With businesses collecting huge volume of data and implementing AI technologies for insights, data governance too must shift gears to protect the organization from risks within and outside. This calls for a need to automate data governance. This blog post outlines the need for automation in Data Governance and the processes you can automate to strengthen your DG.
Stepping into an AI-led future, it is important for businesses to be clear about where they stand when it comes to data ethics. This blog post outlines the complex relationship between data ethics and AI, and key guidelines to build trustworthy AI systems.
From master data management being an important part of your data governance strategy to defining your master data and the processes for managing it, we have come to the end of the three-part series. This blog talks about the best practices and steps in creating MDM strategy for efficient business outcomes.
As organisations grow, there is a need to manage data more efficiently — data system becomes efficient when its master data and the processes for managing it are defined clearly, as per business needs. This blog tells us how data governance is different from master data management; how master data can be stored and transmitted across the organisations without any ambiguity.
By including master data management as a part of your data governance strategy, any data related ambiguity or risks can be mitigated. This blog tells us why master data management is important and should be a part of your data governance strategy.
It is important for any organisation that they choose the right tool to manage their data governance programs. Whether the tool is developed in-house or it is a third party tool, there are some key features that the tool must have. This will strengthen the structure, security, interoperability and many other complex depths of data governance. This blog talks about some of the best practices followed to manage your DG programs efficiently and why considering third party DG tools could turn out to be a better option for your organisation. We’ve also recommended some tools based on our experience.
In this era of cloud computing, organizations try to move most data and processes to the cloud barring those restricted by regulatory and performance constraints. With data passing between multiple systems and environments (on-premise and cloud), it is natural to have concerns about data security. A well-formulated data governance policy which includes these integrations will dispel the security concerns. This blog tells us the integration areas that needs to be covered in your data governance policy.
A lot of organizations are becoming data-led and this means a data governance policy is a hygiene factor. However, a strong DG policy should also include disaster recovery. A data governance policy with clear contingency plans and escalation matrices defined for crises, will provide early responders with all the information they need to move ahead. This blog explains the considerations that an organization needs to make to include disaster recovery as a part of the data governance policy.
Handling sensitive data, highly regulated industry, and lots of local and international laws to adhere to makes data governance in financial industry a complex one. In this blog, we put our heads together to determine the hygiene factors in setting up a data governance policy for organizations in financial sector.
Everything is turning digital in healthcare – medical histories, telemetry, remote diagnostics etc. No part in the healthcare ecosystem remains untouched by digital. This in turn is leading to an explosion of data. But to make sense of the data, it should be available in the right format, at the right time and to the right people. And this is why a strong data governance policy is an absolute must in the healthcare industry. Here are some points you should keep in mind while setting up a data governance policy in healthcare.