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VOOZH | about |
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VOOZH | about |
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Data management strategy is boring, right? More to the point, it’s a solved problem. The relational model, SQL, and data warehouses date to the 1980s. I mean, what could be less strategic than the “what,” “how,” and “why” of an enterprise data management strategy? Build some pipelines and ETL jobs. Define a relational schema to cover big chunks of the business and drive analytics off the data warehouse OLAP cube queries. Easy right?
Not so fast. There’s no one to blame here since the conventional approach to data integration — based entirely on moving and copying data and leveraging data location in the storage layer — was until very recently the only game in town. The way we integrate data caused the data mess to begin with and the longer we stick with it, the worse the problem will become.
CDOs, CAOs, and CIOs (CXOs for short) are empowered to use the enterprise’s most irreplaceable asset — its unique data universe — to gain deeper insight faster than ever. That insight is the primary ROI of data-driven enterprise transformation. Smart CXOs know that enterprise data strategy is key to enterprise transformation, but the smarter a CXO is, the more stressed they tend to be.
Why? Because they know that enterprise data is a mess! It’s big, diverse, and ever-changing. Even worse, it’s fragmented, siloed, disconnected. Worse again, the hybrid multicloud’s proliferation of data environments is exacerbating these other data problems.
So CXOs must impose order upon chaotic data. But conventional data integration systems aren’t ideal for this task because they rely on two outdated assumptions: (1) most data is structured and tabular; and (2) the only way to connect data is to physically consolidate it by moving and copying it into analytics systems.
The key to unraveling this mess is to modernize the enterprise’s data and analytics infrastructure. By transforming an outdated infrastructure with updated approach, data can power analytics more reliably, more quickly, and with greater effect and insight.
The most reliable way to transform the enterprise is by changing the way the enterprise manages data and, specifically, changing the way the enterprise integrates disparate data.
Enterprise Knowledge Graph (EKG) technology offers a way out of this morass. EKG is a family of data management technologies comprising semantic graph data models, data virtualization, query federation, and semantic search that provide a real alternative to the conventional approach, which is entirely reliant on physical consolidation of data in the storage layer.
EKG approaches to data integration are “kissing cousins” of graph database approaches to data storage; both categories use the graph data model, for example. But they are ultimately targeting a different problem, outcome, and ROI. The key goal of EKG is to relocate the “moment of integration” from the storage layer, based on physical consolidation, to the computational layer, based on semantically-enabled, contextualized business meaning.
If analytics systems know what data means to the business, they can avoid the costly and wasteful data movement and replication and, instead, get to insight faster by achieving unprecedented levels of data mastery. Both human and AI-powered systems for generating insight are massively upscaled when they are fed clean, contextualized, and total data inputs.
In short, a system that knows more can see and predict more. The enabling strategic insight for CXOs is that it’s data integration that determines how good analytics engines can be. The primary impediment to accomplishing that linkage between algorithms and data mastery is existing data integration approaches that only work by moving and copying data; thus, increasing rather than decreasing the siloed nature of enterprise data.
CXOs are in some ways the most visible representative inside large enterprises of what is, after all, a deeply felt human need to make sense of the world. We try to accomplish this in all parts of our lives including in our professional careers. It’s far more satisfying emotionally to work in an organization that uses data effectively to chart the way forward.
But there are some pressing, contemporary drivers of urgency, too, not just an inherent human need. The pandemic radically accelerated awareness of data’s importance for both social and commercial resilience, especially in the face of repeated supply chain shocks and disruptions.
But there was another factor too: most of the enterprise world has taken to working from home, operating complex orgs from the relative safety of social isolation, despite the additional challenges such isolation creates. The future of work has become problematized across the enterprise world and that raises questions and urgency around the future of information strategies to support the future of work.
Systems that are smarter, more data-enabled, and more aware of data context are obviously relevant as we try to figure out what the near-term future holds for working together.
Finally, the predicted wave of modernization around AI-powered systems is starting to show up, as some of the challenges of pandemic increased investment in these areas. Again, this wave of uptake for advanced insight systems is happening, though it is not yet widely distributed.
That means the CXOs of every other business have to focus on the connection between insight, analytics, and data like never before. Gartner projects that EKG and related technologies will be powering contextualized decision-making systems inside 30% of enterprises globally by 2023.
While we know correlation data isn’t necessarily evidence of causation, it’s not always a coincidence either. And there are some very suggestive correlations hereabouts. For instance, the leading technology companies in the world are also all data-driven companies: Facebook, Apple, Google, Microsoft, LinkedIn, Amazon, etc.
So, first, there’s a strong correlation between enterprise value and data dominance. But there’s a second correlation, too; all of the top-tier tech companies have existing and ongoing investments in EKG. That’s because, by and large, the missing link between enterprise value creation and AI/ML and other advanced analytics is a sophisticated “supply chain” for data. Every major and second-tier technology company has built or is building EKG systems, both for data integration and for analytics.
But it’s not just the top-tier companies. Increasingly, banks, big pharma, manufacturing, automotive, and other more conventional sectors are adopting EKG to modernize analytics by revising their data integration infrastructure. Some examples include:
With the data landscape getting bigger and more complex every year, CXOs in every industry need to leverage data to gain an upper hand in creating value. Taking a cue from leaders like Google and Amazon, CXOs should consider EKG as the go-to data architecture to supercharge their data supply chains to deliver better analytic insights faster and cheaper than ever before. To conclude, here are a few concrete steps to get started with EKG:
It’s clear that the future belongs to the most agile, data-driven organizations that thrive on connected, contextualized data powering analytic engines of insight. Success will belong to those companies that can modernize their data and analytics infrastructure, and solutions like knowledge graph technology will help speed time to insight, enhance data lake productivity, and accelerate analytics to gain a competitive advantage.
