Big data is here! And, marketers are one of the professional groups that stand to gain the most from these new-found capabilities to analyze data that, until recently, would have been too complex to capture, store and make sense of. Behind the hype lies a golden opportunity for marketers and customer service to help their organizations get ahead of the competition. Cutting through all the noise can be a challenge, so it’s important to understand what big data can achieve, what data is most useful, and how to go about using it. In the following conversation, Verint’s Daniel Ziv and Ovum analyst Keith Dawson share perspective on the sudden lure to the term “big data” and what it means for companies in the coming year.  1. Big data is a buzzword that seems to be making its way into conversations more frequently. How would you define big data, and how is this new business concept different from traditional business intelligence? Keith: Big data is a buzzword partly because the definition of “big” changes all the time, as processing power improves and data storage capabilities grow vaster. However, it does have a real meaning, and is usually shorthanded by the four V’s, which are as follows:  Volume: the amount of data being worked with Variety: the number of different sources and data types Velocity: the speed at which it changes, which is very fast Value: the ability of an organization to process and leverage machine-derived insights Daniel: The first three V’s seem to have become the de facto definition for big data, but Keith’s addition of the fourth Vrepresenting “value” may be the most important yet. Many organizations have a lot of data, but not all are generating significant value from it. This may partially be due to the fact that big data initiatives do not always involve the business earlier on in the process.  2. Is big data something IT departments need to manage and address? How does it have an impact on the marketing organization as well? Keith: Big data starts with IT, most definitely, because they are responsible for acquiring and deploying the infrastructure. From a business point of view, marketing is poised to reap significant value out of big data. Look at the wealth of actionable knowledge inherent in CRM data, social media mining, or even basic customer call recording — there is enormous potential not being realized with traditional analytics structures. This theory applies beyond the contact center and also to business intelligence and ERP systems, which are still trying to figure out how to put data from some of those external sources to work.   Daniel: I agree completely! We, as an industry, have witnessed some phenomenal examples regarding how much value this data can represent — especially when driven effectively by the business including marketing departments. For example, I’ve seen a large telecom provider do this very thing. The company is BI savvy and has traditionally analyzed structured data. It added a speech analytics solution to help analyze contact center calls, and as a resultin the first year of deploymentthey identified $180 million worth of savings, while increasing customer satisfaction by 30%. What’s even more interesting with this particular initiative is that it was driven mostly by marketing, not by the IT department where many big data deployments reside. 3. Do you believe that a company’s corporate big data assets and its use of analytics could become something as powerful as the company’s brand? Keith: There are already companies for which the ability to analyze big data is functionally equivalent to their brand. Facebook is the obvious example, and Google too. Then you get beyond that to customer-facing companies like Netflix or Amazon, where their ability to determine patterns of customer preference and behavior stems from analysis of huge data sets. They are making business decisions on automated data analysis — the kinds of things that used to be done with focus groups. The difference is that they are coming to much richer, statistically valid and more insightful conclusions.   Daniel: Facebook definitely has the potential to be a key data analytics force. The acquisition of Instagram brings yet another dimension of unstructured data to social media. It will be interesting to track how Facebook uses and monetizes the tremendous amounts of data now available. Google is an example of a company where almost all of its services and revenue is driven by collecting, indexing and analyzing data. The recent updated privacy policy also now provides Google with the ability to link various customer insights from its many different services. It holds tremendous potential value for Google, as well as for the consumers, given customer privacy concerns are properly addressed. Recommendation engines from Netflix, Pandora and Amazon have also proven to be tremendously effective in driving sales and building loyalty.  4. Can you share other examples of how companies use this new asset to effectively compete? Keith: You have to look at the social networking space to really see the most advanced use of big data analysis to make lightning fast business decisions. Ad traffic is monetized on Facebook almost exclusively through big data analytics. Without big data, Facebook isn’t perceived as a giant company. The financial services industry also has been applying this kind of analysis to credit card and loan customer transactions for quite a while. You see it in airlines: dynamic pricing of tickets, for example, and for segmentation of customers. Daniel: The social networking space has created a tremendous amount of new customer data, and may be partly responsible for the emergence of the big data conceptgiven the explosive growth in the amount and velocity of this information. According to Twitter’s own research in early 2012, it sees roughly 175 million tweets every day, and has more than 465 million accounts. What many organizations neglect to realize is that their internal corporate data assets may significantly exceed this in terms of content and value. While a typical tweet is only a handful of words or abbreviations with limited context, an average five minute contact center call is typically over 1,000 wordsproviding much richer context that drives more actionable insights, when mined with the proper tools. I’ve heard industry estimates that for every word tweeted, there are over 200 words spoken in the contact center directly by your customers and CSRs. The challenge is connecting the dots between the different sources.  5. One of the key challenges of big data is transforming the common silo approach where each department has its own data assets, which prevents organizations from getting a unified view of the customer. What strategy and technology solutions are available to handle these challenges? Keith: My view is that the main barriers are more cultural than technical. You need business structures in place to share data, and to encourage the deployment and use of data warehouses that cross departments and functions. The idea of big data isn’t really a product, rather it’s more of a process or a label that describes those very strategies implied by the question. I don’t really see siloization as a challenge of big datainstead, it’s a challenge of organizational problem solving and priority setting. Once those silos have been broken down, companies can forge ahead with a more intelligent data analytics strategy. Big data isn’t an end in itself, because organizations can just as likely find themselves in a situation where mountains of data are being analyzed, and they still don’t know how to act on or monetize it. From that point of view, big data is an IT issue. My personal sense is that as teams outside IT begin to understand the potential value embedded in their data, they’ll start to look internally for collaborators who can help them unlock it. That’s going to be a unique process in every organization. Daniel: I think technology can help make this process easier but agree that the key issue is the organizational structures and processes. The emergence of the chief customer officer role and customer experience departments that own the end-to-end customer journey can help drive the right attention and actions. By making sure the organization has a unified 360 degree view of the voice of the customer, these teams will know better how to take action on valuable insights. 6. What industries do you think have the strongest potential to leverage big data as a competitive marketing advantage? Keith: I’d have to say the financial services industry has the strongest potential to leverage big data, because it has been doing big data analysis long before the trend even had a name. Retail also has a lot of potential. Look at the data that’s gathered from supermarket loyalty cards, as just one example. Travel and hospitality, telecomreally any market where there are a high volume of transactions or  interactions that have been historically too “low value” to examine individually, but that add up to a collective picture that’s attractive to mine.  Daniel: Financial institutions use things like credit scores to segment customers and offer differentiated products and pricing. However, it seems in the past that most of the data that was used was structured by nature and not necessarily leveraged as a competitive differentiator. When these organizations start mining the tremendous amount of unstructured content they have, for example, in their contact center calls, emails or even social media they can go much further in their ability to customize offers and leverage that as a competitive force.  Thank you, Keith, for the insight and perspective. Big data is a trend worth monitoring, as its evolution will greatly impact the way organizations — including marketing departments — take advantage of the huge potential value. It would be interesting to hear what readers think. Please share your comments and input on what big data means to you!