The ongoing information explosion from the computer age gained significant momentum in the last decade (or so), finally reaching epic proportions and earning its own name: Big Data. The realities of Big Data encompass bothBig Data challenges and opportunities. The challenges stem from the requirements for eDiscovery, governance, compliance, privacy and storage. But the silver-lining to those obstacles is the opportunity to use the collective Big Data to predict and recognize patterns and behavior and to increase revenue and optimize business processes.
This is where content analytics come into play, and they are becoming an essential toolset, particularly for overcoming the challenges from unstructured and multimedia data. In essence, we need computers to battle the data explosion we’ve caused with other computers. Applying content analytics helps to assuage the risks of Big Data, but also to benefit from the power of Big Data: broad analysis which yields absolute insights.
In two special feature articles in AIIM’s recently published toolkit “Big Data – Using Analytics to Improve Your Business”, I have discussed not only content-analytics methods, but also how to evaluate their overall quality and implement them into a defensible process to control the risks of Big Data and to benefit from the predictive power and new insights that can be gained from Big Data.
In this great new AIIM report, various other authors and myself present some of the more practical and popular applications for achieving the promise of big data. We identify the key obstacles to implementation, and provide a list of recommendations for taking the first steps to overcome them. The report is free to download from the AIIM website: www.aiim.org.
One of the expressions in the machine learning community is that “the only good data is more data”. This is essentially what Big Data is all about. Big Data will help us to develop better content analytics and better machine learning. Content analytics will help us better to control, manage, understand and use Big Data.
As a result, one can state thatContent Analytics and Big Data are symbiotic: one cannot only exist without the other, but they will also enhance one another to become better and more advanced in the very near future!
In the last decade, content analytics have been applied with enormous success in fields such as intelligence, security and law enforcement. Sentiment mining (voice of the customer), analytics on clinical research and early detection of product guarantee problems have saved companies millions. Recently, the application of content analytics and machine learning in governance, compliance and eDiscovery have caused technology disruptions in these industries and lead to incredible efficiency and productivity improvements.
There is no reason why the application of such techniques cannot make a difference for your business as well.
Download the full AIIM report to find out more details!#e-discovery #ElectronicRecordsManagement #BigData #Text-Mining
#Content Analytics #governance #compliance #InformationGovernance #Datavisualization #grc