The value of data is no longer debatable. But the secret to unlocking that value still evades many organizations.
Only 44% of data and analytics leaders think their teams are effective in providing value, according to a new Gartner® survey. And business users are still struggling, too, citing accessibility issues and complexity as barriers to data use. Combine this with low executive confidence in data, and it’s clear that data challenges are ubiquitous.
Inability to effectively use data is a big problem, linked to revenue loss, risk of noncompliance, and increased costs.
Generating meaningful insights from data is complex work. Some of the main challenges businesses face in effectively leveraging data include:
Lack of clear objectives.
Poor data quality.
Inadequate analytics capabilities.
Most organizations have data originating from a variety of sources and departments. Attempts to collect and unify data in a single location often create unmanageable workloads for resource-strapped IT teams, leading to inaccurate and siloed data, long IT projects for simple business requests, and valuable insights getting lost in an abundance of information. And this data overload can quickly snowball out of control without an effective data governance solution in place.
With so many moving pieces, it’s no wonder that few organizations are able to produce advanced analytical insights.
[ Discover how you can eliminate data silos with an integrated data fabric: Watch our on-demand webinar. ]
Data governance consists of rules, policies, and procedures that help standardize enterprise-wide data practices. It helps improve compliance, ensure high-quality data, enhance decision-support, and reduce the costs of data management.
A data governance framework plays an important role in data management. It outlines how an organization collects, organizes, manages, and uses data.
Take, for example, a multinational financial institution. This institution would likely have challenges with data quality, security, and access. And they would handle a lot of sensitive information—like customer and financial data—that would need to comply with regulations across different countries and regions. A data governance strategy provides a foundation for addressing these issues, helping the institution standardize their data management processes, ensure data accuracy and security, and promote compliance with financial regulations.
Data governance and data management are related but distinct concepts.
Data governance is a strategic framework that defines processes, policies, and for handling data.
Data management refers to the operational activities and practices involved in the day-to-day handling of data.
Together, these activities are sometimes referred to as enterprise information management (EIM). EIM is a strategic and integrative approach for effectively managing an organization's information assets to ensure that data is treated as a valuable resource and used to its full potential to support business objectives.
Data that’s inaccurate or inaccessible is difficult to use for decision-making and reduces user confidence. This leads to less overall reliance on data and detracts from creating a data-driven culture.
And with all the moving pieces involved in effective data management, businesses can quickly lose sight of the reason why they’re doing it in the first place: to achieve business goals faster.
Clearly defined objectives and questions to be answered through data analysis.
Data quality management processes for data integration and accuracy.
Data security protocols and compliance with relevant laws and regulations.
Data accessibility that enables employees to leverage data effectively.
A culture of data-driven decision-making and collaboration between departments
Only 23% of organizations identify themselves as being data-driven, according to a 2023 data and analytics survey by NewVantage Partners.
But data governance alone isn’t enough to help you realize the full potential of your enterprise data. Even the best framework will only provide part of the puzzle. That’s because getting value from data comes down to a combination of a good data governance framework, people's ability to use and rely on data, and the right tools to get the job done.
A data fabric can support and even improve data governance by providing an easy and accessible way to unify and connect data across your organization.
[ Want to learn about how data fabric works and its real-world benefits? Get the eBook: Data Fabric Guide. ]
The Appian AI-Powered Process Platform includes a powerful data fabric for connecting and unifying data across your organization to support faster, better business processes.
Integrate data from any system, implement row-level security rules with low-code, and eliminate manual tuning with Appian’s patented architecture for automatic data optimization—all while reducing integration maintenance costs by up to 70%.
Learn about Appian's integrated data fabric capabilities. Watch our on-demand webinar.