How To Lay The Groundwork For Data Science Adoption In The Enterprise

Data science is hard. It's a lot harder than it looks, and it's not just about building models. In fact, building those models is actually one of the least important parts of data science. Successful teams focus on the outcomes they want to achieve, from both a business perspective and a technical perspective. They also focus on building an environment (and culture) where everyone can talk openly about what's working, what isn't working—and how we can improve things for everyone involved in our company's data science initiatives.

Understand Business Outcomes

Data science is a powerful tool, but it can only be as good as the insights it generates. The most important step to ensure that data science delivers on its potential is therefore to identify the business outcomes that are most relevant for your organization and make sure that these are reflected in every data science project you take on.

A business outcome is any goal that helps an organization grow or improve its performance over time. Examples of business outcomes include increasing sales, reducing cost, improving customer satisfaction and loyalty, generating more revenue from existing customers or the acquisition of new ones (market share), increasing retention rates and decreasing churn rates (retention).

The identification process often begins with asking yourself: “What do I want my customers/employees/fans/stakeholders to do?” This may lead you to think about how they might behave differently if they had better information at their disposal. For instance: If we could predict which customers would churn next month based on their current usage patterns then maybe we could send them an offer before they cancel and improve our retention rate by x percent. Or if we had a better understanding of our competitors' financial performance then perhaps, we could manage our spending better so that it won't be too difficult when they announce new products later this year.

First things first, if you don't have a solid infrastructure to house the data that is needed to apply such data science concepts then you need to make the needed investments prior to even mentioning data science.

Data Science Roster Buildout

Data science teams should be cross-functional, including data scientists with different backgrounds and skills. A successful team should have a strong business focus, technical focus and leadership focus.

A well-rounded team will include people who understand both the business and technology sides of your organization's data challenges. This means that they'll be able to set up a solution that's effective for your business while being able to implement it effectively in your organization's infrastructure as well.

Create a Data Science Team

In most enterprises, there is a significant gap between IT and business teams. This can be a hindrance when it comes to adopting data science in the enterprise because you’re trying to get different groups of people on the same page about how data science could help them achieve their goals. They may be using different terminologies or operating by different rules for what is considered good practice in their area of expertise (data scientists might consider certain practices as “dirty” but are accepted as standard practice in other organizations).

You need to bridge this gap if you want your organization's employees to collaborate effectively with one another—and also if you want users from both sides of your organization's divide (those who understand technical tasks vs those who understand non-technical tasks) to work together productively on projects related to data science adoption.

Start Small, Then Scale Up

The most important thing to do is start small. Data science is a relatively new discipline, and the methods for performing data science are constantly evolving. It's very easy to get carried away with grandiose ideas of what your team can accomplish from day one. However, we've found that the best way to gain buy-in from management, as well as get everyone on board with your vision (including yourself), is by starting small and building momentum over time.

Start with an idea that you feel confident will be a success off the bat -- something you're excited about but won't take too long or cost too much money to implement successfully; then scale up once you see how well things are going. You don't want to bite off more than you can chew when it comes time for implementation; otherwise, there could be significant consequences down the road if things don't run smoothly during execution phase due lack adequate planning beforehand."

The most successful data science teams don't just build cool models—they also focus on business outcomes, are supported by the right infrastructure and data assets, and have strong relationships with business stakeholders.

The first step in setting up your enterprise-wide data science program is to understand what you hope to achieve. Are you trying to improve customer retention? Increase sales conversions? Optimize supply chains? Understand which metrics are important for these outcomes, then consider how a data scientist can be part of achieving them.

Next, assess whether the existing people on your team—or those who might be hired in the future—are equipped with both technical skills (for example: Python proficiency) as well as domain knowledge (for example: understanding financial services).

If not, consider making adjustments so that there's enough capacity for exploring new methods and applications within each department/function/business unit where they're needed most at this stage of development."

Conclusion

Data science is a powerful tool, but its success depends on more than just the latest tools and techniques. It's also important to have a clear understanding of business outcomes, create an environment where data science teams can thrive, and cultivate strong relationships between technical and non-technical teams. If you're looking to build out your own team or improve on what you already have in place, these four steps will help get you started.

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