Structuring the data science team requires out of the box thinking

by | Nov 9, 2021

Structuring the data science team requires out of the box thinking

by | Nov 9, 2021 | Blog

Structuring the data science team requires out of the box thinking 

It is no secret that the world is facing a shortage of qualified and experienced data scientists. While it has taken some time for the proverbial penny to drop when it comes to the importance of data science, now that it has, organisations are struggling to hire their own data scientists. 

Many data science functions in the business rely on the likes of data readiness, data accuracy, and data completeness, to name a few. If there are quality issues in any of these areas, then there will be challenges in achieving real value. With data scientists discovering, designing, and developing their models for positioning to the business and technology leaders, there must be a balance between practical use case and future-forward insights. 

Once these models are approved, for pilot projects or implementation, these must still be ‘productionised’ as per the appropriate IT governance practices. Data scientists cannot do this one their own. In fact, the term data scientist is itself quite vague in an industry that is very easy to embrace jargon and buzzwords. 

Horses for courses 

There seems to be two distinct challenges. The first is finding the right data scientist for the job. However, that role must be clearly defined for the organisation to find the correct fit for its requirements. The other challenge is how to build a team around the data scientist that complement their skills with other data-relevant expertise. This can encompass the likes of data analysts, data engineers, data architects, machine learning engineers, data ops engineers, and data visualisation engineers to name just a few. 

It is therefore hardly surprising that many organisations are either dumping all the ‘data’ jobs into the lap of the data scientists. Or, perhaps even worse, if there is no data scientist in place, they are neglecting the function entirely. 

Managed services 

This is where the value of data engineering as a managed service offering cannot be ignored. Over the past 18-months, managed services have gained renewed momentum at a time when businesses have had to focus on delivering on their core mandates. These providers could assist them on the technology side. 

The same can be applied on the data engineering side – a key enabler to data science. Very few organisations have the resources in place to establish a full-strength data science team. Even just getting a data scientist might be a bridge too far. For these organisations unable – for whatever reason – to hire for such roles internally, managed services can provide the breakthrough they needed. 

So, perhaps consider data engineering in the managed services space especially if your organisation is battling to find the skilled individual at a time when so few are available. This could enable your organisation to grow and work with a trusted partner to take care of the heavy lifting when it comes to data science. 


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