For many organizations, data is the single greatest resource already available today, and managing data better is the greatest untapped opportunity to unlock new value and drive new paths to innovation. Indeed, effective data management is a prerequisite to harnessing many emerging technologies. Smart cities, self-driving cars, wearable technology, and beyond are all helping define new models of doing business, driving innovation, and enabling human progress. For organizations that want to be on the forefront of these developments, modern data management is 'must have' not a 'nice-to-have'. The paradigm has shifted, and to be successful as technology and customer experiences continue to converge, organizations must build on a rock-solid data management foundation.
But what does market-leading data management look like, and what does it mean to effectively extract meaningful intelligence from data? It means that organizations must achieve a level of data management maturity where decisions are not made just by individuals, or based solely on past experience, or even the latest market trends, but on a confluence of trusted data collected from all these of sources and combined and analyzed in concert.
To measure how well organizations are positioned to make the most of their data, ESG surveyed 500 IT and business leaders with a strategic focus on their organization's data management practice. All organizations were enterprise-sized (i.e., employing 1,000+ individuals).
Data management and analytics are ubiquitously cited as business imperatives: 89% of organizations say data management and analytics is one of their top ten business and IT priorities for the next 24 months. This is not surprising as 80% of respondents do not have a great deal of trust in the veracity of their data today. But as we will see in the research data, while data management/analytics importance is nearly ubiquitous, only 8% of organizations have actually implemented top-tier data management capabilities. There is a gap between current and optimized data management capabilities for nearly all organizations today.
Why? Simply put, organizations are betting their businesses on their data. In the last 12 months:
of organizations have used insights and analytics from their data management practice to develop a new product or service.
of organization have used insights and analytics from their data management practice to make a major strategy adjustment.
In fact, at a majority of organizations (58%), most business decisions are not approved or acted on until supporting data is provided and vetted. However, that begs the question of if organizations are able to use all their data (from customer data, to data collection at the edge, to machine data, etc.) in concert and effectively. It is likely most organizations actually underuse their data without even knowing it. What is clear is that without a functional data management practice, many organizations will be strategically paralyzed.
While organizations understand the importance of operating a mature data management practice, they also face four key roadblocks that must be overcome:
Data is growing quickly; ESG's research pegs the average organization's data growth rate at 27% annually. The prevalence of remote work scenarios, as well as the pervasiveness of emerging Internet of Things (IoT) and data-generating edge technologies, exasperates existing issues that organizations have with data silos. Siloed data can't be utilized to drive a business outcome and many organizations are finding it hard to locate all their data, consolidate it from different sources, and provide the right people with access to it.
Not only do remote work scenarios create silos, they also make organization's data footprints more distributed. In this survey, only 7% of respondents reported their data footprint was completely integrated or centralized, and even those respondents likely have a lenient definition for what that means. This trend has been established by numerous initiatives like IoT projects and intelligent industrial manufacturing plants. To analyze data at the edge in near real time, organizations must deploy infrastructure at the edge, potentially adding complexity to their environment.
For many organizations, the elevation of data management to mission-critical importance is a recent development, which means that organizations must adapt. To effectively drive change, organizations need data leaders that can effectively drive strategies, evangelize projects, secure funding, and educate the rest of the C-suite why data management matters. Once these leaders are empowered, the people challenge is not solved, as the newly appointed chief data officer must build out a team of skilled but scarce data scientists, which is problematic for many organizations.