Before business users can start to analyze data and consider the next best actions to improve results, it is typically required to submit a request for the data. Depending on the backlog of requests to IT, the business user might have to wait days, weeks or more before moving ahead with analysis
Apple’s product reveal on Tuesday introduced thrilling new capabilities in mobile. The launch of their Apply Pay program will allow iPhone users everywhere to forego credit cards. Management of this type of sensitive big data requires dependable information governance measures from the start. What
Retailers today have an insurmountable amount of data to digest and act on quickly not just to personalize the customer experience, but also to prevent fraud. This week’s IBM Big Data & Analytics Hero, David Speights, chief data scientist at The Retail Equation, shares some insights with us.
In this post, the fourth installment of our five part series focused on real-time actionable insight, we explore how to optimize decision management by infusing context-aware streaming analytics into all decisions. Using IBM’s four pillar approach (Sense, Orient, Decide and Act) you can be assured
Big data can be turned into big business opportunities, but can also send you on a wild goose chase. Using IBM’s four pillar approach (sense, orient, decide and act) you can correct course and shift from reactive to proactive effectively orienting your organization to the business moment.
Data is emerging as the world’s newest natural resource and the basis for a new kind of competitive advantage. Yet, for many organizations, the increasing volume, variety and influx of data is straining their IT infrastructures—traditional infrastructure was never designed to handle the magnitude
When data floods companies in such volume as recent years have seen, it can be difficult to know if big data is reliable data. This “data uncertainty” poses a problem for any company hoping to take advantage of their information’s potential. How can companies ensure that their data meets the
Not every customer or prospect is ready to buy. This means that when you engage you must do so in the context of the buyer’s frame of reference, not solely as a seller ready to make a deal. But how is this done using big data and analytics?
We certainly live in a connected world. It always amazes me when I see how smarter enterprises are using the highly interconnected, intelligent and instrumented qualities of today’s technology to make our world a better place: the way we interact changes, how we approach our day is different and
Need to sell more products? Dump Steve, the guy in the chicken suit flagging down customers in the parking lot, and adopt real-time actionable insight.
IBM’s four pillar strategy (sense, orient, decide and act) will turn your big data into action. In our five-part series, we will introduce real-
“When people use a service for free, they are de facto lab rats for market researchers.” This is one of the responses to the Facebook social experimentation revelations that makes more sense than much of the hyperbolic ones that are saturating all forms of media at the moment.
Think it, try it and build it with IBM Bluemix, a cloud-based developer sandbox for big data and data management services. Bluemix makes agile mobile and web app development with best of breed capabilities a reality.
Insight 2014, formerly Information On Demand, will bring more than 13,000 business and IT professionals together to exchange ideas and share experiences around harnessing the data coming from all directions in real time. Industry pundits, peers and thought leaders will connect the dots between big
Big Data & Analytics Heroes
Mark HilinskiExecutive Vice President of Business Development and Strategic Accounts, The Retail Equation
The Retail Equation utilizes big data to predict and shape shopper behavior. Mark Hilinski, executive vice president of business development and strategic accounts and this week’s IBM Big Data Hero, shares how they “uncover those patterns of activity that were otherwise unseen in the data” and