Business organizations are increasingly adopting data science solutions for decision-making in 2020 according to The Harvard Business Review. The data-driven culture¹ is influencing intelligent business operations with data science teams enabling enterprises to leverage data and optimize operations. Executives are using data science from strategy, implementation, and leadership management.

Despite the acceleration of data science applications² in corporate organizations, executives misunderstand data science deployment where they fail to realize value from data; further affecting performance.

Hiring data scientists and leading a team does not translate to success in the data game. Executives must understand basic principles such as creating a data culture in their organizations and adopting an analytical approach in data management.

In this digital economy, executives must understand how to harness insights from data to stay ahead of the curve. The term ‘Data is the new oil’ makes sense in this context as organizations need to derive insights from their data to remain competitive.

Tech companies including Amazon and Facebook have mastered the art of data science and #artificialintelligence for personalized recommendations and improving user search experience³ making them top performers in their industries. Executives should do the same in their organizations by using data to understand insights to drive growth.

Is your organization using data science solutions?

In this article, I will explore 5 principles of data science⁴ and best practices every executive needs to understand for growth and performance in their organizations.

Principles of Data Science for Executives

Data Science Teams and the C-Suite

Companies hire data scientists assuming they understand everything about #datascience principles. This is not true. In the digital economy, every stakeholder in an organization should understand digital skills⁵ to enable value creation across the board. Some data scientists work in great companies that value data science while others are stuck in organizations with no defined data culture.

The success of data science in organizations depends on the C-Suite and data science teams teaching each other their respective fields. Data scientists are not experts in strategic management. The same applies to executives when it comes to data science knowledge. Both should teach each other their specialties for smooth data science deployment.

Collaboration between data science teams and management does not mean learning data science from scratch. Executives need guidance on basic data science principles and understand their influence in decision-making. This will create harmony between the C-Suite and data science teams leading to productive growth based on data insights.

Favorable Culture for Data Science Teams

For data science teams to succeed, management must support them all the way. A data scientist requires enough resources to perform duties such as:

Data collection

Data refinement

Data learning

Data expansion

Data maintenance

Achieving these steps requires support from executive management⁶in line with data insights for decision-making. Some organizations expect more from data science teams but offer little support in the actualization of projects.

Communication between #datascienceteams and management remains a top priority for every enterprise looking forward to drawing actionable results from data. Cooperation from management means that data science teams will carry out projects successfully because of support from the top.

Netflix is a good example of data culture⁷ where the company offers data science teams the tools and resources they need to perform their tasks at an optimum level. This explains why Netflix continues to dominate the market by using data and #machinelearning to personalize user experiences.

Centralized Information Sharing

Flexible enterprises understand the need for a centralized data system in their operations in driving revenues, risk management, and other areas including employee engagement. Data science teams face challenges without a centralized information system. This makes getting the right data a problem. Sometimes data scientists become frustrated as they seek data from various sources within an organization without success.

Data silos mean that data science teams have problems accessing data and switching to a centralized system is the right thing to do. A data science team with a smooth experience deploying projects translates to success for the company and reaching this goal requires eliminating data silos⁸. Research from Gartner shows that companies using a centralized data system perform better compared to those with a siloed approach to #datamanagement.

Investing in Data Champions

Who is a data champion? A data champion is responsible for everything around data strategy and implementation within an organization. Besides a data science team, the data champion offers updates on the current progress in data analytics and direction taken by the organization.

A data champion motivates data science teams and connects them with the rest of the organization by promoting their projects and speaking about their value to the organization. This is important to keep the organization’s data strategy on course. Driving the data culture of an organization poses challenges for most companies and data champions bridge this gap by keeping everyone informed about the data strategy.

Education on Data Initiatives

Successful implementation of data science solutions requires that enterprises invest in training and education to make sense of current objectives to stakeholders. For instance, employees need education about data initiatives and understanding the activities of data science teams and their role in the actualization of the goals.

Walmart is a good example here where the retail giant develops training programs for management and employees on data science applications. This promotes engagements around data from different channels of the organization leading to improved customer experiences⁹. The lesson here is that educating the organization on data objectives creates cohesion required for successful data implementation.

Data Science in your Organization

Successful data transformation within an organization is not easy. Why? Because achieving a data-driven culture requires support from management and collaboration with data science teams which is not easy as many think. Some organizations start well with data science implementation but hit a snag whenever problems arise leaving data science projects to gather dust.

From a resource perspective, organizations find investing in data technologies an additional cost, therefore, pushing data science projects back. This is where data literacy comes in. Educating management and employees on #datastrategy is a step in the right direction. This will not only open up new opportunities from data-driven decisions¹⁰but also motivate the organization to continue investing in data science as a competitive performance tool.

Works Cited

¹Data-Driven Culture, ²Data Science Applications, ³User Search Experience, ⁴Principles of Data Science, ⁵Digital Skills, ⁶Executive Management, ⁷Data Culture, ⁸Data Silos, ⁹Customer Experiences, ¹⁰Data-Driven Decisions