Big data, analytics and organizational alignment
"Historically, there has been much talk about the difference between traditional analytics and big data, and organizational responsibility for each within an enterprise," the report says. "The survey, however, shows the two are becoming closely intertwined and must work together to deliver the promised results of big data. Further, breaking down organizational boundaries and creating close integration between IT organizations and the business units is a critical step for any organization hoping to build a winning strategy for big data."
"Data management and analytics have often resided in different parts of the organization," the report adds. "IT departments usually controlled the data and analytics was conducted in either a special group or within a business unit. This is contrary to the entire principle of big data and the survey confirms that organizations understand close integration is necessary. Sixty-five percent say, "big data is an integral part of data management," and 68 percent further felt that "big data is part of the advanced analytics toolbox."
Making this leap -- integrating traditional analytics and big data while tearing down boundaries between IT and business units -- is a critical early step in creating organizational initiatives that leverage big data to affect the business, NewVantage concludes.
"Integrating real-time, full analytic capabilities into the business and operating units will enable the type of quick reactions to key business questions and challenges that can build competitive advantage and improve performance," the report says.
"Think about your data and data quality as having different stages that we call bronze, silver and gold," Barth adds. "Data in your data warehouse is gold. When you go to that gold source, you know you're getting data that has been really worked through. But what if data is also available in a raw form and you can get it to me in a week or a month, if you can dump all the data in one place and organize it just a little bit? The data is useful before it's perfect."
Unlike traditional relational databases, big data platforms allow analysts to organize, clean and integrate data selectively, ignoring records and fields that are not the current focus of analysis. This is a significant departure from data warehouses, where a great deal of effort is spent on data engineering to make sure it's production-worthy before it's released to users. NewVantage notes that by deferring full data engineering, big data platforms accelerate TTA during discovery-oriented analysis and eliminate the engineering effort on data that doesn't deliver value.
The idea then, Barth says, is that big data platforms become one piece-albeit an important one-of a data ecosystem that is designed to constantly look for new insights into customers, markets, products and risks, while at the same time building upon what is already known. In other words, pursue the "new" while operating on the "known," a healthy, continuous improvement model.
Creating a big data ecosystem
Think of it this way: Whether it comes from big data or traditional analytics, the important thing is to provide valuable answers. The value of an answer, Barth says, is based on its accuracy and the speed with which it can be delivered. To get an accurate, speedy answer, it's important to ask the right questions. And that's where big data comes in: It's about pursuing the "new."