“Data is everyone’s land and no one’s land”, says Walter Sosa Escudero author of the book Big data: A brief manual to learn about the data science that has already invaded our lives (Siglo XXI, 2019). With this phrase, the expert starts with a warning: the new era of data promises to revolutionize the way of doing science and business, but there is still much to learn.  

Big Data is a term that strictly has to do with the generation of a large volume of data that arises from the interaction with interconnected devices such as cell phones, GPS, social networks, computers, credit cards, etc. 

Unlike the data obtained from surveys, experiments or other recording methods, “Big Data -says Sosa Escudero– are completely anarchic and spontaneous, mediated by a device”. 

Therefore, beyond the massiveness of the data generated, the great challenge for organizations is to understand what they can do with it to improve the industries in which they operate.  

Changes in companies brought about by Big Data 

What makes Big Data capable of revolutionizing business is that it can answer questions that could not even be asked before.  

With such a large amount of information, the data can be analyzed or tested in any way that companies see fit. By doing so, many industries today can formulate problems they did not know they had or even considered unsolvable and give them a solution to improve their services and increase their profits. 

For example, the banking sector highlights the advantages of Big Data to generate predictive models that reduce churn rates. “I think that the greatest contribution that data science has given us, is the capability to reach customers with a precise and personalized offer, at the right time (…). Thus, we avoid sending massive emails and we communicate with the client by proposing an ad hoc offer according to their profile and according to their analyzed information”, comments César Leguía, Manager of Commercial Development at BBVA in Peru 

One of the sectors where data has the greatest impact is healthcare. Every minute thousands of data from patients are collected from their medical records, diagnostic tests and, increasingly, through wearables: the most famous ones are smartwatches that monitor the heart rate or physical exercise performed by each user. 

In an article in El Confidencial, Jorge Capilla Cano, partner of Consulting Service-Data Analytics at EY, says that thanks to Big Data it is now possible to create prediction models to supply material to hospitals before they go into shortage. 

The models can also help decongest emergency services, detecting recurrent patients to generate preventive plans. Similarly, other analysis can help personalize medicines or reduce the time lag between the beginning of research on a drug and its release to the market 

Retail is one of Big Data’s biggest beneficiaries. Not only beneficial for the customer, through the analysis of demand and purchasing patterns, but also in its operations, where it can generate stock forecasts and improve the flow of supplies, optimize the assortment based on parameters such as the profit that they report or the volume of sales they generate and even optimize the pricing strategy with special offers in higher-selling areas 

Big Data challenges 

The knowledge that is generated around this new generation of data is unstoppable. However, companies still face numerous challenges in extracting real, high-quality data from such massive, changing, and complicated data sets. 

On the one hand, talent is a challenge when it comes to promoting an industry based on data. Data analysts, data scientists and engineers in Big Data are some of the most wanted profiles by companies and are among the best paid experts. 

Being an emerging area, resources are still scarce. Many companies offer specialization programs and other benefits packages to get specialists to help them manage large volumes of data and thus improve different processes. 

On the other hand, while the information that we could access before was what we had stored in our ERP and CRM systems, for example, now Big Data opens the door to various sources: social networks, results of marketing campaigns, geolocation, internet of things, etc. 

With data from such different formats and origins, the difficulties in obtaining, structuring and integrating them are not few. In addition, data can change rapidly, so it is important to have an appropriate data quality process and very high processing power. 

For this reason, data management tools that make it possible to connect with different sources, structure and consolidate information, validate it, and then integrate true and processed information into business analytics tools are becoming increasingly central.