Why data scientists fail

“Data scientists are expensive resources that often fail to produce results because they don’t get the technical support they need,” says Brett StClair, CEO of Teraflow, a data engineering firm with offices in London, Johannesburg and Cape Town.

According to StClair, data science is a multidisciplinary function for which the input of various experts is required, not just the data scientist.

Starting from scratch
Newly hired data scientists regularly find themselves entering a workplace with poor machine learning infrastructure or few resources to work with, and no appropriate management function to guide them. They may be embedded into the business intelligence (BI) team or analytics team with only a token amount of data with which to experiment.

It’s not long before they realise this sample data and their personally selected ML tooling is hardly enough to solve even basic business problems. “To get the most from its investment, the management team has to change its approach to data science or it will continue to come up empty,” warns StClair.

An engineering approach
Organisations should think of the machine learning function as being similar to building a factory. Industrial engineers must first lay out production lines in the most efficient and cost-effective configuration possible before the factory manager can start manufacturing. In addition, they require a well-stocked raw materials warehouse with the right components with which to make their finished goods.

In the same manner, data engineers and machine learning engineers need to develop the most efficient data pipelines and machine learning processes before data scientists can effectively analyse data and train algorithms. They also have to fill these with corrected data from a supply chain of data silos spread across the enterprise and the Internet.

“New data scientists are basically being given an empty raw materials warehouse and a deserted factory floor,” says StClair. “Yet, they’re expected to create AI-based components from nothing.”

The right people for the job
The biggest mistake a company can make is to assign the responsibility for developing its machine learning infrastructure to its IT department, BI team or the data scientist themselves. 

It may be hard to believe that those dealing with data all day are the least qualified to construct a sound platform through which it can be transformed into production-ready AI. Yet, companies with winning AI implementations are those who recognise that a highly specialised skillset is required to make this critical element work correctly.

“Factory managers don’t build factories; they produce finished goods,” says StClair. “They leave the factory building and raw material supply to the experts.”

Success begins at the top
Above all, organisations need a strong executive leader to architect their data science strategy and drive its growth, namely a Chief Data Officer or Chief Data Science Officer.

“When it comes to board-level buy-in, most enterprises already have that,” says StClair. “What they often lack is a knowledgeable C-suite champion who knows what they absolutely must get right to reach their goals and overcome the inevitable obstacles along the way.”

ENDS

MEDIA CONTACT: Stephné du Toit, 084 587 9933, [email protected], www.atthatpoint.co.za
For more information on Teraflow.ai please visit:
Website: https://teraflow.ai/
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