Syncsort’s 2019 Data Trends Results: And the Survey Says…
As a product marketer, I’m always on the lookout for the latest trends, indicators and adoption rates that will help us better target and position our solutions to meet the needs of customers. And I’m particularly excited when we publish the results of our own Syncsort annual market surveys, since I get to help write the questions.
While the questions (and survey titles) have varied a bit over the five years we’ve been conducting our Hadoop (aka Big Data) (aka Data Trends) survey, the intent has remained constant: to understand how organizations are using data and technology to solve for challenges they face today, and prepare for the future.
Some of the findings confirm what we would expect – like Cloud/Hybrid Computing topping the list of IT initiatives for the next 12 months, and Skills/Staff Shortage identified as the top IT challenge facing their organization.
On the other hand, some results reveal surprises. For example, only 50% of respondents indicated that their organization is effective at getting data insights to business users, and 68% said their organization is negatively impacted by siloed data. Despite this, “Improved Access to Data” fell fourth on the list of business initiatives that IT will support in 2019, behind Increased Operational/Workforce Efficiency, Improved Customer Experience and Cost Reduction.
Another set of questions, asked of IT professionals in organizations with data lakes/data hubs, shows an apparent inconsistency as well. While only 18% of respondents from organizations said they are offloading data/workloads from legacy systems (such as EDWs, Mainframes, IBM i) into their data lakes/data hubs, these legacy systems account for the top three data sources populating these Big Data repositories. Upon closer examination, however, there might be a logical explanation. The top three use cases cited for the data lake are Advanced/Predictive Analytics (50%), Real-time Analytics (42%) and Operational Analytics (41%). So, it stands to reason that this legacy data is brought to the data lake to fuel those analytics – and possibly some of the traditional “offload” use cases are now moving to the cloud, as adoption matures.