- August 26, 2020
- Bill Tolson|
- Regulatory Compliance|
- records management|
- Data archiving|
- Cloud archiving|
- Defensible Disposition|
- Healthcare Data|
Over the years I’ve written a lot about the benefits of enterprise file consolidation, i.e., storing and managing unstructured data in a common repository.
In fact, most companies still have data spread around the enterprise in distinct stand-alone data silos (usually unmanaged at the file level) including custodian computers, removable media, personal cloud accounts, file systems, email systems, and SharePoint servers (to name just a few).
Companies run the risk of experiencing eDiscovery and regulatory issues, the inability to run effective data analytics processes, and lower employee productivity.
The basic principle of responding to an eDiscovery request is to find, secure, review, and turn-over all potentially responsive data to the opposing party. If all relevant data is not found and turned over, the potential exists of receiving an adverse inference decision - the Judge instructing a jury that potentially relevant information was not turned over (and potentially destroyed) because the defendant did not want the jury to see it. In other words, the data was not turned over because it would have been potentially a smoking gun. In most cases, an adverse inference means that the case is already lost and the only question is how many zeros will be included on the judgment check.
This situation does happen on a regular basis because the defendant made a good faith effort to find all relevant data but didn’t because of the complex data storage environment. Now, because of the 2015 update to the federal rules of civil procedure (FRCP), it is harder to get an adverse inference due to missing or inadvertently destroyed data. Now, the plaintiff must show the data was destroyed or not “found” on purpose, meaning good faith mistakes will be overlooked, however, it does not remove the possibility completely.
Consolidating, indexing, and managing all unstructured data in a single data silo greatly simplifies the collection of legally responsive data in that it provides only one location to search, only one search app is needed, and search speed is greatly enhanced. It also reduces the risk of custodian spoliation because one litigation hold can be applied for all relevant data.
Much like the data silo issues with eDiscovery, relying on numerous data silos raises the risk of regulatory non-compliance. Data retention regulatory compliance requirements require companies to 1.) be able to react quickly to a regulatory information request and 2.), turn over all regulated data (subject to the specific information request) with relevant data spread all over the enterprise. Granted, larger organizations will have moved much, but not all of the compliance “records” into an enterprise content management system (ECM) however, because most companies rely on employees to determine what are records and to manually move them into the ECM system, many regulated records do not find their way into the ECM system. This issue is the main challenge of records management for regulatory compliance. Failure to fully respond to an information request can cause large fines, loss of business, and in some cases – jail terms.
Again, consolidating, indexing, and managing (i.e., apply retention policies with access controls) all unstructured data in a single data silo (ECM systems are not architected to manage many types of unstructured data) greatly simplifies the finding, collection, and presentation of requested data. Consolidated data provides one location to search, one search app to be used, and much greater search speed. It also reduces the risk of inadvertent custodian deletion because one litigation hold can be applied to all relevant data.
Data analytics is the science of drawing insights from raw data. Data analytics techniques can reveal valuable trends and metrics that would otherwise be lost in the mass of information across the many data silos. The result of data analytics can then be used to uncover sales opportunities, additional marketing focuses, and to optimize processes to increase the overall efficiency of a business.
Data analytics has become a major focus for organizations to uncover value from their under-utilized data stores. Most data analytics apps operate only in a single data repository for example file system content, email, or SharePoint data. The issue companies face is this; because their unstructured data is spread across numerous data silos, it's difficult or impossible to run analytics processes on all of their unstructured data across all of the repositories. Consolidating unstructured data into a single repository simplifies analytics processing and ensures a more insightful result.
You may be asking yourself “what does separate data silos have to do with employee productivity.” In reality, employees searching for content to reference, reuse, or just for CYA purposes consumes a great deal of time in corporations. Over the years market research firms have tracked this loss of productivity; how many times per week does an employee search for shared or older data for reference, to conduct research, etc. How much time is spent looking for the data, how often do they find it, and if they don’t find it, how much time do they spend recreating it?
A conservative employee productivity model has employees consuming 4 hours per week doing the above. That comes to 208 hours per year. Considering the average employee's annual wages - $80,000 (fully loaded), the average hourly wage is $38.46. Now multiply $38.46 times 208 hours, and you get an eye-popping $8000 of time spent trying to find or recreate existing data that couldn’t be found. In a company of 5,000 employees, that comes to a cost of approximately $40 million of wasted time trying to find data.
A closely related metric is that of lost revenue due to lost productivity. If you could recover the 208 hours of lost productivity per employee, how much additional revenue could have been generated? Let's calculate an example.
Let assume a company has an annual average revenue per employee of $150,000 – again, conservative in the high-tech industry. With this number, we can calculate the average revenue per hour of $72. Multiply that by 208 hours, and we get an average lost revenue per employee of $15k. Now multiply that by the 5,000 employees, and we see a massive total annual lost revenue of $75 million. To be more conservative, let's assume the average employee couldn’t convert one lost hour of productivity to one hour of additional revenue. We can halve that $75 million to a total annual lost revenue of $37 million – still a massive number.
The last question to address is the return on investment (ROI) for a solution to fix the eDiscovery and regulatory data issue as well as the lost productivity challenge. eDiscovery costs and the potential savings that could be realized by consolidating data takes time - understanding your company’s eDiscovery costs and the overall eDiscovery process, so I will leave that for later discussions. However, just taking the approximate conservative annual cost of $40 million in lost productivity and an estimated cost of a file consolidation solution of $500,000 (on the high side), we calculate an ROI of 7,800% - a number CFOs dream of. In actuality, you could halve the cost numbers again and again, and you would still realize an ROI in the thousands of percent.
Creating a more in-depth ROI model would determine the cost number more accurately, but you get the point. I have been in meetings with CFOs where they argued my numbers…many remarked that productivity numbers are a “soft cost” and therefore not real. But the lost revenue number grabs their attention and causes them to take it more seriously.
File Consolidation is now easier and more cost effective in the cloud. Archive360’s Archive2Azure (a native Azure application) provides a file consolidation and data management application which works with data in your company’s own Azure tenancy. It provides automated processes to move (or copy) data from your numerous enterprise data repositories into your Azure tenancy, index the data, apply retention/disposition policies and provide centralized eDiscovery, elastic search, and case management, including litigation. Archive2Azure also provides end-user access (based on access controls) to address the employee productivity issue discussed.
For more information on data consolidation and ROI, check out this related blog.
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Bill is the Vice President of Global Compliance for Archive360. Bill brings more than 29 years of experience with multinational corporations and technology start-ups, including 19-plus years in the archiving, information governance, and eDiscovery markets. Bill is a frequent speaker at legal and information governance industry events and has authored numerous eBooks, articles and blogs.