Following on from my previous blog on the topic of Enterprise Information Management, I now focus on two specific areas of EIM, namely data governance and data quality management, which should be key focus areas for businesses in 2017.
Happy 2017! I trust you have had a wonderful start to the year. It’s always good to take a break, but I am glad that I am back exchanging thoughts and opinions about the business intelligence and analytics industry.
Just a quick recap, in my previous blog, I touched on the topic Enterprise Information Management, where I examined some of the reasons why organisations get frustrated when it comes to data surges – and offering my views as to why Enterprise Information Management (EIM) is key in alleviating these frustrations.
The reality is – information will continue to drive business decisions and data will continue to grow at a rapid pace – which, can be over-whelming for any organisation. Just thinking about the data created daily – we are told that this is about 2.5 quintillion bytes of data¹. So, how can EIM help, when it comes to this data?
The article entitled Why enterprise information management is a key to analytics success gives a good outline of these two key areas of EIM, namely Data Governance and Data Quality Management, as far as unlocking the value of data is concerned.
Data Governance
This is becoming more of a focus for many businesses across the globe, especially as regulations around data are constantly changing. As organisations find themselves dealing with more data, focusing on a data governance strategy will not only ensure good data quality standards, but also better compliance to these regulations. It is the EIM strategy that focuses on this element of data and can assist the business in getting the process of data governance right. However, the key to get this right is to ensure commitment from the top down.
Data Quality Management
What good is the data if employees within the business (who need to data) don’t trust it? Quality of the data ensures that the data adheres to the key attributes that guarantees user adoption. In the absence of strong quality checks that should be applied across all data assets, and which should be published to all interested parties, users will likely not trust and use the data. A strong EIM approach focuses on the quality of the data, which in turn ensures the data is beneficial and useable for the different classes of users interested in it.
While these are only two of the many areas wherein EIM can assist the organisation in effectively managing its data assets, I cannot stress enough that a shift to prioritising these aspects will be beneficial. These are crucial areas to get right within the larger process of successfully devising and implementing an EIM strategy.
References
Why enterprise information management is a key to analytics success
