Myths About Big Data – Exploring the Realities of Big Data Analytics

Big data is a set of approaches, tools, and methods for processing structured and unstructured data of huge volumes. These are technologies that help solve important problems for business and science. Due to a misunderstanding of the essence of technology and big data management services, myths have arisen, which we will understand in this article.

Myth 1: Technologies Based On Big Data Will Replace People

The myth is based on statistics that say that about 80% of work tasks can be automated. This is a big problem in terms of education: a huge number of personnel will have to be retrained for other professions. To some extent, this myth is correct. Many people will have to give up their current activities. But there will be no cardinal unemployment. New areas of knowledge give birth to new professions.

For instance, when leveraging big data for targeted advertising systems, specialists will be essential not only for their analytical skills but also for interpreting data about deal sourcing to optimize marketing strategies. He will monitor the operation of this system and the health of the mechanism. Now such people as “Data Management Platform Operators” appear on the market.

The control and monitoring of processes is always carried out by a person. Do not be afraid that people will be left without work. Other professions will appear, as has happened many times in the history of mankind. After all, the printing press once changed the history of printing and made the census of books unnecessary.

Myth 2: Data Must Be Collected in One Place

The need to train algorithms leads to the emergence of companies and services that aggregate data in one place. This can cause problems with security and access to user information.

Data aggregation for training artificial intelligence, algorithms, and machines is no longer as important as it used to be. Now corporations such as Google and Apple are actively working to make their devices part of a distributed machine-learning network.

Google on its devices has one network that works at the same time. Apple is taking similar steps in the field of new technologies based on big data. For example, in the Google patent “Federated Learning”, everything is built on distributed learning. Data from the phone does not leak to a specific Data-center, but a model arrives, learns, and starts communicating with other models of mobile phones or through a common hub. Thus, privacy is maintained.

A model is an algorithm that can be complex or simple, but the output will be the answer to your question.

Myth 3: Everyone Needs Big Data

There are areas of knowledge where we have to deal with a lot of data to process. But not always Big Data give a tangible result.

Gartner has conducted research on which cases are most popular in various industries. As the study showed, most of the cases were implemented in the field of marketing, targeting, and customer experience. That is, in the most relevant area of ​​customer analytics and customer service – in the field of b2c sales. The fewest cases were implemented in the public sector, but the area of process efficiency for this sector is the most relevant. The scope of new products is still insignificant for this sector.

It should be noted that Big Data is not needed if your company:

Myth 4: All Data Must Be Processed

This myth often comes up when you need to build a model from a distributed file system. Solution providers claim that “all data must be processed”. Here lies the contradiction. There is no relationship between the amount of information processed and the result of the work. An increase in the volume of processed data does not affect the increase in the accuracy of the final model.

The well-known theory, built on mathematical calculations, showed that we get the best results when we build models from small segments, specially selected for the purpose of the model.

The real value of Big Data is not to process as much data as possible, but to ensure that the entire amount of data is segmented and clustered, and to build a large number of models for small clusters.

Myth 5: Big Data Gives Instant And Magical Results

This myth is prevalent in companies that are just starting to use big data. It is not enough just to calculate a recommendation, for example, which product to target, or which product to ship with which. It is important to be able to do the arrangement itself. At this step, many projects stop.

Big Data needs a lot of work. This involves creating a complex design, collecting data, building infrastructure, designing a model, and then finding the right data to help improve processes.

The main task is to embed models into business processes in production and use the solutions found to their advantage.

Big Data is a tool that, in the hands of a skilled analyst, will help you build your business processes correctly. Therefore, if you need management services regarding big data, turn to a professional company. 

Myth 6: Big Data is Only Suitable For Large Companies

This is another myth, successfully debunked by demonstrative successful cases. It is based on the fact that in the field of medium or small businesses, the very collection of data can become a problem.

Systems such as Google Analytics are used solely to evaluate resource traffic, and no additional reports are generated with their help. Many companies still store their data in Excel, and it is clear that in this case, it is too early to talk about using Big Data methods. Ever since GA 4 (Google Analytics 4) has come into use, I am checking my site’s traffic report more in Big Data offered by our Ad partner Ezoic. And, that is much better than Analytics as I feel they share better and more detailed reports.

Also, for those who want to try high technologies, there are data exchanges where even small companies can afford to acquire data around which they can build a business or improve organizational and marketing processes.

Final Thoughts

Big data analytics enables a better understanding of customer needs and preferences, which helps create more customized and satisfying products and services. If you are looking for a reliable provider of big data management services, we recommend that you turn to Digiteum which offers great big data management services. The company has extensive experience in data analysis and management for various industries.