How To Fight Shadow Data Management Practices
“Shadow IT” refers to technologies adopted without IT involvement, which invokes several responses depending on who you ask. The IT department may tremble, while developers may dismiss it as a shortcut to getting robust workflows.
Today, we must have a similar conversation on a related topic: handling shadow data management. Left unchecked, this is an issue that will create many headaches, ranging from insufficient return on investment (ROI) to security vulnerabilities in AI projects.
Let’s discuss shadow data management. Left unattended to, this is an issue that can create many headaches, ranging from security vulnerabilities to inadequate return on investment (ROI) for AI projects.
Shadow Data Management And Its Root Causes
Shadow Data management occurs whenever analysts or data scientists copy data from the main IT database to run the analysis in their environments and with their own tools – many of which have not undergone the rigor of IT approvals. This management poses many of the same threats as shadow IT (such as increased attack disrupted or vector workflows). Also, it increases a company’s liability if the delicate user information is included in the “shadow” database.
To handle the challenge of shadow data management, it is crucial first to explore the root causes. After all, machine learning (ML) engineers and data scientists are not violators of rules dedicated to generating chaos. Rather, they are driven to practice shadow data management because their approaches do not meet their technology needs to risk and data governance.
Be Willing To Build Governance Models And Net-New Risk
Every organization needs data governance and risk policies to practice responsible data management. However, leaders need to know that trying to force existing models in the new AI era will not work. For IT teams, keep in mind that data management infrastructure is not the ultimate goal but rather a means to meet both data scientists and the company’s needs.
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To adopt AI initiatives, companies need to gather more data than before, which is inherently risky. IT groups should work with their data scientist to develop new infrastructure to manage this risk adequately.
Avoid Buying Your Way Out Of Challenges With New Tooling
It might help pay attention to technical discussions from others in your industry about addressing machine learning or data engineering challenges. Remember that you do not have to enact the same solution because each organization has its own peculiar challenges. Rather, please focus on the issues that those teams identified and how they approached them. The actual implementation may be different for you, but many of the basics will be the same.
Shadow Data Management Generates Technical Debt
Data scientists need to bear in mind that, like short-term successes in software development, shortcuts lead to the accumulation of technical debt in the long run. And someday, someone will pay that debt. Even if it will be a major investment, it is very worthwhile to continue and open communications with the IT group about proper data management practices and fresh solutions to be implemented.
Better Return On AI Investments – An End Result
Organizations need to bring their data scientists and IT teams together to strategize data management to absolutely support each party’s needs. Only then will they see the full benefits of their investments in AI. When data scientists are better equipped to do their job with the right tools and infrastructures, they can produce more efficient results.