Why Dark Data Should Be in Your Master Data Management
Does anyone actually know what dark data is or why it should be a part of your master data management?
Very few people understand what it is and how it works. Dark data is simply data that is in the shadows, meaning it cannot be simply exported to your database like regular data. It’s generated every day by systems using networks – it just isn’t usable in any immediate way. Consulting companies such as Gartner, Forrester, and IBM describe dark data as a collection of a ton of data just hasn’t been figured out yet. And though dark data may not be easy to use, at first, when processed properly, it can unleash a world of new potential.
Dark Data and Master Data Management
Some of the biggest companies in the world generate a huge amount of data every day. The whole purpose of storing this data is to eventually use it. However, most companies admit that the percentage of data on their networks actually used in any real analysis hovers around 20%. The rest of their memory is clogged with this dark data that serves no immediate or apparent purpose.
Companies are afraid to lose any potentially useful data
Because companies are anxious about throwing out any data, they keep a lot on hand even if it is not processed or easily accessible, this means they do not use any method or technology such as Master Data Management. This creates both a lost opportunity and wasteful costs. Ideally, a company will be able to triage all stored data and discard the dead weight accordingly. This requires systems that can screen for useful data and automate disposal of the rest. Once data is designated as useful, the analytics team should come in and find a way to process it for information.
Eliminating unstructured data
As said above, very little data stored on company servers is actually used and monitored. Adding unstructured and semi-structured data to a database is a waste of time and money, therefore the importance of Master Data Management. It’s better to weed through the dark data and extract what can be structured. Then, abandon or separately store this unstructured data to speed up the network and lower storage costs. It makes more sense to spend valuable resources on information that is actually useful.
Analysis of Real-Time Data
Real time data analysis provides the most value of all. That’s because real time work focuses on new data in the company’s data pipeline and reduces a need to store excess data for long periods of time. For this reason, most companies strongly prefer the use of real-time information over stored data.
Use Data to Improve Sales, Production and Distribution Strategies
Unless data can be used to improve production and sales, it is not relevant. There is no need to store dark data on a main operating system. Create a separate file for this data that won’t slow down or compromise your current data. By implementing immediate and real-time analysis of relevant dark data, a company can identify what information is useful and what is not. The firm can then use this information to accurately forecast sales figures, production, and distribution.
Reduce the Risk of Pertinent Info Being Lost in Dark Data
If your system is going to archive dark data, it makes sense to extract useful information immediately rather than risking it getting lost in the dark data. This will also reduce storage issues.
Companies need to devise a strategy of isolating useful information in a proactive way. Once important data is identified, the rest of the dark data can be either discarded or warehoused separately. Once this is accomplished, companies will see a big difference in their bottom line.