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Enterprises across industries collect data from nearly everything. Potentially, every checkout machine, car door, connected coffee mug, or dishwasher has sensors that generate data continuously.
These insights have the potential to transform supply chains, enable safe self-driving cars, or inspire life-changing consumer products and services. While enterprises have every reason to use data to benefit both themselves and their customers, maximizing fast-growing datasets is easier said than done.
Below are three key points that outline the shared challenges that surround activating data at scale.
Also see: Digital Transformation Guide: Definition, Types & Strategy
1) Edge Data Sources Add to the Problem
Connected devices are starting to dominate industries such as automotive, manufacturing, energy, and utilities. Statista predicts that the volume of connected devices worldwide will reach 30.9 billion by 2025, and the data generated from these devices is estimated to be 79.4 zettabytes (ZB) by the same year.
This explosion of sensor data has led to the rise of edge data centers, where data can be stored and processed outside of on-premises or cloud environments. According to Gartner, edge data centers will host 75% of enterprise-generated data in the next three years.
While edge computing has created enormous value for companies that need real-time processing for end users, edge data gets stuck outside the cloud, where it would otherwise add value over time. Edge computing doesn’t allow for the collective analysis of data distributed across many edge data centers—the type of analysis that could inform product roadmaps or shape new business models.
As a result, datasets that are growing exponentially in edge environments are being heavily under-utilized. In fact, a recent study from Wakefield Research found that 69% of data and analytics executives at large enterprises report that their data is trapped and unable to be fully used.
Also see: Best Data Analytics Tools
2) Roadblocks are Coming Into View
Activating data that’s distributed across edge and on-premises data centers requires a variety of resources: infrastructure, time, money, and, arguably the most important resource, talent.
Data is growing faster than the tech talent pool. The Wakefield study found that a lack of technical skills was one of the top reasons why data leaders couldn’t keep up with the pace of data growth. Similarly, Gartner’s 2022 CDO survey cited talent shortages as a key reason why they weren’t able to hit their data and analytics goals.
Tight IT teams are being given the increasingly-challenging task of making sense of distributed enterprise data. They have to oversee hundreds of data centers, identify the most meaningful data to move and analyze, and select the most effective and fitting environments for running machine learning models. This is a serious undertaking, so it’s no surprise that most enterprises cite tech talent as an inhibitor to data goals.
Unfortunately, talent isn’t the only common roadblock for enterprises looking to turn data into value. According to Wakefield, other reasons why data leaders struggle to fully use their data in the cloud are:
- The time required to move it (46%)
- Risk of disruption (44%)
- Financial expense (44%)
3) Urgency is Building
Chipping away at these blockers is critical. Leaving data unused on-premises or at the edge not only slows down digital transformation but also opens the door to potential repercussions. Wakefield found that the top consequences of not leveraging the cloud or cloud computing include:
- Compliance risks (36%)
- Data security (36%)
- Costly infrastructure (33%)
- Lost business opportunities (33%)
Importantly, un-touched data leaves money on the table. Nearly every senior data leader (96%) have found ways to use data to drive revenue, and 56% have identified several new revenue streams from their data. These reports are really promising. Even with all of the challenges associated with moving and managing distributed enterprise data, the data that does make it to the cloud is turning into tangible results.
To move closer to a world in which every data point adds value, enterprise IT teams need to recognize and address these common challenges. The potential outcomes will make all the efforts worthwhile.
Also see: Top Edge Companies
About the Author:
David Richards, CEO of WANdisco
