Data governance is no longer optional for enterprise organizations. Aside from complying with new regulations, such as the General Data Protection Regulation (GDPR), organizations are finally realizing the value of data as an asset that needs to be protected, managed and maintained to increase asset value. But just because businesses understand the value of data governance, doesn’t mean that enterprises are confident in their abilities to execute on it.
True Data Governance Software Doesn’t Exist
Data governance is an organizational discipline that requires a vision and strategy, appropriate people resources and organizational structures, processes, data, and technology to operate. Because data is a digital asset and has mostly been managed within the realm of IT, organizations are quick to look at technology, expecting to find data governance software and solutions that will solve all their problems. However, technology is only part of the solution.
True data governance software is a myth; instead, organizations need to invest in software that supports the process of data governance. IDC calls this data intelligence software.
What is Data Intelligence Software?
Data intelligence software is a collection of capabilities that helps organizations answer and manage the six fundamental questions about data:
- Who is using the data, who created the data or asset, and who is responsible for it?
- What does the data represent, what is the data being used for?
- When was the data created, when is the data being used, and when will the data expire?
- Where is the data in the organization, and where is it being consumed?
- Why does the data exist, why is the data being persisted, and why is it being used?
- How was the data created or captured, and how is it being used?
Additionally, data intelligence software adds another dimension: relationship. What relationships are inherent within the data and between the people that are generating and consuming the data?
What Makes Data Intelligence Software Different
The answers to these questions are what informs and guides use cases around data governance, data quality management and self-service data. To collect these answers, organizations must harness the power of metadata that is generated every time data is captured at a source, moves through an organization, is accessed by users, is profiled, cleansed, aggregated, augmented and used for analytics for operational or strategic decision making. Data intelligence software goes beyond just metadata management, and includes data cataloging, master data definition and control, data profiling and data stewardship.
Organizations also face internal obstacles when advocating for data governance. The word “governance” is often a stumbling block to gaining executive buy-in, either because it implies constraints, or because past governance efforts have failed.
One of the reasons these past attempts at data governance have failed is that intelligence about data was created and maintained manually; another is that the variety of intelligence required to answer the fundamental data governance questions has not existed before now. A recent IDC survey uncovered that spreadsheets, custom software, documents, and word of mouth were among the top most frequently used methods of cataloging data. Manual processes have never been able to keep up with the pace of change, in business or technology; and now in the age of digital transformation, change is even more rapid and constant, within increasingly complex technical and business environments.
Data intelligence, on the other hand, is a fresh and almost intriguing term to organizations because it doesn’t imply constraints, but promises opportunity – to learn more about the data itself and how the organization uses data. Furthermore, data intelligence moves beyond answering the fundamental data governance questions and, when combined with the content of the data itself, may yield a whole new level of insight in this age of big data and digital transformation that has not yet been possible.
Data is at the heart of digital transformation and deserves specific attention as a critical building block of the digital platform. Organizations are facing more complex ecosystems and business environments within an expansion of data characteristics, types, constructs, behaviors, domains, social contexts, and hybrid technical environments. Historically, data professionals have focused on the three V’s of big data: volume, velocity, and variety. These big data characteristics still exist and have become ingrained in data operations, but to get value from data in the era of digital transformation, data professionals need to shift their focus toward the three A’s: awareness, augmentation, and automation.
Data intelligence software provides organizations with an awareness of where their data is, who is using the data, and why and how the data is being used. Data intelligence software also augments data with technical, semantic and business metadata, adding key knowledge elements so that data and data usage is better understood. Data intelligence demands automation of data discovery, definition, duplication, consistency, usage, and protection; manual processes no longer cut it when it comes to data governance or management.
Want to learn more about data intelligence software, and how it empowers data governance and data quality management? Explore IDC’s Data Integration and Integrity Software:
