Big Data is Dead
Originally posted on Altavia.com on Aug 19, 2016 by John Jackiw |
In every industry across the globe, data is gathered, collected, sorted and stored. In the words of one of our recent clients “we have a data rich environment, but we are decision poor.” All of modern humanity is busy collecting “BIG DATA”. Manufacturing has been one if the biggest collectors of data. The Industrial Internet of Things (IIOT) and the Internet of Things (IOT) bring high levels of connectivity ensuring the big data problem evolves even faster.
The problem is we still live in a data segmented world. Users want and are struggling with decision making. No one likes to do the hard work of analysis. With “so much data, so little time” users still have deadlines for decisions right or wrong. Making most users like Veruca Salt in my last blog. They want the systems to make the decision for them or point them in the right direction. Businesses, departments and people are hanging in the balance or worse. As a result, they use guessing and rules of thumb because they cannot see the decisions in the data.
The solution to this problem is bridging the gaps and closing the feedback loops. Helping our businesses act more as an organism whose separate parts work together. The true drivers to this solution are answering directional and operational questions. Questions where finance, health, safety, and operational data alone can’t solve the need. These solutions are available through connected value. Connected value is the ability to determine and balance product demands with operations and capacity. Understanding which products to retire and which products to introduce, which customers are stars and which are dogs and should go to competitors, combined with a clear and auditable understanding of the costs and marginal impacts of the scheduling decisions. These are just some of the big decisions that drive business success, and it’s not big data.
John, totally agree, data is just that data, it's big because of it's volume and velocity. however, without a strategy of actually why and what you need the data for it is just disk space taken up with stuff. Being able to derive intelligent action is what is required from all of this data such as What is the product actually being used for? are there interesting use cases that you could exploit, what features delight? What features aren't even used? what customers to be want to keep and what customers we need to let go? when do they want it and how quick?