By Steve Todd, Fellow and Vice President of Data Innovation and Strategy at Dell Technologies
The Olympic training team at the China National Aquatic Training Base in Qiandaohu is processing real-time data at the edge to evaluate rowing techniques. It’s capturing image data from indoor gym equipment and drones to analyze posture and biomechanical movements to improve its sporting performance. In so doing, it’s both benefiting from and contributing to the vast and growing data universe.
As a former member of a crew team, I particularly like the Olympic rowing example. It demonstrates that any entity can harness data and draw out practical, game-changing insights. It’s one thing to expect blue-chip companies to commission their sea of machines to crunch through data at lightning speed. It’s quite another to witness athletes harnessing AI algorithms to assess data from near and far.
The corollary of the mass democratization of data is, however, the exponential proliferation of data. This is a boon if you’re data-savvy. It’s quite overwhelming and overpowering if you’re not.
To compete in the data economy, businesses will need to be poised for a sustained expansion of data. We passed the point where the amount of data being generated far exceeds what humans can wrap their arms around some time ago. For data to be valuable, businesses will need advanced artificial intelligence solutions and checks in place to ensure they’re reading the data correctly (rather than scurrying down a rabbit hole based on erroneous analysis).
Amidst the data gush, data readiness is key, lest they trip and fall. But what might they trip over?
Businesses Need More Data to Compete…
Businesses face a bizarre paradox. They are clamoring for more data to help them compete effectively, but they can’t handle what’s already coming down the pipe.
More than two-thirds (67 percent) of respondents in the Data Paradox study, conducted by Forrester Consulting on behalf of Dell Technologies, said they constantly need more data than they currently provide. This is understandable given data is now the new fuel for the modern enterprise. As Forrester laid out in its whitepaper, “Unveiling Data Challenges Afflicting Businesses Around The World” companies are eager to embrace digital transformation. However, that requires data—and lots of it.
Data helps businesses understand where their customers are coming from by surfacing trends related to product sales, customer usage patterns, and beyond. It can also help them understand where their customers are going by stoking powerful predictive algorithms that spot emerging trends. Given the advantages of being an early adopter and carving out a corner of the market before anyone else does, this is gold dust.
…But Don’t Know How to Handle It
The good news is that the data that companies need is coming. Almost six in 10 survey respondents (56 percent) said that they were collecting more data, while 66 percent said that they were generating more of it themselves. Forrester found that some companies are seeing their data volumes triple.
The bad news is that companies aren’t ready to handle the flood. Most businesses (70 percent) cannot keep up with what they have already, according to the Data Paradox study.
This failure to keep up has several ramifications. The first is lost opportunity. Data alone is of limited use. Its value is latent until an organization unlocks it via contextualizing analytics. Yet a lot of the data that organizations collect slips through their fingers without proper analysis; only 41 percent of companies are analyzing more data than they did three years ago.
The inability to analyze a gush of data has a chilling effect on business intelligence. Almost half (47 percent) of all businesses report that the quality of data-driven actionable insights is static or decreasing. Another 49 percent admit that innovation from their data and digital capabilities has stalled or decreased. Essentially, they’ll spin in the mire and waste untold resources until they distill data into understanding.
In the meantime, an inability to process data properly will cost companies more than opportunity. Much like water, when it goes unused it stagnates and eventually becomes toxic, creating new problems that can damage an organization. These problems include security issues: Almost two-thirds of companies (64 percent) complain that growing data volumes stop them from meeting security and compliance requirements. When potentially sensitive regulated data floods into your storage systems more quickly than you can handle, classifying and controlling it becomes a best-effort initiative. Suddenly, that data becomes a liability.
The Root Cause of Data Deficiency
Before companies can solve the data problem, they must understand what’s behind it. The Data Paradox study found several technology root causes.
The first problem is inadequate IT. More than half (56 percent) of companies still struggle with an outdated technology infrastructure that isn’t designed to ingest data rapidly. This manifests in several ways, from high storage costs as companies try to sock data away cost-effectively to inefficient data warehouses that aren’t optimized for the volume and variety of modern data.
Some problems are also cultural. Many companies still labor under manual data management practices that will never scale in line with data volumes. Those cultural problems extend from the logistical to the political. Turf wars within organizations contribute to business silos that affect 52 percent of companies. Democratizing data by opening it up to all within the company is crucial, but only 17 percent are attempting this.
Data management issues might manifest as technical problems, but many of the underlying solutions are also human. It takes skill and ingenuity to create the technology stacks and procedural rules for ingesting and processing data at speed—yet 57 percent of companies can’t find the people to manage their technical infrastructures. Data science skills are crucial in producing high-quality business insights, but 61 percent of companies lack them. Meanwhile, just 19 percent are recruiting data and technology talent to fill these gaps.
It doesn’t have to be this way. Companies can maintain data lakes without drowning in them. The answer resides in proper data stewardship. They must re-imagine both their culture and technology architecture to support a seismic shift in data volume and flow.
On the technology side, they must bridge the gap between legacy data management architectures and modern systems. This involves rethinking data not as a by-product but as a transformative asset. From a cultural perspective, it means redrawing or even eradicating internal data boundaries to remove choke points. Many companies have yet to make this transition, but there is hope on the horizon.
In the next blog, my colleague Nicole Reineke explains the significance of 57 percent of businesses planning to move to a data-as-a-Service model in the next one to three years–on the proviso, of course, that intent leads to action.