics process that does not deliver ROI
• A?deficit in non-technical skills

Lack of Strategic Alignment on Data Analytics Priorities

One transformation leader, Alex, works at a large state institution. The potential to monetize their data, through sales to external customers, justified the IT investment. For years, they struggled to align on their data analytics priorities. The executive team had different visions of how data should be utilized, leading to fragmented efforts. This misalignment led to duplicated efforts and ultimately, an over production of data that was not needed or used. Now they were paying the price. Less than 70% of their data was clean enough to migrate to the new infrastructure. The other 30% would need manual intervention. As a result, current opportunities to win new business were being lost to competitors who could come to the table with modern technology and clean data.

Lack of Time Due to Inefficient/Ineffective Process

Another digital transformation leader, Maria, described the data analytics processes that plagued her CPG company’s ability to achieve transformation goals. The company vision called for $1B in additional profit over the next several years. New product revenue and supply chain cost reduction topped the list of immediate opportunities.

Transformation project teams, including data experts, were launched with the expectation that Agile/Scrum would be used to define and iteratively deliver new digital products. However, Data Scientists struggled under the Scrum methodology and soon fell behind on sprint goals and commitments. Because there was a mandate to use Scrum, leaders and sponsors did not stop to address the problem. As a result, only one of five project teams met their MVP goals and release date. The rest took 80% longer than expected and quickly burned through $20+ million in investment with little to show.

Another transformation leader, Paul, was charged with delivering a large process automation platform to be used in manufacturing. Execution sponsors and key stakeholders were asked to prepare a list of data needs. An existing dashboard/data analytics project request fulfillment process would be used to deliver those needs. After several months, less than 20% of requests were completed. The other 80% never made it into production because of delays and bottlenecks, or the requests that were no longer of value to the stakeholder.

The Human Skills Deficit

John’s story about digital transformation at a major airline, highlights the challenge of recognizing the right combination of human skills that are needed for real transformation to be successful. John’s transformation team installed a new data visualization application. After several? months, leaders began using the new set of dashboards in their MBR. As they did, executives became frustrated. The data did not give them the insights needed to avoid cost and productivity overruns. Decisions were still too reactive.

Although the data analyses and visualizations were technically sound, they lacked relevance to business decisions. Insights were not actionable. Moreover, they did not learn this until the MBR, after months of work on the dashboards. The next iteration of dashboards was only marginally better because skill deficits in critical thinking, collaboration, and stakeholder management were not recognized.

Lessons Learned and Stop Gaps

The experiences of these digital transformation leaders offer 3 valuable lessons for pilots and for scaling data analytics capabilities.

1. The temptation to go for quick wins...

The full article explains these 3 lessons and breaks them down into actionable steps you can take today for scaling your data capabilities.

Read the full article on the Aryng blog.