Global survey of 450 enterprise data team members reveals that inefficient data processing could be costing enterprises up to $43.5 million annually
With digital transformation and cloud migration accelerating over the past two years, enterprises have struggled to keep pace with the complexity and volume of data. Now, new industry research, conducted on behalf of Matillion by independent research firm Vanson Bourne, uncovers a root cause of this issue, revealing that 75% of data teams believe that outdated migration and maintenance processes are costing their organizations time, productivity, and money — potentially at an annual price tag of up to $43.5 million.
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The survey of 450 data decision-makers and data team members across the U.S. and UK found that data teams report spending 57% of their time, on average, on data migration and maintenance. In addition to depriving data teams of time and labor that could be spent on strategic and analytical work, these efforts are also costing companies millions. By addressing a few key data challenges, companies could enhance enterprise data initiatives.
The survey pinpointed five critical areas where data teams struggle:
- Significant time spent on maintenance tasks makes it difficult for data teams to deliver insights and results. A majority of respondents cited three main tasks as requiring either a relatively high or very high level of effort: bringing in data from disparate sources (63%), preparing data for analysis (56%), and exporting/delivering data back to SaaS applications/systems (55%).
- The negative impact of slow data migration and maintenance is felt across the business. Two-thirds of respondents (66%) believe their organization is wasting time on data preparation.
- Certain data types are left behind and data teams have new (and growing) blind spots. Nearly 40% of data teams surveyed admit they don’t fully understand how data is being used in their organizations, and 44% worry about the challenge of dealing with the diversity in the types of data they work with. Cloud data (32%) and IoT data (31%) were noted as the most commonly unavailable or unsuitable sources for business intelligence and analytics.
- The war for talent is hitting enterprise data teams hard. While these roles have a notoriously high turnover rate, 87% of data decision-makers agreed that their organization struggles to retain talent. More than two-thirds of surveyed data users stated they are considering leaving their job in the next two years, with 29% strongly considering it.
- Data teams are burned out – enterprises need to lighten their load to retain talent. Half of surveyed data users revealed that the constant pressure and stress that comes with dealing with inefficient data integration is causing them to experience burnout and driving data teams to look for new job opportunities where they’re not stressed (79%) or bored (53%).
“Despite the acceleration of digital transformation over the past 18 months and high volume of data entering the enterprise, the survey results reveal that data integration practices remain outdated and primitive — creating tension among data teams and leaders and information gaps across the enterprise,” said Matthew Scullion, CEO of Matillion. “In order to harness the power of data as a strategic asset, organizations need to reduce the time spent on manually refining, migrating and integrating data and the maintenance of those integration tasks. It’s critical for leaders to unburden data teams, increase their productivity, and widen the profile of users able to make data useful. Only in this way will they be able to respond to their businesses with the speed and agility they require and demand.”
Matillion detailed the findings of this survey in its entirety in an ebook titled The Data Divide: Top Challenges Facing Enterprise Data Teams.
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