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Lowering the Barrier of Entry: Developing a Data Strategy at Scale
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Lowering the Barrier of Entry: Developing a Data Strategy at Scale

“Data isn’t usually treated like an asset. It’s treated like trash, like a waste product.”

Such is the frank appraisal of Walid Mehanna, Group Data Officer at MERCK KGaA Darmstadt Germany, regarding the value most organizations place on their information assets. Needless to say, Walid considers this dismissive attitude to data analytics to be a serious problem, because the future success of many organizations depends on the adoption of an efficient, data-driven approach. So how can organizations, and especially large corporations like MERCK–a multinational science and technology company with over 600,000 employees–establish a sustainable and effective data strategy at scale if data is commonly treated like “trash”? Walid’s advice has the ring of hard-won wisdom: an effective data strategy must lower the barrier of entry into the use of data and raise an organization’s ability to create solutions by leveraging data.

Data’s False Dichotomy

When Walid took over at MERCK, he began by instituting a fully-immersive, 10-week  data analytics educational program for those in the organization who reported high interest and significant space in their schedule. Participants in the program took a Workera assessment before, during, and after their 100 hours of training and mentorship in order to benchmark growth–unsurprisingly, the results revealed how effective an immersion program can be.

Of course, ideally, everyone in an organization would be able to spend months becoming an expert data analytics practitioner. However, few companies have the time and resources to carry out a data transformation in this way. Many company leaders, therefore, feel they must approach their data strategy in one of two less-than-ideal ways–either everyone in the company will develop their data skills on an ad hoc basis, or a few individuals, by participating in an immersion program like the one mentioned above, can become experts who will carry the data analytic load for the entire organization.

The Optimal Third Option: Create a Data Ecosystem

During his time at MERCK, Walid has done his best to destroy false dichotomies of this kind. While continuing to offer an abbreviated (aka more efficient) immersion program, he also began to develop Optimize, a data ecosystem that seeks to make data more accessible to the organization as a whole. Walid's goal for Optimize was to enable people across the company to create data-driven solutions by removing many difficult tasks that often become obstacles, such as identifying data, begging for access, testing models, and combining data. By providing a catalog of the data analytic skills across the organization and introducing technologies that allow individuals to access data more autonomously, Optimize seeks to lower the barrier of entry into the use of data in hopes of creating a company culture that prizes data-driven solutions.

“Unfortunately, technology can be easily misunderstood…that it’s the silver bullet. It is not. Technology is useful and it’s helpful, but it’s always the question how you leverage it, how you make it really count for the people.” Walid Mehanna

For Walid, making data accessible and upskilling an organization’s personnel are simply two “levers” aimed at the same goal–that is, to use data to solve business problems. Making data accessible lowers the barrier of entry for everyone in an organization, while targeted upskilling allows individuals to make more effective use of the data at their fingertips.

Key Takeaways

  • Making data accessible and upskilling both aim at the same goal, to encourage the use of data to solve an organization’s problems
  • An effective data strategy makes a compelling offer on behalf of creating data-driven solutions by removing the foundational efforts that often deter those interested in working with data, such as identifying data, begging for access, testing models, and combining data.
  • Immersion programs are an effective way to develop data analysis practitioners, but must be augmented by scalable data strategies, such as offering resources for ongoing upskilling and developing a data ecosystem.


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