Digital business transformation requires data engineering heroes


Most organizations intuitively realize that sound data management and data engineering practices are at the core of any digital business transformation practice. However, a survey of 150 IT professionals working for organizations with at least 4,000 employees published this week by 2nd Watch, a provider of managed IT services, suggests that most organizations are still a long way from mastering data management fundamentals. In fact, only a little more than a quarter (26 percent) claim to have data management strategy at all. Well over two-thirds (70 percent) said they don’t have what they consider to be a mature data strategy.

That absence of a data strategy creates an opportunity for managed service providers (MSPs) to bring some order to chaotic IT environments that are hindering the rate at which organizations can digitally transform, assuming MSPs themselves have the appropriate data engineering skills. Organizations that are already falling behind the digital business transformation curve in effect need an MSP hero to save them from themselves.

Reasons for data engineering optimism

The good news is there’s more awareness of a data management crises, that prior to the COVID-19 pandemic was largely ignored. Overall, the survey finds two-thirds of respondents (66 percent) recognize that data quality is the most important aspect of their data strategy, followed by security and privacy (61 percent) and integration (48 percent).

Despite giving themselves low marks for data management, however, 60 percent of respondents claimed to have some type of enterprise-wide data catalog in place. Nearly half of respondents (47 percent) also said they’re using best-of-breed integration tools with bundled data quality, governance and self-service provisioning, with half relying on tools provided within the cloud platform they selected. Nearly two thirds (64 percent) are storing data in the cloud, with 57 percent reporting they are using a cloud data warehouse. It’s clear organizations are acquiring tools and platforms, but a willingness to spend money isn’t quite the same thing as having the data engineering skills required to succeed.

In fact, even if organizations can organize their data properly, it’s not clear they have the tools required to analyze it. Less than half (42 percent) of respondents said they have the analytics expertise in-house to meet the business needs, with 38 percent noting they require some sort of self-service analytics capability to be set up on their behalf by an internal IT team. Only 39 percent said all their data was available for reporting and analytics.

On the plus side, more than half (52 percent) said they have data science/machine learning projects in production, while 42 percent are testing some use cases. At the same time, 41 percent said moving to the cloud has allowed them to be more agile, with 44 percent noting their cloud infrastructure and costs are under control.

Finding the data opportunity for MSPs

Collectively, that absence of analytics expertise creates a significant secondary opportunity for MSPs that help organizations makes sense of their data once it’s been organized. Many of those organizations are simply not quite sure what is the right question they should be asking to manage their business better.

MSPs, of course, have been moving and managing data to varying degrees using everything from backup and recovery platforms to extract, transform and load (ETL) tools. What many of them don’t appreciate is what was once considered relatively common maintenance tasks, are now foundational to data engineering, which at the moment is one of the skills in greatest demand across all of IT. The challenge and the opportunity for MSPs in many cases now is just letting customers know they already have the very data engineering skills they need most.

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