With more data streams coming online and their integration into existing BI, CRM, ERP and other mission-critical business systems, the single view of the customer may finally come into focus. While most customer service and field sales representatives have yet to feel the impact, big companies such as IBM and MicroStrategy are working to see that they do soon.
Big Data Moves Analytics Forward
Big data is pushing a tool more commonly used for regression analysis into the hands of line-level managers, who can then use non-transactional data to make strategic, long-term business decisions about, for example, what to put on store shelves and when to put it there.
However, Gartner's BI analysts say that big data is not only about to supplant traditional BI tools. If nothing more, big data will make BI more valuable and useful to the business. It's a fact that we will always going to look at the past. When you have big data, you are going to look into past even more. That's why, BI doesn't go away. It just gets enhanced by big data.
The Power of Social Media
There is value in social media. Nobody can deny it. Just try imagine, what if you learn, as the buyer for a retailer, that Justin Bieber fans really loved the jacket he was wearing at the concert last night, and that someone tweeted he got it from one of your stores? You could then make a decision to stock up on that jacket just in that city since you know it's about to become a very hot item. Do you realize the possible power now?
But, without a predictive analytics (PA) package looking for patterns in the Twittersphere that correlate your brand with geographic location and factors such as the number of mentions, you could miss out on a great but small window of opportunity to move merchandise.
Shortening "Time to Answer" Key to Big Data Analytics
Founder and managing partner of New Vantage Partners, a boutique information management and analytics consulting firm says that one big advantage to this type of analytics is the shortening of the "time to answer" (TTA). The queries, or models, that used to take data scientists months to build in order to answer forward looking business questions about supply chain or production schedules can now be done, in some instances, in hours, and in bulk.
Big Data Analytics Not Ready for Prime Time
While all of this is promising and exciting for business users, hooking big data analytics into a natural language processing engine and a Siri-like Q&A interface is some ways off. Hadoop, while powerful, is still by all accounts a "primitive" tool for tackling massive data sets. Think very carefully about the usefulness of these insights, too. Are 100 million opinions really worth more than 100,000?
There's a lot of repetition online, and we still need really smart analysts if we want to do our analyses right. Fortunately, big data gives us very powerful tools to start in that direction.