As a key enabler for driving business insights and innovation, data is a company's most valuable asset. However, amidst the rise of data-driven decision-making and the shift to remote work, some users have turned to tools that haven't been sanctioned by the company — giving rise to shadow analytics.
Shadow analytics shine a light on the challenges of limiting access to data and analytics tools at a time when 55% of organizations believe that data availability leads to faster decisions. Yet only 24% reported using advanced decision intelligence technology and analytical tools to automate processes and help to make these decisions.
This shows there is an appetite for analytics — and companies that embrace its democratization open the possibilities for data-driven decisions. In the absence of that support, organizations can face concerns that compromise data and potentially regulatory compliance.
With this in mind, IT teams need to empower users across the organization to help them continue delivering on business goals without compromising the validity of their most powerful asset. After all, using data that is inaccurate, incomplete, inconsistent, or outdated has a trickle-down effect, as analysis of it leads to poor decision-making, which, in turn, negatively affects customers, revenue, and market share.
Mitigating the risk of shadow analytics lies in cultivating a data-driven culture that prioritizes access to data and analytics across the company while delivering easy-to-use self-service tools and providing employees with the training and resources to better understand how to protect data.
How to Detect and Mitigate Shadow Analytics
There are three steps companies can take to detect shadow analytics in their organization:
Step 1: Assess the organization's analytics maturity (that is, the level of sophistication and value that an organization can derive from its data and analytics capabilities) and the ROI of existing analytics investments. If the organization's analytics maturity lags its target and the ROI dips below expectations, there's a good chance it has a shadow analytics problem.
Step 2: Conduct an organization-wide survey to identify the tools being used and compare those results against a list of approved enterprise analytics technologies. This can reveal gaps between the approved tools and the actual tools being used or identify the reasons why employees resort to shadow analytics, such as a lack of access, ease of use, or functionality.
Step 3: Implement a data governance strategy that can prevent or reduce shadow analytics in the future. Data governance is a set of policies and processes that ensure the quality, security, and availability of data and analytics across the organization. It can include providing data literacy training, data quality monitoring, and data security policies to empower employees to use data and analytics effectively and responsibly.
How to Empower Enterprise-wide Adoption of Analytics — the Right Way
While it may be tempting to limit data access across the organization, doing so also hinders the company's ability to gain insights and a competitive advantage. That's why it's important for organizations to implement processes and solutions that help find the right balance between totally removing barriers to data and completely locking it down.
Several considerations can help here. They include:
- Identifying critical data assets and establishing data ownership
- Creating a data governance framework
- Defining data management and integration procedures and deploying tools that support these processes
- Investing in enterprise-grade data and analytics platforms that include features to deploy, govern, and manage self-service analytics at scale
- Investing in end-user enablement training and education about the risks of shadow analytics
It's important to remember that enterprise-wide adoption of data and analytics — giving employees the ability to access, analyze, and act on data without having to rely on IT or external vendors — might reduce shadow analytics. Organizations can eliminate data silos and inconsistencies and reduce security risks by providing users with a platform that solves all their data needs. At the same time, doing so empowers employees to make the most of data responsibly and for everyone to contribute to better business outcomes.
Jay Henderson, senior vice president of product management at Alteryx, has spent most of his 25+ year career as a product leader for companies that make software for analytics or marketing technology.