Will Big Data determine what we eat, wear and purchase in the future? Will we be influenced by Big Data to buy unnecessary products? Is big data an invasion to our private information?
Last year, Google launched BigQuery and this will change the strategy to many companies offering data analytics and also other companies wanting to use this for forecasting and trend analysis. For instance, Google had an amazing data collection outcome with the flu outbreak tracker. Since, Google stores all of its searches and they were able to identify and predict a flu outbreak in certain regions of America. The Center for Disease Control takes about two weeks to report the data. Google does it in real time simply on search queries.
Recently, Kenneth Cukier published Big Data: A Revolution That Will Transform How we Live, Work and Think. Kenneth states, “You're living in a world in which you're never going to have enough information, but you're going to have to come to answers and conclusions and make decisions based on it. So it's really important that you take in as much information and come up, using your judgment and wisdom ... come up with a decision based on that."
Even with Google creating BigQuery and having this analyzed by many academics Big Data is still a new technology and progress has been slow for most firms. Big Data is critical, but only one piece of your customer data management is not the solution. Many companies are stating we need a “Big Data strategy.” However, what they really need is a holistic strategy that includes Big Data, predictive analytics, in-memory, and destroying data silos. First, every customer data that is available is important and allows for the customer experience to be enhanced for any service or product. The need for cloud analytic platforms is essential to create strategies for any company. Secondly, predictive analytics are necessary to learn more about the patterns of the any customer. In order to do predictive analytics there is the need for technologies to store, process and access the volume, velocity and variety of Big Data. Google is able to create BigQuery because of their huge memory and velocity capabilities. The third part is sharing data and dissolving silos within data sharing platforms. For instance, sharing NoSQL, Hadoop, and external sources like Facebook and LinkeIn data can help the huge problem of silos in most firms. Firms that invest heavily in a holistic three pronged approach will be ready to take advantage of its customer’s Big Data and use it as an advantage.
There are obstacles in any new strategy. For instance, many enterprises are not comfortable allowing sensitive data outside for competitive advantage, privacy, and regulatory concerns. The In addition, deployments are actually more expensive than building internal infrastructures. And then there’s the data movement problem, getting large volumes of data from internal data centers into the cloud and back often requires more than just an Internet connection.