The Changing Roles Supporting IPA in the Workplace

Celaton’s Chief Technology Officer, Richard Hill, has been working within the Software and Business Process Automation industry for over 20 years. During this time, he has witnessed many changes in the roles and functions of people who support and facilitate technology.

With the current drive for organisations to adopt increasingly more sophisticated automation technologies, it is more important than ever to understand how people can support Digital Transformation and thrive in the Future of Work. Below, Richard explores the importance and changing roles of Data Scientists and Analytics Translators.

Richard Hill

How important is the role of Analytics Translators and how has it evolved?

Analytics Translators are a vital part of an organisation. They are the bridge between Business Leaders and Data Scientists. Historically, the role has been combined with the responsibilities of Data Scientists, however, future Translators need to balance an understanding of both data insights with creative business outcomes, in order to inspire and incite business leaders to action.

Could Business leaders become Translators and Analysts perform some of the tasks of Data Scientists?

No. I don’t believe so.  Business leaders have essential strategic and tactical knowledge and skills to drive an organisation. Business Leaders need to be able to see the wider picture and data requirements to support this, but they are unlikely to have the time to interpret the individual intricacies of it.  Instead, Business Leaders need to support and appreciate the needs of Data Scientists and Analytics Translators to empower them to be creative as they have unique skills needed to translate the data into actionable insights.

Data Science

Are there alternatives to data science to minimise the impact of the current talent shortage?

Data is not easy for an organisation to make sense of, yet the more data an organisation has the greater its understanding and competitive advantage is. Data Scientists are needed to facilitate understanding, but they also currently perform the Translator role, resulting in less optimal insights. Organisations need to clearly define and separate the two roles so that each can focus on their specialities and deliver the best results. What we would like to see is that as organisations deploy this approach, Higher Education establishments will develop courses to address the changing needs in businesses, resulting in more talent in the future. 

If data science is a means and not an end in business, how can business leaders better understand this more actionable take on data?

Analytics Translators’ creative thinking is essential because it enables Business Leaders to focus on their roles with greater insight and knowledge to make strategic decisions to develop the company. To ensure this, Leaders need to remove knowledge sharing barriers, which should create additional informative insights leading to more successful decisions and actions. 

And what does this mean in the augmented enterprise?

Data is often complex and not necessarily insightful to Business Leaders. Data Scientists and Translators are therefore needed to generate and communicate insights, which free leaders to perform creative activities such as idea creation or problem-solving.

Through augmenting technology (machine learning) and associated roles (data scientists) with creative roles, organisations can consume and make sense of huge amounts of data and use it more effectively to drive advantage.

Organisations need to move beyond the black box of data science – but how can we effectively do this?

Data is complex and when it arrives into an organisation it is raw. Data Scientists give it meaning; however, it then also needs translating into a business context, which is the role Analysts perform. Analysts shed light on the black box by connecting it with the rest of the organisation and measuring the impact of outcomes generated.

Should we do more to show how data models are validated and be honest about their effectiveness?

Yes – if the data model doesn’t work then the decisions based upon it will also be wrong, potentially negatively impacting the business as a result. Data scientists need to accept and anticipate they might get the data model wrong and this is a crucial step in validating a successful model along with the desired outcome.


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