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Data projects failing thanks to skills shortages

The channel should have a role to play in helping enterprise customers execute data initiatives

In theory where there are skills shortages should spell an opportunity for resellers to step in and help under resourced customers and the red lights are now flashing on the data management front.

Enterprise customers are finding it difficult to deliver data initiatives, with more than half of projects failing, according to research from database player Exasol.

Retail and financial services were the verticals most impacted by skills shortages with 40% of firms in those sectors blaming lack of expertise for their data projects failing.

Some of the plans that businesses had which have failed to come to fruition include around data consolidation, migration and getting in shape for GDPR.

There were various reasons given from the surveyed UK and German firms (see box at end of piece) for why projects missed the mark giving the channel a clear idea of where the pain points are.

Sam Sibley, strategic partners & alliances manager at Exasol, said that there was more demand from customers to get the most out of their data given the ongoing focus on digital transformation.

Some of the areas where skills were in short supply were around some of the emerging areas like AI and machine learning.

"Businesses want data to work for them, and this is very much behind the rise of data-driven initiatives such as machine learning, where the algorithm takes control. Investment in this area is rising fast, a recent Deloitte survey highlights that 57% of businesses are increasing spending in the technology. In general, this technology is no longer seen as a cost, but an opportunity and a revenue driver. However, there is still work to be done to ensure data-driven initiatives succeed and are understood at all levels of the business," he said.

"The most successful businesses will be those that invest in their technology and hire the skills to make those investments work," he added.

Why data initiatives failed

* data security issues 
* poor data quality 
* lack of employee skills 
* lack of employee buy-in 
* siloed data 
* not delivering the time and cost savings expected 

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