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Understanding the importance of the Big Data projects

the importance of the Big Data projects

Data management is a process that includes the acquisition, validation, storage, protection and processing of the data necessary to ensure data accessibility, reliability and quality. Companies goal is to make a better use of existing data to ensure that they are fully exploited. Companies are using Big Data more than ever to improve business decision-making and obtain detailed and relevant information on customer behavior, market trends and user experience opportunities. In France, Big Data is no longer specific to technology companies but has established itself in a wide variety of trades and sectors. The EBG’s “Big Data 5 years later” barometer, conducted on behalf of Qlik, the market leader in Data & Analytics, and Micropole, confirms this. But before going into detail on the subject, let’s first discover what Big Data is.

What is Big Data?

Big Data is an evolving term that refers to a large volume of structured, semi-structured and unstructured data that has the potential to be exploited for information and used in machine learning projects and other advanced analytical applications.

Big Data is often characterized by 3V: the extreme volume of data, the wide variety of data types and the velocity and speed at which data (text, images, photos, and videos) must be assimilated, analyzed and processed. More recently, several other Vs have been added to the descriptions of large data, such as veracity, value and variability. Although Big Data does not correspond to a specific volume of data, the term is often used to describe terabytes, petabytes, and even exabytes of data collected and stored over time.

This voluminous data can come from multiple sources, such as business transaction management systems, customer databases, medical records, RSS feeds, mobile applications, social networks, scientific experiment results, machine-generated data and real-time data collectors used in Internet of Things (IoT) environments. The data can be left unprocessed or pre-processed using pre-analysis software (multi-source import, cross-referencing and preparation, advanced analysis, visualization, etc.)

Big Data has an impact on many sectors such as health, transportation, commerce, marketing, etc. Marketers, in particular, are increasingly using artificial intelligence and machine learning to analyze the huge volume of data they collect and obtain useful information on which they can rely for targeted and relevant actions. In data management, the challenge is to manage and exploit exponentially growing digital data in such a way as to make it a valuable aid to decision-making. The future of companies and other organizations depends on it.

What is data management?

Because data collection and storage is not enough, for companies to be able to exploit the large amounts of data they collect, they must manage them successfully to understand their meaning. The role of data management is precisely to manage and analyze raw data in order to make the most of it. This can be achieved by using data processing, storage and validation systems, as well as effective analytical strategies.

Another data management challenge arises when companies categorize and organize data without first considering the answers they hope to get from it. Each step in the data collection and management must lead to the acquisition and analysis of actionable data to generate the ideas needed to make informed decisions.

Big Data is becoming essential in companies, and for good reason. For 66% of the respondents who participated in the EBG’s “Big Data 5 years later” barometer, Big Data projects create new business opportunities, while 65% of respondents consider that they provide a better knowledge of the customer and his paths. Finally, the improvement of operational performance, which is an important objective, comes in 3rd place with 59% of the responses.

Some examples of judicious use of data

Better targeted marketing campaigns

Companies are collecting more and more data about their customers and prospects, which allows them to adapt their communication according to the targeted profiles. As a result, it is now possible to target consumers based on their recent research, the articles they have read or consulted, the target audience, etc. There is no limit to the impact that Big Data can have in the marketing and trade sector in general.

More relevant content

With Big Data, advertisers are able to create ads with greater impact. Marketers, bloggers and website publishers are able to offer their customers more personalized content based on the people to whom they are addressed. Meeting customer needs should always be an absolute priority and companies that do not embrace these new changes will be left out in the fields of marketing and e-marketing in particular.

Real-time adjusted prices

Pricing has always been a top priority for marketers, especially in terms of monitoring and adjustment. Using Big Data, it is now possible to adjust prices in real time to better meet market standards. In addition, if a user tries to buy a product online but judges that its price is high, it is possible to target them with ads offering similar products at a lower price range.

Improved customer loyalty and engagement

More targeted advertising combined with more relevant and personalized content results in more loyal and more engaged customers than ever before. Customers become ambassadors for the brand and thus improve its image and reputation.

A well measured return on investment

It is surprising to see the number of companies or marketers who do not know how to measure the return on investment (ROI). Big Data takes into account all the steps that make up the customer’s journey, as well as all channels and activities, providing a cost-benefit analysis for each element. This makes it almost impossible to misinterpret the reporting of results.

The MBA in information technology & data management, offered by The American Business School, prepares highly demanded profiles by employers in data management, that are able to:

  • Forecast the evolution of an Information System and design a business strategy policy;
  • Organize the evolution of a structure and drive its governance on strategic axes;
  • Make strategic decisions;
  • Monitor results and ensure that decisions are respected.