Data may be at the heart of all digital engagements, but most enterprises are still behind the curve when it comes to effectively identifying and managing it. That’s the takeaway from the latest survey of 419 enterprise executives from BARC, which finds continuing challenges with identifying and surfacing the data assets needed to succeed in today’s digital economy.
While most organizations intend to digitally transform themselves, few are taking action at this time, the survey found. Ninety percent, for example, agreed that information has a high priority in enterprise decision making. But only 25% stated that decisions are predominantly based on data at this time.
“In principle, everyone agrees that data is important, and its targeted use can make a decisive contribution to improved company results,” the survey’s authors said. “But the fact is that data use is far too difficult today. Investing in improvements is not usually a real priority. Decision makers in particular have little insight into their data-related problems and the benefits of potential investment.”
The challenge is that convincing decision makers to invest in data is a chicken-or-egg issues, the survey’s authors added. Close to two-thirds of respondents, 65%, agree that the value of data is not sufficiently transparent, but only 23% believe that creating more transparency in this area is an important approach to improving the handling of data.
The survey report identified “best-in-class” companies that are functioning as data-driven enterprises. These leaders “have already created transparency about the value of data and what can be drawn from it. They have thus created the basis for convincing decision makers to invest.”
The Value of a Data Catalog
A data catalog will help meet the requirements of an enterprise seeking to run on data analytics, but this demands buy-in from business users. Tellingly, 60% of companies state they “waste a lot of time” asking the same questions about data or repeating work. The top three approaches to improve the handling of data include providing more information about data (59%), defining clear responsibilities (57%), and providing a business glossary (56%). “Data catalogs help meet these needs,” the survey’s authors pointed out. This type of technology is in use or planned by 72% of the companies in the survey. A majority of the “best-in-class” companies identified in the survey, 57%, already have a data catalog in use.
Data democratization is also on the table for many enterprises. A majority of respondents, 74%, stated that they already analyze a lot of data, “but conditions are not in place to use this knowledge in real-time processes.” In addition, 58% said their data governance processes are still too immature to deliver data analytics in a widespread way. True data democratization requires a “new deal” on how data is handled across the enterprise, according to the survey’s authors. “Data producers need to understand and take into account which data-related needs data consumers have. At the same time, data consumers must understand the requirements and restrictions of data production processes. Enterprises need a new deal between data producers and data consumers that effectively addresses the top three challenges to improving data handling—time spent, a lack of transparency of data value, and insufficient data quality.”
Automated Data Management
Best-in-class adoption of automated data management using machine learning offers a potential benefit. Thirty percent of leading companies have already taken this approach, and 43% are planning to do so.
Data quality is another issue that continues to hamper digital transformation efforts. “Insufficient data quality drives the need for individual data preparation, inevitably leads to an inflation of data silos, and undermines any governance efforts.” Seventy-two percent of respondents agree that business users lack the time to develop new ways to use data, and 62% agree that business users lack the competence and skills to work with data.
“Enabling a data-driven enterprise requires a fundamental cultural change driven by the executive level,” the report authors stated. “Technology is an enabler but not the driver for data-driven working. Individuals adapt to the corporate system. Corporate culture and organization must therefore be realigned. In this respect, the widely adopted bottom-up approaches to digital transformation are very limited in their impact. Measures such as establishing clear responsibilities for data in the line of business, investing in data literacy by carrying out targeted staff development and training, and developing the corporate data culture from ‘need to know’ to ‘right to know’ require strategic orientation and active support by the executive level. You will also need a cross-functional team of mid-level directors and managers who have a vested interest in becoming a data-driven organization.”