Recently, there has been a lot of attention on the way automation is changing the marketing industry. Companies in all industries are finding new ways to incorporate automation to bring value to customers. Although the use of automation in business operations has been around since the introduction of the assembly line, we are now seeing the birth of a new form of automation based on advanced analytical tools. With the rise of new forms of data and the development of algorithms, automation will not only continue to streamline business operations, it will also drive business intelligence.
Along these lines, Thomas Davenport of The Wall Street Journal introduced the idea of a fourth type of analytics he called “automated analytics” earlier this year. Previously, there were three generally accepted forms of analytics:
- Descriptive analytics which describe what happened with simple descriptive tools: frequency distributions, charts and graphs, and “measures of central tendency,” such as means and medians.
- Predictive analytics which use models describing past data to predict the future.
- Prescriptive analytics which make recommendations—often to front-line workers—about the best way to handle a given situation.
Davenport added automated analytics to this list. Automated analytics is a form of analytics that does not present a recommendation to a human but rather takes action based on the results of its analysis. Companies that already use automated analytics include Amazon with its automated recommendations based on previous browsing experience and LinkedIn with its recommendations of “people you may know” based on email contacts.
Currently, predictive analytics is one of the core strategies used to address the challenges of Big Data. This is because it uses past data to make forecasts and intelligent decisions; in other words, predictive analytics is the art of making Big Data work. Some leaders of predictive analytics state that predictive and automated analytics can most effectively be used together. This is because 99 percent of decisions today are the same decisions you have made in the past. Using automated analytics along with predictive analytics is a key area in driving business intelligence forward.
The use of automation can also be a key strategy in e-discovery to sort through and analyze massive volumes of data. Automation is already used in backend processing and reporting tasks, but can now be used to create complex, repeatable workflows based on search parameters to save time and reduce risk by eliminating redundant manual processes.
The next step in automating e-discovery tasks is adding predictive analytics to these automated workflows. By using existing data to predict new patterns, automation can create tiered review populations that organize review sets based on likelihood of responsiveness, privilege and other preset parameters. This analysis could also be used to determine the cost and time of a review, the merits of search terms, and strength of a case before a review is even started.
While there are concerns that automation will remove human intervention, working strategically with the strengths of automation can eliminate mundane, redundant processes and allow attorneys and litigation support staff to think creatively and work on higher level strategic projects while at the same time improving efficiency and reducing costs. Automation, if used intelligently and strategically, could provide significant benefits to parties employing such approaches.
About the AuthorMore Content by Larry Gomez, Esq.