The importance & value of Trustworthy Data – Part 2

In Part 1 of 'The importance and value of Trustworthy Data', we looked at the reasons why it is essential to create a set of accurate, Trustworthy Data, outlining how to access and measure its value.  

In Part 2, we will provide guidance on the next stage; the cleansing and normalization processes of obtaining Trustworthy Data.

 

Cleansing and normalization

Once you have collected data from across your environment, the next key stage is to know how to structure the data to obtain value.

Data cleansing and normalization is the first step. Here, the raw, mass data is cleansed to provide a common, normalized view that can provide context-driven business intelligence. For example, the varying names for software applications and cloud services from multiple sources can be cleaned and normalized to provide a more consistent, accurate set of data. This is a critical stage to future ITAM success, as if you don’t have an accurate picture of what is in your environment, it is hard to know how to optimize it later on.

 

Duplications

Identifying duplicates is a crucial part of the cleaning and normalization process and involves processing huge amounts of deduplications in order to achieve a set of good data. Inevitably, during the discovery collection process, you will come across duplicate data records. When it comes to inventory processing workflow, you don’t want to be reporting the same asset multiple times, so here are some key questions to consider – Who has the best data? What has the latest scan date? Based on this, where shall I get the data from?

Investing in the process is huge feat, especially if doing it manually, but in the end, you will obtain a set of data that is truly representative of your environment.

 

Contracts and entitlements

Next you need to think about contracts and entitlements, and what you have the right to actually use. With a likely huge volume of contracts, that come with massive amounts of history, there will likely be similar duplication problems. The same cleansing and normalization process as used in discovery is essential to achieving an accurate view here.

You may also have to rely on the manual exercise of sifting through contracts to weed out additional information like renewal dates and different usage restrictions. It is again useful to capture this information in a consistent way, to normalize it and eventually combine data sources to obtain a meaningful set of information.

 

Recognition approach

Once the data is clean and normalized, every single change in the environment can be seen and used to your advantage. Taking a proactive recognition approach allows you to understand the percentage of your environment that has already been transformed into Trustworthy Data. So, when you have an important business decision to make, you will know it is based on accurate data.

Indeed, this insight helps with compliance, contract, consumption and control for ITAM by creating a more in-depth and holistic view of your ITAM environment. The more you know about your data, the more invaluable it will be.

Keep an eye out for Part 3 of ‘The importance and value of Trustworthy Data’, where we take a look at cloud management, forecasting and prediction.

To learn more, download our Trustworthy Data Guide or visit our dedicated Trustworthy Data page. You can also listen to our webinar Visualize your ITAM data

 

 

ABOUT THE AUTHOR

Simon Leuty - Chief Technology Officer

Simon is a founder of Livingstone Technologies. He currently sits on the Executive Leadership Team as Head Platform Development. Simon is responsible for the development roadmap of our client portal LUCE.

He has over 15 years’ experience of the Software Asset Management and Cloud markets, working closely with our growing customer base to help them take control of their software and cloud cost. Simon helps to eliminate unacceptable spend and enforce tight governance standards that keep them compliant, agile and secure.

 

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