Data analytics moves beyond Dilbert

Sept. 11, 2019
Why pharma manufacturers shouldn’t get stuck in a comic

Until recently, data analytics has been in a realm of manufacturing only occupied by the mysterious engineers who graced the colorful squares of Dilbert comics. A world where management is clueless, timelines are open-ended, the engineering department is an impenetrable black box and IT resists all change. While it can be frustrating to work in a corporate structure where the value of data isn’t recognized, or where technology solutions may not be properly funded, there is hope.

In modern manufacturing, data collection and analysis are the keys to the kingdom, and the technology is available to unlock business value. Understandably, deciding to invest even more money, time and resources into the world of big data can be daunting. However, when a passion for creating real change in an organization combines with data analytics technology, therein lies great opportunity for impact.

Painting a picture for decision makers that clearly shows what success looks like is imperative. Hesitation to act, due to either technical reasons or personal fear of failure, can hinder taking the steps required to effect change. However, with a clear vision, strategy and shift in cultural values, it is possible to establish and execute a winning business case for investment in advanced analytics. This requires showing everyone involved what’s in it for them, and how investments will deliver measurable success.

In my experience, the most frustrating, destructive and unfortunately, pervasive attitude is that there is no time for change, but there is plenty of time to slog away in complex spreadsheets that don’t provide insights quickly and hinder the ability to share findings effectively. Instead, we must be clever and employ an agile approach with phases that support early wins.These phases are discovery and launch, extended engagement and strategic alignment.

In the discovery and launch phase, business initiatives and key performance indicators are identified. Workflows are redefined as required with an understanding of desired short-term outcomes, and a shared set of terms is created to ensure broad understanding while avoiding unnecessary jargon. This first step can be difficult and is often met with significant resistance. Overcoming this inertia requires one to:

• Align data capture strategy, process analytical technology and analytics goals.
• Empower Subject Matter Experts (SMEs) with advanced analytics tools designed to work with process manufacturing data. Technologies that couple the intuition and experiences of SMEs with the power of machine learning will provide the greatest impact.
• Remove technology and workflow barriers to facilitate access to a data-rich environment for individuals and teams.

In the extended engagement phase, a successful strategy will target opportunities to unite teams. Since departments are often mired down in their own protocols, it is important to:

• Provide clear demonstration of how the strategy and advanced analytics solution benefits groups working together.
• Define actions required to shape and encourage a culture of engagement and risk-taking.
• Identify leaders who can help drive these cultural shifts.

In the strategic alignment phase, it is time to solidify the commitment to the broader digital transformation strategy. Having employed an agile approach, the early results should have quickly demonstrated value. This phase focuses on securing the additional emotional and financial investments required to further drive growth and profitability by:

• Identifying new value-added workflows.
• Evaluating new opportunities for cross-site decision making.
• Asking and answering more strategic, complex and high-level business questions.

Instead of remaining stuck in the middle of a Dilbert comic, why not spend time and energy driving towards a winning business case for investment in advanced analytics? Embrace the risk. Extract the value. Savor the results. 

About the Author

Lisa J. Graham | Ph.D.