With the recent ransomware attacks that have been in the headlines over the last year, many companies are reconsidering their data protection strategies to protect their company against these new, growing threats.
Today, companies are looking for solutions that can archive inactive data from little used enterprise applications. Those applications can be decommissioned, saving the company the expense of keeping them running for little payback. But the question not addressed early enough in the project is what to do with all of the application’s legacy data – delete it or save it (and where). By migrating the legacy data to an intelligent archive, organizations can preserve the value of legacy application data, ensure regulatory compliance, and address any legal concerns.
In my last blog, I discussed the connection between information management and data value. I laid out a math exercise showing how a lack of information management can dramatically affect productivity across the organization by calculating the actual cost of employees not being able to find information when the need it. This in turn causes employees to waste time looking for it, and when not found, being forced to recreate it. By estimating the number of hours of lost productivity as well as the fully loaded cost of the average employee, we are able to determine the total cost of lost productivity.
Taking this theme further, we can use the estimate of lost productivity hours and calculate total lost revenue – the revenue the company could have captured if enterprise-wide information management was more efficient.
Corporate data is what powers most businesses and so is a valuable business asset. In fact, you can say that companies employ information workers to generate and consume data for the betterment of the company. But can you actually calculate the value of data?
Employee’s annual salary, benefits, training, and corporate infrastructure all go into calculating the cost of information. On the other side of the equation, average revenue and profit per employee are measures of efficiency and productivity. To be successful, companies must generate more revenue (and profit) than total cost. And these are driven by how well companies manage their information.
In past blogs I have discussed the possibilities of machine learning and information management, i.e. predictive information governance (PIG) and auto-categorization to automate the management of electronically stored information (ESI). One of the challenges the information management industry continues to face is how to extend this machine learning capability to audio and video content.