Right now, Big Data is one of the most heard buzz words in IT, next to cloud, analytics and mobile. So far, most of the discussions on Big Data focus on storage.
In the past, a lot of people thought Big Data was only a storage problem. Organizations were wondering if they will have sufficient capacity in the future to store all the data they generate.
While capacity is important and organizations need to ensure they are well-prepared for the future, the real issue with regard to Big Data is not storage, but information management.
A forward-thinking information management strategy allows an organization to categorize and analyze data. A good example can be found the banking industry where thousands of customer transactions are processed and stored every second.
In the early days of electronic banking, it was only possible to look at relatively new transactions on your computer screen. If you wanted to look at transactions older than, let us say three weeks, you had to check your paper bank statements.
Today, many banks offer the possibility to go way back and do an extensive search. Banking apps are also making life easier in finding all sorts of financial information.
The better a bank or other financial institution can provide this specific end-user information, the more of an USP it becomes. It is obvious this is not technology-driven but marketing driven.
Successful e-commerce retailers apply the same principals. They also use transactional data to service, entertain and engage their customers. The point is that they use static transactional data and transform it into actionable information through re-use.
Most companies look at Big Data right now, as they realize its potential. According to industry analyst firm Gartner, Inc., there are a lot of plans, but very few organizations have made the steps towards real implementations.
On that road, they have to deal with a few clear challenges. First of all, they have to determine which data can provide value to the business. This is easier said than done.
There is a lot of data to examine and finding the ‘gems’ can be a challenge. Still, determining which data has business value is the first step in any Big Data strategy.
Next, an organization needs to select the right analytics tools for determining trends in data, for example trends in the impact of a specific marketing campaign or in customer service. With these tools, data can be analyzed and the information that is gathered can be used to improve business processes.
Historically, large companies have not only had the most data to analyse, but they have had the resources to invest in that process. However, this technology is evolving rapidly.
Therefore, analysis and finding trends is no longer reserved for large corporations. Mid-sized companies too can profit from this type of technology. They also generate more and more data and can profit from easy-to-use tools that fit their company size.
A Big Data strategy starts with an information management strategy; that much is clear.