The need for proper tool to organize and understand the underlying patterns in machine data exists. From what we’ve been hearing, that’s gripping the Chief Data Officer’s mind. Why?
From a data strategy point of view, we often hear whether it makes sense to store and build a historical archive of a particular piece of data. The cue to make a firm decision comes from making an assessment on the data’s value for future business. With storage costs spiraling downwards and the availability of data discovery tools now seem to justify the push to normalize data preparation, ingestion, and historical data archives as a crucial decision-making hinge point. That is even before we get into product design.
What’s enabling this to happen? Who now is able to access the value of a particular piece of data?
The tremendous advances in data preparation tools specifically targeted at quasi-technical-business users who need more than the traditional business intelligence provide but don’t have the specialized skills of advanced data scientists. They need easy, highly visual, self-service data interaction capabilities for machine log data.
For example, a data modelling or schema traditionally was a highly sophisticated job with reliance on a core group of people. That has changed. We already see this happen in the consumer-side of the analytics space. Machine data is now getting the same treatment as business users take to the helm of constructing data models from raw data previously inaccessible - ‘Dark Data’. Read machine logs.
This perhaps is the single most significant advance that allows traceability through end-to-end of where the data lies. In addition, it also means successful scaling from the business peers perspective in terms of what data I have and state it is in.
So, what does this data assessment tool look like in the Chief Data Officer’s toolkit?
A data ingestion tool that allows business users to discover the value of the data even before we get into product design is the first clear benefit. For example, a complex event and sections log that contains thousands of rows of technical data should be made accessible to a non-technical user to easily dissect and structure it. This eliminates data scientists from having to structure and organize that data while allowing them to focus purely on analytics. A business user brings that crucial market context while structuring the data. That’s good advantage to have, am sure you agree here?
User interfaces are much easier too, that is somebody not familiar with SQL and/or relational databases for example can do tremendous work based on simple drag and drop interactions. So, when new pieces of machine log lands, the tool should be flexible enough to help business users add the organizational context and not just the technical perspectives.
Can Glassbeam Studio be a key piece in the Chief Data Officer’s toolkit to add that important business perspective to the data preparation activity? Check out the Studio datasheet here.