Three Parts to an “Early Win” for Your Data Science Team

Science is the process of curiosity.  It doesn’t care about profits, margins, timelines, or shareholders.  In science, failure (learning) is an acceptable outcome.  In business, however, companies invest in data science with an expectation of ROI.  In order to obtain the corporate “buy-in” for more resources and support, new data science programs scramble to find results worth shouting from the rooftops – or at least highlighting at the next board meeting.  We crave the hole-in-one even though it's our first time golfing. While you can’t be guaranteed immediate success, there are three ways to pick an early project that supports the value of your initiatives and helps you gain momentum with important stakeholders.

Access Trusted Data Sources

 You may have data coming in from an overwhelming amount of siloed data sources. Choosing the right data to kick off your project is an important step. Start by identifying the sources both IT and the business already have access to and trust.  If it will take IT a long time to provide access, you will lose momentum early in the project. If the data sources have missing or user-entered data, you may spend too much time cleaning it and less time on complex analysis.  Make sure the business has established clear definitions for the data (ex. defining a product, company, or target customer) to avoid controversial topics.

Questions to ask to ensure you have access to trusted data:

• Which sources have database diagrams already created?  
• Are there a few business subject matter experts that truly understand the data?  
• Can you easily access the data for analysis?  
• Can you easily tie the data to other trusted reports?
• Are there standard definitions for the data being used?

Find a Project With Measurable Value

The data request list may be overflowing, but use this flagship project to solve a problem with measurable impact.  Will a larger value for a small number of users be more impactful than a little bit of value to many? It depends on how measurable the results are.  Saving 100 hours or $10,000 is not only easy to explain but will grab attention! If your organization is revenue-focused, prioritize projects with measurable revenue increases, even if just for one department.  While a flashy new dashboard might be nice for the salespeople, will it actually increase their sales? If so, how will you measure the sales increase in a way that can be communicated clearly through the data?

Questions to ask to ensure the project has measurable value:

• Will this insight increase revenue? How?
• Will this solution create time savings? How much?
• If this information will unearth great conversations, how will you measure the value of those additional insights?
• How often will it be used once complete?
• How many users will be able to use this information?

Use Your Results to Support Positive Action

Using the data to allow leadership to engage with the business in a way that guides, empowers, and positively propels your business forward will be the ultimate “win”. For example, leadership may be excited about increased transparency into performance metrics previously muddled by the time it reaches them. They may request the ability to drill into which regions, salespeople, and products are excelling and use this information to learn and inform, benefitting the entire organization.

Communicate clearly with leadership in the early stages of your project. Set clear expectations and agreement on the intent of the project and the goals it is meant to achieve. Without this consensus, a project could be used to impact negative consequences such as layoffs or budget cuts or cause people to discredit the data, process, tools, and solution. Aimed toward a positive purpose and fueled with clearer information that supports relevant business outcomes, your organization can accelerate its progress and move the needle confidently.

Questions to ask to ensure the project will result in positivity for your organization:

• What action will be taken with this information?
• Will the action based on these results be celebrated openly?
• What incentive do end users have to provide this information?
• Who will be excited about the results?
• Who will be apprehensive of the results?

Introducing data science into any organization takes time. Identifying an early project that can be proudly showcased to leadership and throughout the organization will expedite this process. You will quickly go from trying to convince others of its value to needing to prioritize a large list of data project requests!  If you select a project where you have access to trusted data, measurable value, and results that create positive action, you will be on track to building a successful data science practice.

To learn more about building a data science or analytics practice, a data project backlog, or how to prioritize your projects, visit our Data Advisory services page.  We meet companies where you are in your analytics journey and help you get wins no matter where you are in the process.