By Gabriel Lobitsky, Infor sales director for Brazil and South Latin America
By generating insights, data science is able to outline strategies, optimize actions in sectors with critical operations such as retail, supply chain and logistics
Data analysis goes beyond big data and its growing adoption. Recently, a survey by Forrester Research showed that, on average, 40% of global data analysis leaders say they are already implementing or expanding the use of big data technology, and 30% of those who have not yet adopted plan to do so by next year. There is no doubt about the ability of data science to transform traditional industries and business models. However, massive adoption encourages discussion about the transformation of data into insights and their evolution, the machine learning, able to 'predict future problems' by using algorithms and data pattern analysis to identify and indicate effective solutions to business problems.
By using algorithms and a mathematical language, data science can create transformative solutions for businesses. Using predictive analysis, the big data it's the machine learning allow tcreate strategies, optimize actions, interact with customers and, of course, boost sales. But this is only possible because the cloud has increased computing power on demand, making it easier to store and analyze data. Today, predictive models are able to understand the critical side of each operation through software.
See how technologies can generate insights and transform some operations:
- Supply Chain: complex for many companies, the smooth functioning of operations is essential to minimize costs with delays and lack of products in stock. Data scientists are making full use of computational power to model schedules and anticipate information about events that could negatively impact operations, such as the combination of machine learning and big data. Together, the technologies provide better visibility and understanding for companies to identify normal delays and those that are the result of unforeseen events, such as natural disasters, strikes, etc.
- Logistics: data science is allowing the next generation of business software, the result of predictive solutions, to tell the user the amount of inventories needed to meet future demands; that tells you how to price items to ensure long-term profitability; and point out the ports with the best shipping capabilities, in order to minimize the impact of delays.
- Retail: normally, smart CRM applications act predictively in the sector, and show potential buyers and most sought after products. Complementary technologies, such as sensors and RFID, give stores a wider visibility of the stock to know the location and movement of a product, for example. If associated with data interpretation, the technologies will allow a true digital transformation in physical stores, which will not only be able to understand the consumer's journey, but also offer specific products and items. In the clothing sector, for example, data captured by sensors can expand the offer of products that adjust to the customer's taste and size. And, this same type of solution can support the management of storage and inventory at retail, by providing information on inventory levels, eliminating the need for manual and time-consuming counting, with a greater incidence of errors.
Data science needs to be understood as a fundamental component for digital transformation, as it is the only way to create solutions that, in fact, impact decision making. The algorithms used in the software should not be like a closed black box, which does not show users what is going on inside, and on the other hand, there is no need to bombard the user with disconnected information. A middle ground is needed for science to become accessible and understandable to everyone, especially for users of companies with critical operations, such as retail, logistics and supply chain, where the word optimization is imperative.
EN 
PT 
