How to harness Data Science in your company

The importance of data science for business is something we hear about more and more. The digital transformation, hastened by the impacts of the Covid-19 pandemic, has unleashed a revolution in the global economy, leading industries from all segments to seek ways to process large volumes of information to gain a competitive advantage. More than ever, companies across sectors are investing heavily in tools and technologies that will help them get to know their customers better to provide an exceptional customer experience thus.  

The strategic use of data allows organizations to improve various aspects of their approach to the company-customer relationship. These include achieving greater speed and assertiveness for the best sales opportunities or improving the consumer experience using algorithms that make it easier to analyze behaviors and feelings, predict needs, guide strategies, and teach machines through experience to provide an increasingly humanized service. In turn, the information sourced from data science models is used to guide business processes and achieve organizational goals.  

What is data science?

There are different definitions for data science, a field that involves areas such as statistics, computer science, and software engineering, among others. Technology experts say it is “an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract value from data.” The mathematician D.J. Patil, one of the most prominent experts on the subject, sums it up as “the ability to extract knowledge and insights from a large and complex set of data.” The University of Berkeley devised the Data Science Life Cycle, organized into five stages: capture, storage, processing, analysis, and communication.

More than ever, the information sourced from data science models is being used to guide business processes and achieve organizational goals. The data is stored and presented in various formats: videos, images, text, spreadsheets, signal sequences, tables, etc. 

However, the use of data science for business began to gain weight over the past two decades, when data became “the new oil”, giving way to the emergence of new professions such as the “data scientist.” After all, data is only valuable if people can analyze it and turn it into knowledge for generating insights.  

With the advance of technology, knowing how to interpret what the data is showing and taking strategic actions using this kind of foundation will leverage companies’ results and reinforce how they relate to customers. However, few companies know where and how data analytics can create business value.

Data science for business 

Data science improves business efficiency and creates greater value through the information captured, developing analytics that generate insights by maximizing business, mitigating risk, increasing self-service retention, guiding the product and service strategy, and minimizing friction. 

Forward-looking companies are increasing their data science, analytics, and AI capabilities to connect more closely with their customers for an increasingly humanized service and to identify CX opportunities in real-time. Those that advance faster will have a significant competitive edge, and those that lag behind risk becoming irrelevant. 

Today, companies can legally collect smartphone and interaction data for financial transactions and other purposes, generating valuable insights into their customers. It is now possible to access a wide range of internal datasets on customers’ interactions, transactions, and profiles. These third-party datasets show behavior, including social networking activity, and new data on customers’ health, sentiment, and location (in stores, for example) generated by the Internet of Things (IoT).  

Nevertheless, the data we have needs to make sense and not be combined at random, so it is essential to have a specialist team capable of providing the best insights. Analytics creates value when big data and advanced algorithms are applied to solve business problems. By doing this, companies can create an analysis strategy that delivers value to their business by identifying, scaling, prioritizing, and eliminating all applicable use cases.

Today, predictive platforms allow companies to measure better and manage their CX performance using data science applied to business. These platforms also inform and improve strategic decision-making and create an accurate, quantified view of the factors driving the customer experience and business performance. They also produce a comprehensive view of each consumer’s satisfaction and value potential, often in real-time.  

At Atento, we constantly seek the best practices and the latest resources to generate valuable consumer experiences and the best results for our customers. We consistently apply data science for business in our deliverables. In this area, we work based on three data analytics models:

Descriptive Models: we observe the past data set and describe aspects that can summarize it or be relevant to the analysis; such as mean, median, maximum and minimum values, standard deviation, skewness, distribution, correlation with other variables, etc. 

Predictive Models: based on the data available, the idea is to extrapolate conclusions about the future or what is not present in the data.

Prescriptive Models: directs us toward the best strategy to change the future in our favor.

In this regard, information compiled through data analysis streamlines the design of exclusive and personalized experiences. But the information will only be efficient if it is categorized and interpreted correctly to generate insights and improve business efficiency.