For most businesses data is the foundation upon which they wish to build better customer experiences, deliver new innovative products or improve operational efficiencies. However, for many showing the return on their data investments remains illusive. Arecent McKinsey reportindicates only some 30% of proposed benefits were achieved over the past 5-6 years.
In February Hortonworks commissioned Forrester Research to explore challenges associated with big data adoption and understand the current trends shaping architectural choices. According to the study 3 out of 4 decision makers surveyed are expanding their use of big data in areas of automated decisioning (eg fraud detection), real-time analytics and also new innovative products.
One of they key challenges highlighted in the research is that the current approaches to bringing the data from the disparate systems and apps are heavily labor intensive and time consuming, leading to frustration with the Business community waiting for fast results. Over the past decade the application strategies adopted by companies have gone from standardizing on monolithic business suites, running in your own data center, towards best of breed applications running in the cloud. Just to answer the same type of business questions that we did over the past decade now demand a bigger effort to integrate these apps to create an integrated view of your business.Â
In addition to the expansion of our app landscape there are now so much other interesting data we could also get our hands on – customer product reviews on Amazon.com or clickstream data from your web or a stream of data from a personal health monitoring device an individual might be carrying. While the application data integration is challenging because of the distributed nature of the apps, the new data ads the complexities of massive volumes and the fact that the data itself is not formally structured like we used to. In short, our old approaches are not able to help much with these new problems. Read how Prescient, a travel safety company, is pushing this to the limit by ingesting data from thousands of data streams.
The third element of our data landscape is about real-time streams of data and distributing decisions to the edges – managing data in motion.
