3 Roles that Data Professionals Play to Bridge the Gap Between Data and Business

In the world of data analytics, there are 3 crucial roles that data professionals play to bridge the gap between data and business.

1 - Data Translation and Interpretation

The ability to translate business context and impact to technical requirements,  interpret analytics output, extract business insights, and suggest action plans is a necessary and powerful skill to harness. 

As organizations collect and store a vast amount of data, it’s up to mostly analysts and data scientists to make sense of this information and communicate its significance and implications to business stakeholders. 

Some datasets, models, and analyses are more complex than others. Data professionals have to develop strong communication and storytelling skills to effectively translate technical concepts, statistical results, and data visualizations into business language. They play a vital part in enabling data-driven decision-making and tying analytical insights to real-world business strategy and outcome, so it’s no longer just analyses on paper, but tangible actions and results. 

[Story Time]

When I was working for a marketing agency, one of the deliverables for our client was to evaluate the effectiveness of the loyalty program and identify the drivers of incrementality using advanced analytics. Some metrics were easy to interpret, like redemption rate, repeat purchase rate, average basket size, and retention rate. However, the output from the regression model was not easy for business stakeholders to fully understand although it was considered as a relatively basic machine learning model. 

Initially, we tried to explain the model in detail, walking through the technical aspects and highlighting why it was a robust analysis, hoping that the audience would appreciate and understand the “power and beauty” of data science. Eventually, we realized that was the wrong approach. Instead, we shifted the focus to explaining what the model did, what the results meant for the business, and what needed to be done based on the insights instead of just laying out the figures and facts. 

There’s a powerful framework I learned from that job to translate and interpret data insights: What, So What, Now What. It’s about gathering the data, understanding the meaning of it, and turning insight into action. This helped the stakeholders understand the true value of the analysis and feel confident in using the insights to drive strategic decisions.

2 - Requirement Gathering and Analysis

Once the organization starts to see the value of data analytics, the requests to leverage data assets to drive strategic decision-making will increase quickly from various business areas. However, they usually come in with some level of ambiguity, so data professionals need to work closely with business stakeholders to understand their specific needs, pain points, and objectives. This involves conducting in-depth interviews and collaborative discussions to elicit detailed requirements around the type of data needed, the questions that need to be answered, and the key performance indicators or metrics that define success. 

Data professionals then have enough information to analyze these requirements to identify data sources, design appropriate data models and pipelines, and determine the necessary analytical techniques and technologies to answer business questions. This requirement gathering and analysis work lays the essential groundwork for delivering impactful, business-centric data solutions that drive tangible value.

[Story Time]

A few years ago, I was involved in a project that started as a one-time data analysis and somehow evolved into a complex machine-learning solution that needed to be integrated into the product using live data. The requirements were initially vague, and the scope quickly spiraled out of control.

The project dragged on for months without a clear resolution for multiple reasons:

• Although the vision for the end output was great, the requester didn’t take the time to clearly articulate and document exactly what was needed, and the analyst was not able to fully grasp the essence of what the requester wanted. 
• Once the desired outcome was finally clarified, we realized that the solution was so much more complex than expected as it required involvement from data & BI engineers and Product & Engineering teams.
• Due to the unclear expectations across all parties, the project was in limbo for months which created a lot of friction and misunderstanding for everyone involved.

Eventually, the head of analytics had to step in to resolve the conflict and realign the expectation from the requester, consult with data scientists, engineers, and product managers to assess the true complexity of the project and the resources needed, and then eventually come up with a feasible solution.

The key lesson here is the importance of clear communication and scope definition, especially for data-driven initiatives that span multiple teams and functions. Without that upfront clarity, even the most well-intentioned project can quickly spiral into protracted uncertainty and internal conflict. Setting the right expectations from the start is critical to driving successful data-powered outcomes.

3 - Business Acumen Development:

Understanding the industry, market dynamics, competitive landscape, and business models isn’t just business stakeholders’ roles and responsibilities, it’s also data professionals' job to learn about them so they can contextualize data insights effectively. This involves immersing themselves in the day-to-day operations, challenges, and decision-making processes of the business units they support. 

By cultivating strong business acumen, data professionals can better anticipate the evolving analytical needs of stakeholders, identify emerging opportunities to leverage data, and frame their data-focused work in the language of business value and impact. It does sound like a lot of work on top of all the technical work that needs to be delivered, but the business knowledge can be built over time as data people grow with the business functions. However, setting the intention to develop business acumen from day 1 will set them apart from other technical data experts, and ultimately drive overall organizational success.

[Story Time]

A few years ago, I was a few months into my role within a Corporate Marketing/CRM analytics team. Despite my ability to deliver accurate data analysis and create visually compelling visualizations, I struggled to extract truly relevant and actionable insights for the CRM manager. The root of the problem was that I lacked critical context about the specific marketing programs and campaigns that had been launched over the past year, as well as the overarching CRM strategy and tactics being employed by the organization.

At the time, I felt that this information gap was unfair, as no one had proactively shared these important business details with me - yet the CRM manager still expected me to think and operate like an experienced hire. However, I soon realized that it was also my responsibility as the data professional to take the initiative and build a collaborative relationship with her. By actively learning from her expertise and experience, I could better understand the business context and design my analyses with clearer intention.

This experience underscored the crucial importance of data professionals developing strong business acumen, in addition to their technical analytical skills, to truly bridge the gap between data and business needs.

Overall, the ability to bridge the gap between data and business needs, while effectively translating technical outputs into practical decisions and actions, is a hallmark of successful data professionals in today's data-driven organizations.