In my last article, I argued for a case about democratizing data science. The article resonated with many people. And I was asked this follow up question — it is great to imagine a future where data science is accessible by everyone, but starting from today, what could I do in my company to initiate the change?
This is a common question we often receive from our customers. We are seeing more and more consumer insight managers, strategists, or HR specialists asked by their leadership team to incorporate data into their decision making.
Over the years, we have helped many companies to build their data driven culture. While there are many different paths that lead to the success, the failures appear more or less the same: a data strategy championed by an executive from the highest level is imposed to the middle management team. The middle management reluctantly initiate a few projects and implement some changes, but in a year to 18 months, the strategy was never mentioned again.
A top down strategy doesn’t work. To initiate the culture change, you have to start from the bottom. And here I summarized a few steps we learnt from our customers that could help you start building the data driven culture from the position you are in today.
Step 1: Demystify data science. Data Science, Machine Learning, Artificial Intelligence, those are hyped words that are overly used . These words don’t mean anything in isolation of a concrete business problem. So let’s stop talking about technology, but focus on the business problem the company is facing. Are you looking to optimize your pricing? Do you need to improve your employee engagement? Once you define the problems, you can then pick the right data science techniques to solve them. The world of data science seems confusing. There are so many “cheat sheets”, “modelling techniques” you can find on the internet. But just like you don’t need to know thermodynamics to drive a car, you don’t need to understand specific data science techniques to solve a business problem. Data science techniques are commodity nowadays, and you can access them easily through many tools and platforms. So instead of focusing on data science, focus on the right problem to solve, and pick a product or tool that does the job for you.
Step 2: Start your experiment with a tangible use case. If you want to initiate a change in your organization, start small. Focus on an area where you don’t have to spend a lot of effort to collect the data, and the contrast of using data vs. not makes a huge different in the decision. In your first experiment, it is unlikely you can start tackling the most difficult problem of your organization simply because people will hesitate to give you access to the data, but establishing a quick win with high visibility can help you gain your credibility. We have seem customers choose to analyze the Net Promoter Score survey with NLP, or do a diversity study based on employee data. In these cases, you can gather the data relatively easily. In addition, as customer service and HR are not the typical departments that use data analytics, the potential impact you can generate is high.
Step 3: Repeat your analysis on a bigger problem, involving a bigger team. If you are successful in your first experiment, then follow up by taking on a bigger problem. Probably by this time you can find a use case that is more strategic to your organization. For example, predict which customer will churn, or create a different price scheme based on customer segmentation. What is critical in this process is to involve a bigger team representing both business and technology, and both users and managers. Initiating a change is difficult. It is better to take a group of people with you than arriving at the result by your own. Encourage people to take on ownership and contribute to your problem solving, hopefully they will become champions of the culture change in their own way.
Step 4: Follow through to implement your decision. Your job doesn’t end with delivering a report. Unless you follow through and implement your recommendation, data analytics alone will never make a tangible impact. So if your recommendation is to target a group of customers with a new promotion, make sure it is implemented, measure the result to understand if your analysis made a difference, and use the result to convince your leadership team.
Step 5: Now tackle the bigger problem of infrastructure. If you have managed to complete all the steps above, congratulations, you have done a great job influencing your organization. But the road to culture change is still long. To do this properly, you need to build a supporting infrastructure of data collection, data cleaning and data integration to support your ongoing analysis. But hopefully by this time, you have enough evidence to build a business case to get buy in from the key stakeholders.
Step 6: Build a network of supporters. Expand your network of supporters inside and outside of your organization. Reach out to thought leaders and influencers in your community, and customers who benefited from your project. Continue to work with them to articulate the value of the culture change you are delivering. Remember the transformation could be a multi year effort. So having a network of supporters who can vouch for your project will help you go through this journey.
I hope you enjoyed reading this article. I know these advices may still sound vague to you, so here are a few articles where I talked about specific use cases and best practices in the area of Market Research, People Analytics, Customer Insights so you can continue the reading!
