The all-round team needed for futureproof Data Analytics

Posted on: February 23, 2018 by

This is the 2nd article in a series of 5 publications on organizations’ Data Analytics strategy. The series, called ‘Moving beyond point solutions and pilots in Data Analytics’ addresses five challenges that organizations most experience when they want to upgrade their Data Analytics from point solutions to strategic, data driven and coherent governed activities.

This second publication is explaining how you should optimize the Data Scientist’s competences within your organization, by making use of an all-round team.

As stated in our prior article, the Data Analytics competency is evolving quickly with extreme data, handling real time data and applying complex and unique statistical methods. Moreover, the industry is heavily demanding applications like deep learning and artificial intelligence. This is the sweet spot where the Data Scientists need to stand out. The most beneficial approach to making use of real Data Science, seen with our clients, is by putting together a multi-disciplinary dream team – yes, a dream team, as it consists of different, complementary roles & capabilities.

Mapping your Data Analytics requirements to your team

Most of the time, the Data Scientist is preparing, cleaning and analyzing the dataset – time consuming efforts, that are required and need careful attention and may require a specialist. Instead of letting the Data Scientist execute this, these tasks are well trusted with the Data Engineer. Leaving space for the complex and unique statistical methods at the Data Scientists side.

Supporting roles to optimize the Data Scientist:

  1. Data Engineer
  2. Data Analytics Consultant
  3. Data Architects
  4. Data Operations Engineer
  5. Data Privacy Officer delegate
  6. User Experience designer


Just like the Data Engineer, the Data Analytics Consultant is an excellent role to help assist in Data Analytics use cases. By letting the Data Analytics Consultant collect, clarify and stress business needs, the use case will deliver maximized value. By selecting use cases and pre-select data sources, the effectivity of the Analytics team is maximized.

Next to these crucial roles for Data Scientists, Data Engineers and Data Analytics Consultants, our most successful clients show adequate Data Analytics teams consist of:

  • A Data Architect. Data Architects are best suited to reuse insights, data and business logic, and to setup metadata management. Most of them also take care of selecting tooling and integration into primary processes to ensure action where needed.
  • A Data Operations Engineer, capable of ETL (Extract, Transfer & Loading), who knows where the needed data is stored, how to extract it and how to provide it through the proper channels, including data masking for example.
  • A Data Privacy Officer: monitoring data governance, making sure the data in your use cases is GDPR compliant and consulting the Security Officer.

Once your all-round team has come up with their first results, the most effective way of presenting your analytics results requires an additional expertise: the User Experience designer. The UX-designer is specialized in developing mock-ups. Let him work with your dream team and an application developer to maximize the team’s results in the business.

Crucial roles outside of the Analytics team

Next to your self-organizing dream team, mandate outside of the Analytics team is needed to maximize your data use cases. Sponsoring, business support, interpretation of your findings, using and changing business insights as a result of your findings, are typically situated outside of your team. Therefore, you require some support outside of the dream team.

Supporting roles outside of your Analytics team

  1. Product Owner
  2. Subject Matter Expert
  3. CEO
  4. Business Change manager
  5. Data Infra Engineer


Let’s not forget that each application requires a Product Owner and a Subject Matter Expert to be involved, also to prepare the organization for integrating results into business processes. The CEO should provide the Analytics team with support – and mandate if applicable. The role of the Business Change manager can help to support agile delivery within the organization, mapping stakeholders affected by the Analytics team and help to implement the way of working into the rest of the organization. The Data Infra Engineer should help you with network issues, setting up and configure firewalls in order to let you access the data needed.

All of the above applies to you, if your organization strives to become a strategic, data driven organization. It is what you should have in place to move beyond point solutions and pilots in Data Analytics.

In order to maximize the benefit of Data Analytics for your organization, and in order to move beyond point solutions and pilots, an all-round team is your way forward. How to map these skills to your organization and meanwhile assuring the right mandate and responsibilities will be the topic of our next article.

The research for this article, which is a series of 5 with the collective title 'Moving beyond point solutions and pilots in Data Analytics', has been done by my team at Atos Consulting. So the thought of the gentle courtesy goes out to Tom Konings, David van Steen, Marcel van de Pol and Carline Nauta.

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