Forget Business/Analytics Alignment: Your Data Strategy and Business Strategy Should Be One in the same.

The first step in any successful data democratization approach is the development and operationalization of a sound enterprise data strategy. At the upcoming AI & Data Democratization Live virtual event taking place April 27 - 28, 2021 Catherine Lopes, Head of Data Strategy & Analytics, ME Bank will share her approach to partnering with the business to do just that. 

Data not only helps companies make better day-to-day decisions, it also helps them pivot in times of crisis. By embracing data-driven sense-and-respond approaches, organizations are much better positioned to successfully tackle the volatility and rapidly respond to fast-changing scenarios.

The key to doing this goes beyond simply aligning data strategy with business strategy (which, alone is difficult to achieve). Instead your enterprise data and business strategies must be fully integrated. 

Developing an effective data strategy starts with identifying the unique strengths and weaknesses that exist within your data environment. “You quite often find that data analytics is really a glue that pulls together technology and ideas into one cohesive vision,” according to Catherine Lopes, Head of Data Strategy & Analytics, ME Bank. “If you want to help the business to achieve a certain goal and reverse engineer a solution, you have to understand the business.”

Companies that adopt a miopic, short-term approach whereby each team is off pursuing individual goals, open themselves up to risk. Instead, Lopes suggested co-created combined KPIs that drive long-term success while also delivering some quick wins. 

Identifying achievable and measurable goals that improve data access, sharing and strategic usage often organically wakes the business up to the value of data analytics. By allowing business users to experiment with data analytics and see how they can be used to enhance decision making first hand, they’ll start to more proactively incorporate data data strategy into their functional strategy and vice versa. 

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In addition, data strategies should always be holistic, encompassing the entire data analytics lifecycle, and highly agile. “The lifecycle of data analytics models or products is very different from that of a technology. You can’t just add a patch to update or fix it. It changes all the time from month to month with the data” Lopes explained. 

Simply look back on the past year. From January 31-May 1, 2020, organizations of all types probably witnessed unprecedented changes in their data streams. A massive shift in historical data can render your existing tools obsolete so having a service in place to continuously monitor and enhance model performance is paramount to long-term success. 

“Data science and data analytics is not art, but it is beautiful.” Lopes stated. Like a great work of art, data analytics mirrors the world around us while also beckoning us to look deeper and question the status quo. 

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