Adopting a data governance strategy for your newly flowing data lake necessitates some fundamental changes. In this blog post, we discuss some of these changes across people, process and technology dimensions.
Today, with businesses dealing with massive amount of data, it is highly important that their technology landscape include a data lake. In this blog post, we share with you what, why and how to get started with data lake implementation.
To help you build a clear and actionable implementation plan, we present the 4-phase Product information management implementation guide.
Setting up data governance policies involves a lot of stakeholders. In this blog post, we tell you why the marketing team should be a part of data governance conversations and how businesses can find their golden mean when data accessibility becomes a matter of concern.
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.
For AI systems to function effectively, you need good quality data — a robust data governance. This blog post outlines how you can prepare your data for AI and the key data checks before investing in AI.
If you’ve just begun your PIM initiative, picking the right tool might be a bit overwhelming. In this blog post, we list a few of the important questions, the answers to which help you pick one perfect PIM solution for your needs.
A PIM core working group gets the idea on the drawing board to a functional, steady-state deployment. However, guaranteeing the continued health and utility of PIM requires the attention and leadership of the Data Governance Council (DGC).
The quality of data is a key factor in the success of your PIM implementation. In this blog post, we present a 5-step framework for successfully conducting a data quality audit.