Technology Alone is Not the Answer to Big Data

Blog | February 13, 2012 by admin

When it comes to Big Data, people up and down Highway 101 in Silicon Valley talk about technologies such as NoSQL, Hadoop and MapReduce as though they’ll solve all of our problems. While these are certainly exciting new capabilities, technology alone is only part of the answer to Big Data. Follow us on a six-part series of blog posts to learn more.

First, though, let’s not discount the contribution of technologies to solving the Big Data dilemma. Hadoop, MapReduce and other recent innovations are helping companies deal with ever-increasing amounts of data, whether they’re working with traditional rows of transactional data inside enterprise applications or information in documents, images, video and the whole universe of social media out on the Web. From a technology point of view, we’ve made a lot of progress with providing people better tools to solve the technical challenge of dealing with this massive amount of ever-changing data, but what we also need to do is help companies leverage this data to support business objectives – be it to improve the efficiency and operations of a business area or make better business decisions of almost any kind.

For example, technology has allowed people to spend entire days reading email, scanning Facebook and staying current with what’s happening on the Web. But does this make them more productive at work? Yes and no. No in the sense that the access to more information in itself doesn’t necessarily make you a better employee, and access to everything at once can actually be overwhelming. But having insight to the “right” information can help you close a deal or deliver better customer service if it comes to you in a way that gives you insight to the task at hand.

At SAP, we are focusing not only on the technology dimension of Big Data but also on how to integrate these new innovations into business solutions that help the individual lines of business and industries identify what pieces of the data stream people need access to and how to turn the data into actionable information that the business can understand and use. The real opportunity with Big Data is it gives the business users more sources of knowledge to tap into, to combine with sales and inventory data stored in traditional data warehouses, and thereby get a better, more complete understanding of how their customers perceive them and their brand, what products and services are most appealing, and perhaps what the competition is up to. This better, more complete picture of the market then informs the business users on what they should be doing next – such as when to run promotions, adjust pricing or plan new product enhancements.

For example, let’s say you are a product manager in athletic footwear and you’re trying to decide what’s going to be the next update of your product. You’re designing next season’s running shoe, and you need to figure out when would be the right time to introduce the next version and what design changes you may want to incorporate. Part of that decision will be based on how well the current version is selling, the inventory level and the cost and profitability of the current version. A lot of that information is easily accessible in enterprise systems you already have. But once you come to the conclusion that inventory is low, or that price discounting is increasing due to competition, then maybe it’s time to start planning a product update. What will that update entail?

With running shoes, one popular trend for the past few years has been a very minimal, lightweight running shoe. Based on just past sales data, you could see the recent strong demand and say we need to create another minimalist shoe with a new color and pattern, and that’s your update. Good product managers would also go to industry events, read the relevant press and magazines, and maybe work with a consultant for more knowledge about industry trends, or even conduct a focus group or two. That’s how you would have proceeded in the past. But wouldn’t it be better to augment that with more insight and analysis based on hard data?

By leveraging these new Big Data technologies and integrating them with existing business solutions and processes, innovative organizations can now give that product manager a lot more insight to validate some of the decisions being contemplated. In the realm of athletic footwear, there’s a huge amount of discussion occurring in online communities, blogs, expert commentary and online magazines. The challenge is that there is so much discussion going on, it is more than one person, or even a team of people, can read and analyze on their own.

However, by using these new technologies to do the monitoring, aggregating and analyzing of these numerous communities, tracking running publications, and even following influential runners and coaches on their blogs and Twitter feeds, the application can detect patterns and highlight trends in millions of individual postings. One trend discovered could be that a segment of the market – maybe the “weekend warrior” runner – is encountering Achilles heel injuries, which is a serious injury for runners, when they wear the minimalist-type of running shoe. With this type of information in hand, the product manager can now make more informed business decisions as to how to plan the full product line and associated marketing campaigns so that there will be products and advertising that appeal to the type of runner who will do well with minimalist footwear, and they will also still retain traditional running footwear styles and promotional spending to go after the people not suitable for the “barefoot” runner trend.

Used effectively, Big Data combined with your existing enterprise data can help you get closer to your market and your business, to shift traditional conversations around pricing and profitability to one that considers a holistic view of not only what happened yesterday, but also what is happening now, next week and next month. By doing this, you don’t replace your current best practices for, say, product planning, you just augment them with additional information sources so that you can ask questions and discover new trends and insights you may have not realized about your business.

To see how Big Data tools can be combined with analytics methods, visit Blair Wheadon’s analysis of ads shown during last weekend’s Super Bowl.

Next up, we will explore many of the ways people can use Big Data in specific industries and lines of business.

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