With digital transformation and, consequently, the acceleration of this process through the COVID-19 pandemic, data has become an extremely important input for companies.
No wonder: today we generate data in tons. By the year 2025, IDC estimates that we will reach 180 zettabytes of digital data produced on a global scale.
But what is this plethora of information for? What to do with all the data your company stores, for example?
The reality is hard and simple: just generating and storing thousands of bytes of data and letting it rest for eternity gets you nowhere. In fact, having data and not using it to your advantage is like having treasure in the bank and not investing it.
That's because data is only useful when we can observe and analyze it. And the great challenge today is to transform this massive amount of information into profitable insights for the business.
Fortunately, there is a science to making this happen. That's right!
It is through Data Science that companies can learn how to make good use of their data - and thus generate great results for the business, in the short and long term.
Keepg readinthis article and discover what Data Science really is, what its benefits are and why your company needs to invest in it immediately!
Let's get to the point: Data Science is the study that deals with large volumes of data using tools and techniques to find invisible patterns, obtain meaningful information and help make business decisions.
In short, Data Science is the macro area that studies how to generate, store and analyze data for the development of companies.
Thus, it incorporates various disciplines such as data engineering, data preparation, data mining, predictive analytics, machine learning and data visualization, as well as statistics, mathematics and software programming.
The primary role of a data scientist is to analyze, often large amounts of information, in an effort to find useful information that can be shared with executives, business managers, and collaborators.
The insights generated by Data Science help companies to identify new business opportunities, increase operational efficiency and improve the areas of marketing and sales, among other advantages.
The truth is, the correct interpretation of data can revolutionize every aspect of a business's strategy.
Through Data Science, it is possible to:
And this is just the tip of the iceberg.
In healthcare, for example, the use of Data Science includes the diagnosis of medical conditions, image analysis, treatment planning and research. Likewise, in sport, teams can analyze player performance and plan game strategies through data analysis.
The truth is, without Data Science, companies and organizations across all industries miss out on valuable opportunities for strategic improvement—and make the wrong decisions.
After all, as we have already discussed in another article here, it is the data that lead to the facts!
When a company understands the importance and sees value in the information obtained from data, we say that it has finally developed a data-driven mindset.
Thus, investing in Data Analytics becomes a fundamental part of the process.
Within the universe of data analysis, it is necessary to keep in mind that data are not uniform and, consequently, their results may vary, depending on the type of analysis used.
See below the 4 types of data analysis that dominate the market:
Descriptive analysis is the least difficult and therefore the most common. This type of analysis answers the question: “what happened?”. This analysis model is based on both historical and current data, with the aim of detecting patterns.
Following the same line of descriptive analysis, diagnosis also serves to understand the current context. Its differential is to try to understand the reasons, that is, "why did it happen?".
Diagnostic analysis aims to find both the causes and the correlations with key variables of an event. Your research is based on historical data to find the root of an event or behaviors identified in the first step.
Therefore, this method is suitable for companies that have already been through turmoil and in the future, want to have the means to deal with the same problems should they recur.
Predictive analytics aims to answer the question “what will happen?”. Based on the reading of historical data, it seeks to anticipate the effects of a decision.
In predictive diagnosis, statistical models based on regression are elaborated in order to establish cause and effect relationships. With it, a company can predict events or behaviors with statistical calculation techniques, as well as Machine Learning resources.
It mainly involves the study of patterns and trends, in a way that allows companies to more easily identify opportunities for the future.
A prescriptive analysis aims to raise possibilities according to a given, answering the question “what should I do?”.
Among all, the prescriptive method has the highest level of difficulty, but consequently has the greatest potential value among the alternatives cited. This method relies on Artificial Intelligence tools and Machine Learning objects to determine probabilities.
It is considered an extremely analysis, which suggests actions to be completed and also points out as possible possibilities of each one.
Now that you know the importance of Data Science and its technologies for data analysis, it's time for us to understand, in practice, the benefits of these practices in business. See below 4 advantages of investing in Data Science:
It is nothing new that, with the advancement of technology, the increase in cyber crimes has become a recurring concern within organizations.
Being able to have any type of cyber-attack forecast to protect both systems in general and prevent data leakage has become one of the top priorities for companies.
The Data Science technology helps exactly at this point: by using it, the company is not only able to understand some recurring patterns of these crimes, but also to predict some scenarios and outline its security strategies with greater assertiveness, identifying gaps and possible vulnerabilities in processes or software.
One of the great advantages of Data Science is predictability. As we have seen before, data analysis can bring high predictability of scenarios, whether financial, market, demands, prices, etc.
With a large volume of data available, the person in charge of the area can carry out a preventive analysis of the company in different areas and sectors, optimizing or reallocating resources whenever necessary.
From a competitive point of view, this resource is widely used when the company needs to understand consumer behavior and to what extent its product or service is having market adherence.
With this predictability in hand, companies are able to have a 360º view of their business, which directly interferes in decision making, since the organization is aware of the possible results, making it much more strategic.
It is only with accurate data analysis that we can optimize results, especially when it comes to companies that have growth methodologies or startup models.
With Data Science, it is possible to analyze which campaigns are being profitable and generating positive results for the company, making the necessary repairs to optimize processes in order to generate results. In this way, the insights from the data analysis are of great north for decision making in order to improve the returns that certain areas of the company are having, for example.
In financial terms, there are great chances for organizations to become more organized and profitable in general, since decision-making based on data analysis is done in a more agile way and ahead of competitors that do not use this technology.
As mentioned above, it also helps in the pricing of products - since decision making is based on consumer responses - and also in the personalization of services, helping to develop new products according to market demands.
Retaining customers has never been an easy task. However, with the advancement of communication and all services increasingly technological, consumers are becoming more demanding as new products come to the market.
The demand for more and more innovative products makes it difficult to retain customers and, therefore, using technology to obtain a competitive differential becomes essential.
With Data Science, hyper personalization becomes possible: companies are able to collect a large volume of data from their customers and, in this way, understand their needs, creating increasingly targeted actions.
Likes, channels, purchase preferences, birthdays, personalized campaigns, platforms most used by customers are some examples of the large volume of information that is in the data that customers produce, becoming a great ally when the company thinks about strategies to differentiate and deliver what the consumer wants and expects.
We already understand that Data Science is a technology that is constantly evolving, whether due to changes in market demands or even innovations in the technology itself.
It is estimated that this market will reach a global value of 79.7 billion dollars by 2030, according to a report released by Allied Market Research.
It seems obvious that adopting this type of technology only brings benefits to organizations, especially when it comes to decision making, right?
However - and, incredible as it may seem - a very large portion of companies still outline their strategies without using data resources to guide their decision making.
However, this scenario will be left behind. The trend is that data-driven methodologies are increasingly present in the culture of companies and that this technology becomes essential for the survival of organizations, especially in the sense of guiding strategies and devising more assertive plans to reap good results.
Regarding Big Data, the scenario also looks promising. The advancement of the Internet of Things (IOT) enables an increasing and more precious production of data and information by consumers, which, consequently, has a positive impact on Data Science solutions.
Furthermore, when it comes to the world economy, we can expect other sectors besides those involving technology or marketing to open the door to Data Science. Sectors such as agriculture and health, for example, are beginning to understand the benefits of adopting new technologies to improve their processes, modifying the entire production chain of consumption, especially with regard to innovation.
Data analytics is increasingly helping companies leverage their own data to map growth opportunities. Thus, the result could not be different: smart business, increased profit, successful operations and happy customers!
That's why Qintess helps its clients make the most of their competitive advantage by structuring the true value of data, both for internal processes and new business models.
We apply intelligence to reveal hidden patterns and trends and gain meaningful, useful and relevant insights for strategic decision making.
Click here to learn more about our Data Science solutions!
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