As more organizations seek to operationalize artificial intelligence (AI), they are encountering data management challenges. These challenges are presenting new opportunities for managed service providers (MSPs).
A survey of 550 IT and business professionals across the U.S., United Kingdom, Ireland, France, and Germany from organizations with 500 or more employees finds more than two-thirds of organizations are struggling to access all the data needed to run AI applications (69 percent) and convert the data into a usable format (68 percent).
Conducted by the market research firm Vanson Bourne on behalf of Fivetran, a provider of tools for moving data, the survey also finds that 67 percent of respondents work for organizations that plan to deploy new technology to strengthen basic data movement, governance, and security functions.
The barriers to adoption cited include disorganized or siloed data (43 percent), lack of support from senior leaders (42 percent), current IT infrastructure being outdated (42 percent), lack of internal skills (41 percent), access to data (40 percent) and low-quality data (35 percent).
Overall, survey respondents estimate inaccurate or low-quality data results in misinformed business decisions. These decisions cost organizations, on average, 6 percent of their global annual revenues, or $406 million, based on average global annual revenue of $5.6 billion.
In fact, data science teams hired to construct AI models spend most their time on basic data management tasks. More than two-thirds of respondents (67 percent) said data scientists spend most of their time preparing data rather than building AI models.
As a result, many data science teams are augmented by data engineers with the skills required to programmatically manage data. However, there was a shortage of data engineers long before the AI boom came along. Many organizations have to use data scientists to perform data management tasks such as classification. This often takes time away from their core mission.
A once-in-a-decade opportunity for MSPs
Most MSPs, however, already have a fair amount of data management expertise. There is now a clear opportunity to extend the scope of the services to include data engineering tasks that most data science teams would prefer to have someone else perform. A managed data engineering service would significantly increase the pace of data science teams to build and update AI models.
It’s not clear at what pace organizations will be building and deploying AI models. However, as application development continues to evolve, the bulk of most applications will include one or more AI models. The amount of data required to train all those AI models is nothing short of massive. The truth is most organizations today don’t have the robust processes in place to manage data at that level.
An opportunity for MSPs to create an entirely new class of services at this level of scale comes along about once a decade. MSPs don’t need to have a lot of AI expertise to take advantage of it. All they need to be able to do is organize and manage the data pipelines that feed the AI models. The challenge and the opportunity now, is to acquire the data engineering expertise needed to manage those pipelines. Especially at a time when most data science teams are already desperate for help.
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