How companies are using Big Data to boost competitiveness

At a time when the market is eagerly mobilizing around the need for companies and organizations to adapt to the constant competitive movements and to identify new opportunities, it is worth taking into account all the tools that are available for us to achieve success. One of them will undoubtedly correspond to the capacity to collect and interpret large volumes of data and to use the analyses resulting from them for our own benefit.

As early as 2013, in the Oxford Economics Survey, technology and innovation were referred to as important pillars in the growth strategy of Small and Medium Enterprises. Through the use of Big Data tools, we have a much greater capacity to analyse and predict market and consumer behaviour, which practically puts SMEs at a level similar to that of large organizations. By implementing these solutions, they can increase productivity, anticipate problems and new approaches, and essentially provide a more satisfactory response to customer needs, which ultimately means gaining competitive advantage over competitive proposals.

Whether we are talking about developing and creating products and solutions, or the ability for organizations to improve their marketing and sales strategies, the possibilities created around Big Data are endless. Proof of this is the large number of organizations (97.2%) who indicated that they are actively investing in Artificial Intelligence (AI) and Big Data initiatives in NewVantage Partners' report on this subject in 2018. In addition, 98.6% mentioned to have as a goal to create and/or develop a data-driven business culture.

Given the context, it is quite natural that tens or hundreds of organizations are already, currently, largely mastering the analysis of the data collected through their processes. With examples multiplying with some transversality, sectors such as health, retail, construction, transport or financial services end up being highlighted by the use of this type of tools with success.

In the case of retail, for example, the way in which the interaction between the organization and the consumer is established has undergone profound changes. So much so, in a study conducted by McKinsey Analytics in January 2018 ("Analytics Comes of Age"), industry leaders indicated that they have had a significant impact on the use of such tools in marketing and sales, investigation and development. This is closely linked to the way in which consumers dominate technology. An example of this is that a consumer can start a purchase by viewing a product in an app, buying it online and picking it up at a physical point of sale, or even receiving it at the place of work, which entails immense coordination and multichannel data management by the organization.

As an example, the american retailer Target presented a solution several years ago that crossed the register of baby products viewed by consumers to target specific communication actions according to the specific time of pregnancy. This way, it has become possible to identify popular products for each trimester of gestation and to recommend them to consumers at the right time of demand.

On the other hand, if we look at a traditionally more conventional sector in relation to technological innovation and the change of its processes (manufacturing, administrative, sales and even brand promotion), we find in the industry a series of opportunities to be exploited by the competence of data analysis. From product optimization and the production process itself, quality checking, own-company management or after-sales service, all these areas of work can see their operation being improved, allowing to reduce costs, increase the margin of profits and provide customers with a product of higher quality, durability and safety.

In 2014, for example, BMW used Big Data to detect vulnerabilities in the prototypes of its vehicles, with data to be collected from sensors placed on the tested prototypes and on vehicles already in use. With this solution, it was possible to identify weaknesses and error patterns in the products, allowing the engineers of the German manufacturer to correct these faults even before the vehicles move into production. In another example, Caterpillar Marine was asked to analyze the performance of hull cleaning in a customer's fleet of ships. It was quickly realized that the frequency of the cleanings was not sufficient and that the alteration of this service would compensate, in the long term, in the maintenance of the ships.

Similar to these, many other examples could be pointed out to illustrate how companies and organizations of all sizes are betting on this type of approach to improve the way they are presented in the market. However, it should be emphasized that the use of these tools can not and should not be done in a "blind" way and without any kind of defined criteria or objectives. Given the enormous ease with which it is now possible to obtain data along the entire value and production chain, it is important to define a strategy for obtaining them (which are really important for the business and how can they be analyzed) and for the analysis process, that is, so the conclusions to be drawn must, in fact, improve the processes of organizations.

I recall the quote by W. Edwards Deming that says "Without data you are just a person with an opinion". Because it is this essential access to information about our products and/or services, our customers, our production process and even our talents that makes Big Data something valuable to the success of organizations.