Tableau’s new tool, Business Science, “helps domain experts understand the key drivers of a model without having to learn traditional data science tools.”
This announcement follows a surging trend in data analytics— to make data and insights more accessible to business people. Many companies now recognize the value of data science in this endeavor and are striving to put advanced analysis skills into the hands of business people.
Since data science and its successful deployment is something we’re very familiar with at AnswerRocket, we want to jump into the conversation.
Tableau’s Business Science Sparked a Data Science Conversation— What Should Businesses Consider?
With uncertainty from COVID-19, businesses must navigate unprecedented scenarios with an overwhelming amount of noise in their data.
Many businesses fire on all cylinders to simply analyze historical data, let alone capitalize on current or future growth opportunities.
Data science provides the diagnostic and predictive capabilities that enable businesses to make proactive decisions with sufficient precision, speed, and context.
However, data science teams are stretched thin, tasked with shepherding their models through the business and selling their findings to decision-makers. Meanwhile, business teams lack the technical context to make use of the models’ output and take action.
There’s a gap between what data science can do and how businesses can gain value. One approach to solve this problem is to put no-code AI in front of business people, guiding them to adjust models based on their understanding of the data.
In theory, this allows data scientists to focus on higher-level analysis, while enabling business people to answer everyday questions with data science skills.
However, there are two potential gaps in this approach:
- Data scientists still aren’t empowered to “sell” high-level analysis to business people
- Business people aren’t necessarily clear on the use cases that call for data science, meaning data scientists must undertake significant change management work
In both cases, data science teams must perform work that’s best left to the realm of the data science unicorn— an expert storyteller and modeler who’s too rare to count on.
The work of data science is not the same work of designing outputs that make sense to business users and helping decision-makers take action. Nor is it the same work of getting business users to adopt a new modeling tool, no matter how user friendly.
With that in mind, what other approaches can businesses take to incorporating the strengths of data science into their organizations?
Approaching Data Science for Best Results
Let’s see how business users and data scientists can solve problems without requiring either to level up their skills in areas outside their scope.
- Data science as a service — This approach brings a team of data scientists into your business to do the hard work of building and deploying models to end users. Data science as a service pulls rare talent into your organization without a long hiring process.
- Operationalize data science models — High-level data science can be incorporated into self-service analytics and automated for business people. This approach enables domain experts to assess output and refine analysis, while preserving a single source of truth for every end user. Data scientists can leverage their expertise to fine tune the model, while business people can get visualizations and natural language insights that speak in their language.
- Access pre-built machine learning skills — Models that have already been refined and packaged for critical use cases can accelerate data science skills without overhauling their process.
In each of these approaches, data scientists can lean into their data science skills, and business people can lean into their business skills.
To learn more about these strategies, check out RocketScience, AnswerRocket’s data science services.
