Big data and analytics: an unstoppable combination
It is no longer a matter of finding answers in the data, but rather of asking the right questions
In recent years, a term that has generated a great deal of interest and confusion, in equal parts, has made a strong entrance. We are talking about big data. Although there are many ways of interpreting and understanding this concept, we define big data as a set of data with special characteristics in terms of volume (a large amount), speed (generated at high speed), or variety (different types, structured or not, from different sources) that requires the use of new processing techniques that allow us to gain new knowledge, improve decision making in our business, and optimize processes.
A related concept that should also be discussed is analytics, which refers to the mathematical algorithms and statistical models we will use to process our data for descriptive purposes (identifying patterns) or predictive purposes (estimating future values).
All of this strongly reminds us of what has traditionally been known as business intelligence. However, two differentiating factors have led to changing the name. The first is the exponential growth of the amount of data we generate and store. Without going into specific figures, in 2013 it was estimated that in the two previous years, more data had been generated and stored than in the entire history of humanity. Traditional tools are unable to handle this new reality. The second differentiating factor is the drastic reductions that are taking place in storage and processing costs. This makes it technically and economically viable for an entity to attempt to make a profit from all the data available.
This leads us to a new paradigm in which we must change the way we handle information. It is no longer a matter of finding answers in the data, but rather of being able to take advantage of all the information sources within our reach and ask the right questions.
A number of use cases that are starting to appear show that big data is extremely useful for business. This is the case of industries such as banking, retail, utilities, and telecommunications, which happen to be leading the adoption of solutions based on these technologies in order to create new business models. If we focus on the telecommunications industry, some of the use cases that have been implemented are:
Preventive infrastructure maintenance: By analyzing the data collected by sensors located throughout the network, it is possible to detect equipment failures before they occur, thereby avoiding network interruptions thanks to preventive actions.
Analyzing call data records (CDR): It is possible to analyze millions of calls per second in order to discover failure patterns and their causes.
Optimizing infrastructure investments: The analysis of massive data on the performance and use of networks makes it possible to plan growth more efficiently.
Redistributing bandwidth in real time: By analyzing network activity and external sources such as social networks, it is possible to predict behavior patterns that can cause network saturations, thereby allowing the implementation of preventive actions that maintain SLAs.
New product recommendations: Through the use of exhaustive knowledge on customer behavior patterns, NBA (next best activity) systems make the best selection of new products to offer them.
Big data is not a trend. We are convinced that it can provide significant benefits for nearly any industry or sector, even in the case of public entities, where big data is playing a key role in the development of smart cities.
Implementing a big data solution in a company
There is a wide array of technological solutions from which to choose, and the number of options continues to grow. This can become a barrier to entry for many businesses and public entities that lack professionals specialized in these new technologies. In light of this situation, good advice is essential in order to define a pilot project that allows assessing the potential benefits that can be obtained from using big data and analytics. Research must be done in order to identify the most valuable data sources (internal and external) and to select the best technologies, with a special focus on visualization tools, which will play a fundamental role in decision making.
The nature of data is changing, and we are facing the challenge of being able to take advantage of all the new information sources available in order to generate new knowledge that will help us make business decisions.