Why culture is the key to driving digital success.

Despite how far data collection has come, 70%of digital transformations fail. 

In the Harvard Business Review article “What Does It Actually Take to Build a Data-DrivenCulture?” by Mai B. AlOwaish and Thomas C. Redman the authors present key takeaways from a case study on an important topic – digital transformation.

If Peter Drucker is right that culture eats strategy for breakfast, then perhaps there are some nuggets in this case study that can help the rest of us map out what a culture capable of transformation looks like. 

Data Quality First 

The author notes how the CEO suggested the transformation focus on data quality before anything else. In the words of the author, “Nothing is more basic than quality. Bad data is the norm.” There’s a reason data scientists laugh (and cry) when they see memes that joke about how 50-80% of data scientist’s time is spent on data wrangling and data cleansing. They laugh (and cry) because, sadly it's true. Bad data (and bad data architecture) increases technology costs, reduces the value of the data stored, slows decision making, and results in poor business outcomes. 

This is why we at Kognitiv invest equally in new product development and critical foundation layers, such as ourData Hub and Kognition AI/ML Decision Engine. To engage customers and prospects with one-to-one experiences that drive business growth, clean and well-organized data is the most important raw material. 

People & Culture 

The case study is a feature for talent acquisition, empowerment, and recognition.The transformation started when new blood (in the form of a Chief Data & Innovation Officer) was brought into the company. Without new blood bringing in new ideas and setting new expectations, organizations will struggle to grow and change as quickly as they need to. In addition to bringing in new talent, the case highlights the central role played by the company’s people and culture team to make the data-led initiatives interesting, rewarding and fun by facilitating trainings and workshops, recognizing ambassadors, and building awareness with internal branding. This fosters buy-in, collaboration and encouragement company-wide.  

Our journey at Kognitiv has mirrored this. We’ve hired new and incredible talent in data science, data visualization, machine learning, data engineering and other fields to build our vision of omni-channel and omni-audience loyalty and, we’ve given these innovators a more prominent role and prioritized support in the company. 

Bring Everyone Along 

The case study mentions that after the authors created an ambassador program to evangelize the data-led initiatives they further developed a “Data 101 Program” to help everyone in the company understand how they are data creators or data customers and why data matters to them. This expanded awareness created an environment for collaboration and innovation that propelled the business forward. If transformation starts with new talent and a clear vision (phase 1), expands to a core team of ambassadors to innovate, incubate, and educate (phase 2), then bringing everyone along with early, consistent, and frequent messaging is a critical third phase to maximize contribution from the entire organization.  

After restructuring our leadership at Kognitiv and bringing in new talent in key roles we incubated many of them in a new Data + Algorithms team. The team’s focus on data-driven innovation quickly expanded to the broader Technology organization, and now the initiative is being driven more broadly across Commercial and Marketing. Education and incubation one step at a time.  

One Missing Piece  

One piece missing in the case, is any commentary on how to establish a rhythm of innovation. Whether a company is catching up or innovating at the forefront, velocity is key. Whether it’s process improvements or software releases, how teams define, design, develop, test, deploy, review, and repeat (and track performance against each step) will dictate transformation success and contribution to a company’s bottom line. Without establishing a regular rhythm of innovation, even companies that successfully transform might find themselves falling behind again.