In my previous post
, I talked about the complexity and characteristics of Big Data, its rampant growth, the lack of traditional tools to manage it and the emerging technologies at a high level. In this article, let’s explore some of the challenges and opportunities that Big Data presents to businesses with three examples. But first, let’s quickly summarize the challenges it poses.
BIG DATA: VOLUME, VARIETY, VELOCITY! INFORMATION?
Executives across industries acknowledge the exponential growth of data. As the data available to an enterprise increases, the amount it can humanly process, understand and analyze is decreasing and straining existing IT infrastructure. Without executives and managers being able to access valuable information hidden in all this Big Data, they are unable to take timely decisions and apply appropriate solutions to their business challenges. The tools currently available are unable to filter out irrelevant data, which could lead to suboptimal business decisions.
According to an Avanade paper
, “Amidst the sea of data, one in three executives is regularly unable to find the right people who can provide the information they need when they need it. […] During the latest recession, more than one-quarter of executives have lost their business because they couldn’t access the right information.”Data overload but lack of “good” information is real and causing problems for business leaders.
BIG DATA, BIG CHALLENGES!
Some of the big challenges that we face with Big Data are:
- How can we understand and utilize it, when it comes in such a multitude of unstructured formats?
- How can we capture relevant data in real time and then use the insight derived from that data for business results?
- How can we analyze and manage the need for and the size of computational capacity required to handle it safely?
In summary, the challenges exist in storing large volumes of data, analyzing its variety, and providing faster access to and security for this growing mountain of data.
For example, in contact centers, the time/quality resolution metrics and trending discount patterns can show up weeks after a particular situation has occurred. Information latency is critical. This latency means if someone’s on the phone and has a problem, you’re not going to know about it right away from an enterprise perspective. Take my example, a fresh one. Last week I called my bank to request a soft copy of the provisional certificate for my home loan. The bank took four days and six follow-up calls to send the email! The bottom line in this (and in many other cases) is that all this information comes “too little, too late”, and the problem is left solely with a customer service rep(CSR) to handle without consistent and approved remediation workflow in place.
It is possible that the customer service rep would have either missed sharing this information with the loan officer because of the myriad of interactions that he/she handles every day, or maybe the loan officer was waiting for an approval from a senior official. Nonetheless, amidst these transactions, several terabytes of data is produced. If this data could be harvested, comprehended and utilized, the transaction that took four days would have been resolved in less than an hour.
BIG DATA PRESENTS “VALUABLE” OPPORTUNITIES
Despite these challenges, enterprises understand that the opportunities presented by Big Data are tremendous. McKinsey
calls Big Data “the next frontier for innovation, competition and productivity.” With insights, answers, trends and knowledge available to us (earlier beyond our reach), we realize, improved productivity, competitive edge and economic value.
Take for example fraud detection in the financial services business. A financial transaction anywhere (insurance claims, credit card transactions) always presents a potential for misuse and the ubiquitous specter of fraud. The traditional approach to fraud detection uses samples and models to identify customers that characterize a certain kind of profile. The problem with this approach is that it works by profiling at a segment level rather than at the level of granularity of an individual transaction or person. Quite simply, making a forecast based on a segment is probabilistic, but making a decision based upon the actual particulars of an individual transaction is obviously more pinpointed. With banks leveraging Big Data, they could identify such fraud instances earlier and even stop them.
Another example is from social media, a pretty hot topic today. Utilizing Big Data here would help us to better understand customer sentiments. This could help us figure out trending topics, understand their impact on sales via “social” feedback, effectiveness or receptiveness of our marketing campaigns, the accuracy of our marketing mix, and so on.
BIG DATA, WHAT’S NEXT?
Enterprises can analyze this “data-in-motion” by building models to find out what’s interesting based on conversations converted from voice to text, or with voice analysis while a customer call is happening. By analyzing “data-at-rest” and “feeding it back” into contact centers, you can examine the calls that are actually happening in real-time. Constantly running these analytics/heuristics/models against data, discovering new patterns, creating new business rules and pushing them into operations streams can ensure immediate action when a certain event occurs.
Clearly, Big Data presents huge potential benefits across all industries, including government, healthcare, media and energy among others. Data is becoming central to business operations. If we learn to derive value from it, it will fundamentally change the way our businesses function.
How can we harness these business benefits from Big Data? Leave your comments below on Big Data applications in business and how companies are managing them.