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Big Data for Business: How to Optimize Your Data?

Until a few years ago, many people did not understand the concept of Big Data, what it referred to and how it could be fundamental to the development of an activity. Many companies did not see the value of adopting a Big Data solution, which required not only a change of strategy, but also a significant investment.

Big Data: definition and challenges

Big Data is born of a phenomenal amount of data generated by users around the world. The technological development that we are currently experiencing makes it much easier to exchange digital data. This is a real opportunity for both large and small businesses to collect enough to take advantage of it. According to figures published by IBM, we generate 2.5 quintillion bytes of data every day from our message exchanges, social networks, online transactions, GPS signals, and so on.

In such a situation, old management systems are no longer large enough to ensure good data management. It is for this reason that we had to find a new method of treatment: that of Big Data.

For companies collecting large amounts of data, the challenges of Big Data are particularly important. This technology allows them to better meet the expectations of their customers and prospects, to anticipate the needs of the latter by offering them adapted innovations, and of course, to reduce operating costs by adapting production, deliveries …

How to set up a Big Data strategy?

To optimize its data, it is imperative to adopt a good Big Data strategy. To do so, one must analyze its objectives upstream, and establish precise specifications to aggregate the relevant information. Expert companies in this type of operation have been created and companies now have at their disposal a wide range of solutions to define and implement the adapted Big Data strategy.

Upstream optimization operations

To optimize its data with Big Data, it is essential to define its objectives in advance. Indeed, many are embarking on a Big Data project only because the term is in vogue. It is a mistake! It is very important to define the purpose of the optimization. Do you want to improve customer satisfaction? Do you want to revitalize your sales? Or, is it to create an innovative market? First and foremost, set your goals well. It is according to this that you will be able to elaborate your treatment strategy for a certain optimization.

Once your specifications have been established, you will need to think about aggregating the different sources of data at your disposal. This step can be difficult in some companies, but it is essential: customer databases, newsletter, contest, social networks, etc. Companies use many sources of data for their collection, which may have the disadvantage of reducing the effectiveness of treatments. In this case, multi-source aggregation makes it possible to optimize its data efficiently.

Implementation of the data analysis

For successful data optimization, organizations can rely on Big Data professionals and specific analytics solutions.

1. The Big Data Professionals

Their goal is to make sense of the data collected to enable companies to make strategic decisions for their growth. Many companies internally hire one or more specialists who work daily on data management and analysis. Others prefer to opt for external solutions.

These data professionals will have to devise new analysis models to enable the best treatment of data that cannot be studied with conventional database management tools. To do this, they usually combine a triple skill: knowledge of databases, statistical and computer expertise, and expertise in specific sectors such as finance, marketing, etc.

The most common Big Data profiles have titles such as Data analysts or Data Scientists. The difference between these two types of professionals obviously lies in the extent of the tasks they perform. Indeed, the data analyst is usually responsible for analyzing data from a single collection source, based on a defined model. The Data scientist, meanwhile, has a more global view that allows him to cross data from different sources.

Note that in addition to Data Analysts and Data Scientists, other professionals such as Data Science Architects and Data engineers are often in high demand profiles, especially by startups. The Data Science Architects are responsible for conducting a monitoring to identify exploitable open-data sources. Data engineers maintain data collection, storage and provision systems.

2. Big Data Analysis Tools

There are more and more tools for data processing and analysis. Each company must be able to identify and select the solution that best suits their needs. That said, we must remember that a Big data analysis tool must meet the rule called 3V: Volume, Variety and Velocity. In other words, organizations must choose the tools that can handle a large volume of data from different sources and share the results in record time, or even in real time.

3. Some examples of solutions

NoSQL (Not only SQL) databases which are management systems (DBMS) with a different architecture than those of classical and relational databases. They are considered the most powerful systems for mass data analysis. Cassandra, MongoDB, or Redis are the most common examples of these databases.

Server infrastructures can handle simultaneous distribution of processing on several nodes. This method is also referred to as massively parallel processing. The best known of these tools is certainly the Hadoop framework which is a combination of the NoSQL HBase database with the HDFS file system and the MapReduce algorithm. There is also a recent upsurge of tools tending towards a more “real-time” treatment, including of course Apache Spark.

At the moment, Big Data has emerged as one of the most important levers for a business. There are tools for all types of companies so that they can rely on Data to innovate, energize, evolve or better analyze their activities. However, in order to use this lever correctly and optimally, investing in new analysis solutions or in new skills at the team level is essential.

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