Big Data Optimization Techniques that Are Redefining Analytics in 2020


The market for big data is surging rapidly. It is estimated to be worth nearly $49 billion by 2025. However, the big data ROI of big data strategies vary for different businesses, since some utilize it better than ever.

Refining data optimization strategies must be a top priority. Companies that use the right big data optimization techniques will see much better results in the years ahead.

The Importance of Proper Big Data Optimization

We will cover the importance of identifying the best techniques to optimize big data shortly. However, we first wanted to discuss the evolution of big data as a technology.

Big data is analogous to a raw material can be used to generate information. The patterns that these profiles identify lead to greater knowledge, which in turn allows decisionmakers to come up with more enlightening ideas. The software incorporates the use of data collection from all areas of an organization in “data warehouses” (data warehouses), which are periodically analyzed to discover the most viable insights regarding the performance of any business.

But many companies have encountered an unexpected opportunity: a growing range of datasets in the age of the internet. A wider array of networks corporate data and found a shortcut to knowledge. In the last twenty years, a lot of new knowledge has been accumulated all over the world. This data has become more accessible, which expands the ability of companies to utilize it in their algorithms. As soon as knowledge is newly generated (medical articles, travel guides, reviews of pop culture), it is made available to the public. The value of Internet accessibility has surged and the incentive to gain such access has risen to par. Furthermore, smartphones are becoming more universal, which brings knowledge to virtually all places (the subway tunnel and the field included).

Therefore, the development of the information is reverting to paths it had left behind. Every device connected is also a sensor capable of collecting data on the location of people, the performance of the machines, the contaminants of the factories, the humidity of the fields, the health of patients and people’s feelings – according to content that they publish on social networks. This gives decisionmakers greater insights into their behaviour. This proliferation of sensors has generated massive torrents of Big Data. This leads to huge opportunities for data collection, much of it in real-time, which at the moment are faster than the ability to add them to the level of knowledge. But the industry is going full speed ahead to catch up.

In this publication, you will read about the growing role of Big Data, a term that encompasses a growing awareness among executives, politicians and individuals about the new availability of data to assist in the collection of decisions. You can also learn about the importance of using new data optimization strategies. Let’s take the case of a data-driven system for improving car navigation. It uses big data to propose a traffic route and uses information-based alternative in real-time, generated from the data of thousands of mobile phones with GPS incorporated. Or the sensors on the meters of electricity, which help electric companies manage peak loads.

A booming industry will use Big Data to optimize its technical infrastructure. These organizations collect, store and give others access to the data. Continuous access has replaced the centralized data warehouse model in the batches – the transportation systems which must be updated in real-time when the bus or the train will arrive. The accumulated data is quickly transformed into new information to highlight insights about  the performance of the system. At the forefront, these applied big data techniques illustrate greater knowledge and even provide enlightening ideas from new types of data. For example, a medical researcher collected an extensive database of medical records and loaded them into a machine learning algorithm without any specific objective. The program found that type II diabetes is not one, but rather four different diseases.

Neurologist Antonio Damasio (who participated in Future Trends Forum) has written that perception developed in humans with the awareness of our inner state to integrate data on the outside world collected from the five senses. Big Data is adding an outside consciousness to our information systems, which were originally built to describe the internal state of the organizations.

Interview with Big Data Expert on Leading Optimization Strategies

In 2020, big data is and will continue to be one of the main drivers of growth for companies around the world. A large part of the success of companies like Amazon and Google is the massive amount of data they have access to and the way that they use it.

However, there is a misconception that big data is only affordable tool large companies with the ability to make highly scalable technological investments and those organizations that have a pool of experts in the field.

Sergio Ayala, Master in Analytics from Universidad de Los Andes, and current Head of Analytics and Digital Metrics at EL TIEMPO Casa Editorial have explained the importance of big data for companies of all sizes. Ayala emphasizes the importance of using internal information to help meet the objectives of companies regardless of their size or sector.

Here is a summary of an interview that Dr Ayala participated in earlier. The findings here emphasize the importance of using the latest big data optimization strategies in 2020.

Question: Sergio, what do you think is the most important thing to implement a data strategy in a company regardless of its size?

Answer: The most important thing is to connect the data strategy to the needs of the business. While it’s true that the goal is to bring organizations into a data-based decision-making culture, it’s also true that the most important thing is to understand the business itself. Today’s companies are collapsed with information, so it is necessary to clarify from the beginning what will be measured and what will not.

Every analysis that is made must always answer a specific question that the organization needs to answer, and it must always work to make sure that the information that is being collected and analyzed serves to make high-impact decisions.

Central Principles Behind Modern Big Data Optimization Strategies

Understanding that data is a tool that can be used by any type of company, what advice would you give to those who work in small or medium-sized companies so that they can start taking advantage of the information from today?

  1. Organize and clean up.

While it doesn’t have to be perfect, the information must at least be available and decipherable for analytics to do its job. This takes time and one must be aware of it.

  1. Avoid perfection.

It is easy to confuse correlation with causality in analysis, and an error in analysis can lead to a wrong course of action. For example, if you are taken to a media outlet, the notes of older journalists may be more engaging to the audience than those of your younger colleagues, but that does not necessarily mean that age is the cause. Older journalists may have a greater affinity with the reader profile, or may have been trained in better audience engagement practices and therefore have better results.

Another mistake is that imperfect data can lead to procrastination. At first glance it seems logical to make a decision only when there is 100% certainty of the data, however, this can lead to paralysis because the numbers are never perfectly accurate. We see this in sciences such as finance or marketing that have systems for working with imperfect numbers.

  1. Start small.

Once the barrier of organization and data cleansing has been overcome, early victories must be worked on to gain acceptance and credibility within the company.

  1. Break down cultural barriers.

This is an inevitable point and must be taken into account when implementing a data strategy. When human logic meets that of a computer running a mathematical model, it is natural for personal insecurities and organizational politics to arise in this field.

While this may slow down a data strategy, it cannot stop it.

Many people think that to obtain the benefits that data offers, large technological investments must be made. Is this true?

It is not a question of large technological deployments. It is simply a matter of understanding that the information that is stored and analyzed must be based on a business question.

It is clear then that any company can begin to benefit from the information it has regardless of its size, and that to have a data strategy you do not necessarily have to make large investments.

The important thing is to know the company in-depth, to have clear objectives to be achieved with the organization of information and to have basic knowledge in analysis, management and data collection.





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