Collaboration Between Data Scientists and Decision-makers? It's Possible.

By Greg Silverman, Feb 08, 2021

When each department of an organization works seamlessly together, the business benefits from streamlined operations and improved decision-making. However, achieving optimal collaboration is hard because natural differences exist.

One area where this disconnect is felt the most is between data scientists and business decision-makers. While both work with data and reports to come to conclusions to help their organization, their methods differ, which diminishes the quality of their collaboration.

When these two teams aren't in sync, data scientists are left digging through siloed data, and business decision-makers receive differing reports and must act on their intuition. This lack of communication leads to fast or ill-timed decisions that affect organizational growth.

This divide has started to shrink because analytics teams are bridging the gap with new processes. With a foot in both departments and the right tools, analytics enables collaboration.

Working with assumptions vs. working with facts

While data scientists and business decision-makers are both working to find information to make educated plans of action in the best interest of the company, there is a distinct disconnect between them.

Ultimately, the separation of these two teams comes down to how they operate. As Northeastern University states, data scientists mine and scrutinize data to answer complicated business questions. With the goal to find the absolute truth to the question at hand, data science teams spend weeks or months developing and testing models to come up with solid answers to share with other members of the organization. Data scientists operate in "knowledge-mode" to minimize decisions that are based on assumptions. Timelines are often pushed out, so quality reporting is achieved.

Business decision-makers, on the other hand, often work in "crisis-mode." While highly trained in making decisions, they don't typically have the time to dive into data to make decisions in response to a rapidly evolving business landscape. Basing their decisions on assumptions and existing transactional data helps them change direction at the pace of their market. The trade-off, however, is that timely decisions may not always be as informed as they would like.

So the dilemma exists. While data scientists mine the information and test hypotheses that would help decision-makers, the decision-maker doesn't always have the time to wait for models to be perfected. Additionally, by the time these models are completed, market conditions may have changed, so the results are no longer relevant to decision-makers. 

These teams could be more efficient and effective in answering complex strategic questions if they collaborated.

Where do analytics come in?

Luckily for businesses, there's no longer the missing piece to bridge the communication gap between data scientists and decision-makers. Enter the analytics team. With a focus on both useful processes, data, and a sense of urgency and emphasis on timeliness, the analytics team takes vital insight from data science analytics and turns it into accurate and timely recommendations for decision-makers. 

When the analytics experts bridge the gap between these two teams, the business benefits from a cohesive data set and consistent interpretation of the results that are relevant from the initial hypothesizing and carry through to making a decision. Of course, this collaboration doesn't magically happen overnight - it starts with the analytics team bringing everyone to the table to agree on vision alignment.

This process begins by having all teams agree to the objectives of the model and hypothesis. What is your organization trying to learn from the data, and how will it be used to make business decisions? In the same breath, make clear the terminology that will be used along the way. This can be as broad as defining the data sets or as niche as clarifying the definitions of terms being used. 

Setting a timeline - agreed upon by all parties - ensures deadlines are being met, and models are still accurate when decision-makers need to run their scenarios. Finally, mutually agreeing on the model's framework, such as where and how the organization is competing, allows the hypothesis and further predictions to be aligned with everyone's ideas. 

Making sure everyone sees eye-to-eye before diving into data promotes collaboration between teams and a consensus to agree on the results. The process from data mining to decision-making is streamlined, and the newfound collaboration and trust between teams benefits the business from the inside out to solve complex problems.

Unifying analytics on one platform for decisions

Analytics teams skillfully unify data and insights to promote collaboration between data scientists and business decision-makers. They encourage a communicative process to extract analytics, explain the past, and hypothesize the future using the best skills of both teams to do so on one platform.

Utilizing predictive self-service tools, the analytics team manages models with integrated data from data scientists and allows decision-makers to run scenarios about the market in near-time for the most accurate results. This effectively connects the broken links between the data scientists' hypotheses and information and the in-market testing results needed by decision-makers. Each team can work to their full potential in their area of expertise and together create a more comprehensive model ready to tackle timely and complex problems.

With the right tools, it's possible for the analytics team to enable conversations and collaboration between data scientists and decision-makers that never organically occurred before. This dialogue naturally benefits the entire organization by breaking down barriers to communication and information silos to provide faster, better answers to strategic questions.

As the world continues to change at a rapid pace, it's more important than ever to promote collaboration to ensure your organization is prepared to model complex problems. With Concentric, analytics teams don't just receive software; they gain a blueprint to foster communication and company-wide involvement with process modeling.

Contact our team today to deepen the trust in your organization.


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