High quality product data is one of the key factors that will determine the success of your PIM implementation. But how do you know if the quality of your data is good or bad? In this blog post, we outline the six important dimensions to assess your data quality.
Your PIM system will function efficiently only when the quality of input data is good. This blog post talks about identifying your data assets for data cleaning and preparing it for PIM implementation.
Bringing together the right people to form a PIM team is the first crucial step for a successful PIM implementation. In this blog post, we discuss the key stakeholders you consider include in your PIM team.
The most important ‘data’ in a product company is the data about products — SKUs, price, size, quality and the like. This product information and its strategic management can just be the difference between success and failure.
In this blog post, Shyam of I&I Software presents five irrefutable reasons a product company needs PIM.
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.