Businesses are increasingly focusing on the strategic use of data to enhance organizational performance. Therefore it is advantageous for strategists to have a library of successful (and unsuccessful) use case examples from different industries, and organizations in different stages of data maturity, to support decisions and strategic advice. The benefits should always outweigh the costs when pursuing a higher level of data and AI maturity within a company, and a data strategist will need an extensive use case arsenal to demonstrate the potential value of a data investment.

End-to-End (E2E) solutions require cross functional and cross domain knowledge and skills to be able to conceptualize and translate "the big picture" to teams. Data strategists need to be able to see how different business domains and functions within an organization can work together to solve problems and innovate new products and services. Such knowledge requires application when, for example, siloed domain experts could be unaware of the data and solutions other siloed domain experts are using, often missing out on valuable business insights with reusable datasets.

Computational thinking and data science change the way we think about problems and develop business solutions. Familiarity with methods such as design thinking, brainstorming, reduction and abstraction, for example, can help leverage Artificial Intelligence (AI) for innovation and gain deep insights to problems using data science.

It is very important for a strategist to understand what the different roles in the data domain are (i.e. scientists, engineers, business intelligence developers and others), how they work together and what compositions of data experts are needed for different use cases.

Often termed as one of the most important business and leadership skills of the 21st century, foresight is about planning for the future using different strategic methods to scan and source evolving pathways. Digital transformation and technology have created on one hand a business environment that is undergoing rapid changes. Large amounts of automated data with machine learning also provide new ways to deploy predictive analytics to gain future insights.

As with any business strategist, understanding and growing the value of business assets is a prerequisite for performance. Data Asset Management (DAM) is a key skill for data strategists because the outcomes, quality of recommendations and advice of data strategists is increasingly connected with the quality of the data. Therefore data strategists need to be aware of the best practices for collecting, curating and managing the value of data within an organization. In addition, it is important to know what methods are suitable for which types of data (i.e. computer vision vs. text, data 360). For example, Deep Learning methods are often more useful when working with large amounts of unstructured data.

Knowing the puzzle pieces for setting up viable data infrastructure and architecture are essential to support the implementation of a data, AI and/or digital transformation strategy. To this point, the questions of database selection, cloud provider and various open source technologies, should be made to fit the business strategy by the data strategist. Consideration of performance of the architecture and technology versus the cost efficiency are key factors in the decision making process.

A picture says a thousand words and can convey the concept using vast amounts of data very quickly. Data visualization is a powerful skill that can persuade and awe business leaders and managers, and drive change.

Recent legislation and regulations to protect personal data and recognize the importance of cyber security require new protocols that need to be managed within business functions. In addition, automation and recent technology developments (e.g. 5G, Cloud etc) have created new ways of working. Data strategists need to be aware of their organizational data protocols as well as the jurisdictional legalities that protect and safeguard data. Poor data governance can have strong potential implications to the overall business strategy.

While it is certainly not necessary for a strategist to be able to build and deploy Machine Learning (ML) models, it is nevertheless essential that they have a good understanding of the different techniques currently available. For example, supervised vs. unsupervised methods, regression vs classification and deep learning. Understanding the essential ML methods and the relevant potential business use cases and analytical insights for which they can be deployed is of strategic business importance for also maintaining an effective algorithm portfolio.

Now that machine learning systems are being deployed in more critical industries (such as healthcare and government, among others), it is of utmost importance that ethics permeate every aspect of the work. Successful machine learning systems have generalizable results. For this to work, the data has to be representative of the task at hand. Additionally, those data must be free of bias — here methods from an evolving field termed eXplainable Artificial Intelligence (xAI) can be applied within different parts of the system (i.e. in data collection, model training, deployed model debugging)

Project management is an essential business skill. For a data strategist, knowledge of different project management practices, from scrum to waterfall, will be useful, as many advanced analytics projects are initially structured through shorter project / pilot approaches before they are rolled-out into enterprise wide actions.

With all the data-driven insights, delivery is important when communicating complex and technical concepts to business people that have different skill sets. Presentation skills are where the translational role of the data strategist culminates. For instance, a data strategist discussing analytics with a data scientist will use a very different translational language in the delivery and discussion of results compared with the context when discussing the same results with the CEO / C-suite.

Perhaps the most underestimated skill in the implementation of a data strategy or any digital transformation process. Once the digital transformation process starts within an organization and the Return on Investment of pursuing a high level of analytics maturity is understood by business leaders, changes need to be accompanied by timely and appropriate communication. A good change manager can gain trust in a constantly changing, abstract environment, and create a smooth change process that business leaders commit to for the long run. Change management is the skill that data strategists need to ensure an efficient implementation of the data strategy.

Listening is the other side of the translational role. Listening is becoming important because it is crucial to productivity and managing the emotions and reactions of teams and people

The path to advanced analytics can be long and weary, and coaching managers through the barriers and resistance, as well as giving managers and their teams the courage and confidence to implement the data strategy roadmap are highly appreciated and valued.