If you are interested in the influence of technological innovations in the corporate world, you must have heard of Big Data. This term refers to the countless volumes of data generated at all times in small, medium, and large companies.
We are not exaggerating. According to a recent BSA | According to the Software Alliance survey, about 2.5 quintillion bytes are created daily. It is not difficult to imagine the effect that so much information has on the planning and decision-making of an enterprise.
This data reveals valuable insights that can optimize communication, internal processes, sales, and your brand’s overall bottom line if used well. To do so, however, you need the basics: knowing how to put this idea into practice.
That’s what we’ll cover in this article. One by one, you will know the Big Data steps that must be followed to reap the rewards of the investment. Check out!
Data Collection And Cleaning
Of course, the first step is to develop a strategy that makes it possible to collect the information. For this, it is interesting to define the objective of the action previously.
If the intention is to gain knowledge about consumer behavior to feed the marketing team, the project can revolve around collecting data such as:
- demographic information;
- search and purchase history;
- device type and operating system;
- email address.
With this knowledge, your company has the opportunity to plan to reach the target audience more accurately. It is worth remembering, however, that this process must be done transparently and proves the consent of the person whose data will be collected. This practice is essential for the brand’s reputation and avoids legal complications, especially when the GDPR comes into force.
The Pre-Processing Step
Popularly known as data cleaning, this phase is one of the most decisive for the successful use of Big Data. The objective here is to identify and eliminate anomalies that could compromise the efficiency of the process.
Cleaning is performed by a thorough inspection of the collected information. Using statistical methods, it is possible to recognize deviations and determine their relevance to the analysis. Thus, null, duplicate, or contradictory values are removed from the equation.
Pre-processing is crucial to ensuring the legitimacy and effectiveness of the collection, but it can serve other purposes. At this stage, evaluating what was collected generates suggestions to improve future activities and enrich the database.
Translation of the English term “data mining,” the name of this stage is quite intuitive about what it proposes. Simply put, it is examining a vast amount of data to extract consistent patterns. Do you remember that we talked, at the beginning of the text, about the superhuman number of information generated daily by Big Data?
That is, this characteristic makes it impossible for mining to be carried out without the aid of specific programs. To this end, we use learning algorithms that combine concepts of artificial intelligence and machine learning. Once found, the patterns go through a validation process and can finally be considered valuable information.
Automating this process also prevents errors caused by human interference. Our search for behavior patterns can corrupt the essence of the step, which is to look at the data in general. Thus, issues that would go unnoticed by us end up being identified by purely statistical analysis.
This is one of the Big Data steps that most depend on a clearly defined strategy. Efficiently analyzing the content of the collected, filtered, and validated information will generate beneficial insights for the enterprise. Therefore, we must emphasize what we discussed in the first topic: knowing your goal.
If the idea is to clarify the economic scenario in which the company is inserted, a descriptive analysis is necessary, providing real-time performance data. You should commit to developing predictive analytics to anticipate trends and possible future plans.
Likewise, it is possible to take a prescriptive approach. It aims to clarify the results of actions already taken and, thus, provide insights that help optimize the strategy. Finally, we have the diagnostic analysis, which seeks to contextualize possible failures during some process.
Ensuring an intuitive data visualization is essential for the enterprise’s success. After all, the information gathered was not created during the process. They were already there; they just hadn’t been panned and exposed. At this stage, the challenge is to make access even more accessible for everyone involved in the operation.
For this, graphic adaptations are used to eliminate noise and factors that may divert the focus of the person responsible for the analysis. Charts, infographics, spreadsheets, and tables are helpful resources to facilitate understanding. As each individual can count on a greater inclination to one of the methods, it is essential to know the characteristics of the professionals involved.
Nowadays, looking at a company as a range of departments with distinct functionalities makes no sense. It is necessary to understand the importance of having sectors aligned in their objectives, which, therefore, can have certain integrated functions. Thus, Big Data steps must generate collaboration between all parties.
Want an example? Think about your marketing and sales teams. If the first identifies that the pattern of consumer behavior points to a preference for telephone approaches, the second can adapt its methodology to improve its success rate. Likewise, if the commercial team identifies the channels that generate the most conversions, marketers can focus their efforts on them.