Up until a few years ago, many people were unaware of the concept Big Data, what it referred to and how it could be fundamental for the development of an activity. Many companies did not see the value in adopting a Big Data solution, which required not only a change in strategy but also a significant investment.
Today, things have changed. More and more companies are interested in this type of solution. According to the results of a Markess study conducted in 2015, 61% of French business leaders considered it as a driving force for growth in its own right, in the same way as their products and services. More than half of the French companies have already started operating Big Data, and many are planning to do so in the near future. Learning, understanding how this technology works and the impact it has on their business, is essential for today’s leaders.
Big Data : definition and stakes
Big Data, also called “mega data” or “massive data” was born from a tremendous amount of data generated by people worldwide. The technological development that we are currently experiencing considerably simplifies the exchange of digital data and is a real opportunity for both large and small businesses to collect enough digital data to be able to take advantage of it. According to IBM figures, every day we generate 2.5 quintillion bytes of data, from our message exchanges, social networks, online transactions, GPS signals and so on.
In these conditions, the old management systems are no longer sufficient to ensure proper data management. This is the reason why we have had to find a new processing method: Big Data.
For companies collecting vast amounts of data, Big Data is particularly significant. This technology enables them to respond better to the expectations of their customers and prospects, to anticipate their needs by proposing adapted innovations, and of course, to reduce operating costs by adapting production and deliveries.
How to implement a Big Data strategy ?
Before the optimization operations
To be able to optimize your data with Big Data, it is essential to define your objectives in advance. Indeed, many people embark on a Big Data project only because the term is in fashion. You must not do this! You need to define the purpose of optimization. Do you want to improve customer satisfaction? Would you like to revitalize your sales? Alternatively, is it to create an innovative market? First of all, define your objectives. It is on this basis that you can develop your treatment strategy for a guaranteed optimization.
Once you have established your specifications, you should think about aggregating the different data sources at your disposal. This aggregation may be difficult for some companies, but it is an essential step: customer databases, newsletters, competitions, social networks and so on. Companies use many data sources for their collection, which may have the disadvantage of reducing the effectiveness of treatments. The multi-source aggregation allows, in this case, to optimize its data efficiently.
Implementing data analysis
Companies may call upon Big Data professionals and specific analysis solutions, to ensure that their data is successfully and efficiently optimized.
1. Big Data professionals
The objective of Big Data professionals is to give meaning to the data collected, to enable companies to make strategic decisions for their growth. Many companies hire one or more specialists in-house who work on a daily basis to manage and analyze the data. Others prefer to use external solutions.
These Data professionals, develop new analysis models to allow data to be processed as well as possible, including data that is impossible to study with traditional database management tools. To do this, they generally combine three skills: knowledge of databases, statistical and computer expertise, and expertise in specific sectors such as finance, marketing, and so forth.
The most frequent Big Data profiles have titles such as Data analyst or Data Scientists. The difference between these two types of professionals lies in the scope of the tasks they perform. Usually, the data analyst analyzes the data from a single data collection source, based on a defined model. The Data scientist, on the other hand, has a more global view that allows him to cross-reference data from different sources.
Please note, in addition to Data analysts and Data scientists, other professionals such as Data Science Architects and Data Engineers are often in high demand, mainly by startups. The Data Science Architects are in charge of monitoring to identify open-data sources that are used. Data engineers, on the other hand, maintain the systems for collecting, storing and making data available.
2. Big Data analysis tools
There are more and more tools for data processing and analysis. Each company must, therefore, be able to identify itself and select the solution that will best suit its needs. Nevertheless, we should remember that a Big data analysis tool must meet the so-called 3 V rule: Volume, Variety, Velocity. In other words, companies must opt for tools that can process a large volume of data from different sources and share the results in record time, if not in real time.
3. Examples of solutions
NoSQL databases (Not only SQL) are management systems (DBMS) with an architecture different from those of traditional and relational databases. They are considered the most powerful systems for mass data analysis. Cassandra, MongoDB, or Redis are the most frequent examples of these databases.
Server infrastructures for the simultaneous distribution of processing on several nodules. This method is also called massively parallel processing. The best known of these tools is undoubtedly the Hadoop framework which combines the NoSQL HBase database with the HDFS file system and the MapReduce algorithm. There has also been a recent upsurge in tools tending towards more “real-time” processing, including, of course, Apache Spark.
At present, Big Data has established itself as one of the essential leverage tools for a company. There are tools for all types of companies so that they can rely on Data to innovate, energize, evolve or more effectively analyze their activities. However, to use this lever correctly and optimally, investment in new analytical solutions or new skills at the management team level is essential.
