Data analysis is the sum of techniques used to gather and process data to generate information that would not otherwise be obtained.
The essence of data analysis is the intersection of information that, “loose”, would not make sense.
Therefore, when analyzing data, what you do is extract insights using a set of data that can reveal something , after being subjected to certain statistical processes.
How does data analysis work?
The operation of any data analysis procedure invariably consists of collecting, cleaning, processing and organizing data so that it can be interpreted.
Thus, what data analysis does is, in practice, bring to light a facet of a previously hidden context.
To achieve this, it is essential to apply technology so that data can be stored in large volumes and processed using advanced calculations.
Data analytics and advanced data analytics: what are the differences?
Another way to understand what data analysis is is by knowing the difference between conventional and advanced analysis.
In “standard” analysis, the collection, cleaning and treatment processes serve to identify patterns and behaviors that already exist.
Advanced analytics uses sophisticated techniques, such as Machine Learning , AI and predictive modeling, generating insights and models to predict scenarios.
What are the 4 types of data analysis?
An example of data analysis in the commercial sector is when a store uses the data it has about its sales to find out which products sell the most.
With the information and insights generated , your managers can balance the product mix, set better prices and create promotions based on the sales volume each month.
Data analysis goes much further and can be classified in at least four different ways.
Let’s get to know each of them from now on.
Predictive analysis
Considered an advanced type of data analysis, predictive analysis, as we have seen, uses refined techniques to build models that help make predictions.
After all, what is data analysis if not a method used to see things that haven’t even happened yet?
For this, predictive analysis is the best option to obtain answers that point to trends and confirm the chances of a hypothesis coming true.
Prescriptive analytics
As Gartner suggests , prescriptive analytics is ideal for answering questions like “what should be done to achieve a certain result?”
In some ways, it’s a type of data analysis very similar to predictive analytics, but focused on a specific, desired outcome .
Therefore, prescriptive analysis, in addition to predicting scenarios, is also used to discover what to do if that scenario is confirmed.
Descriptive analysis
Descriptive analysis can be considered a preliminary stage of an analytical process , as it uses data to describe a pattern.
It is the basis of statistical methods and can even summarize what data analysis is, at least at a more elementary stage.
This is because it generally uses graphic resources or tables to show how a set of data is organized, showing a cutout of a sample.
Diagnostic analysis
In turn, diagnostic analysis aims to shed light on a fact, finding cause-and-effect relationships that can explain it.
The idea is not to determine whether a hypothesis is false or true, but to raise probabilities regarding the object to be analyzed.
How does data analysis contribute to strategic decision-making?
Understanding what data analysis is means understanding what companies, institutions and entrepreneurs do to guide their decisions.
When you are in charge of a business, you are constantly dealing with risks, which can only be mitigated if the decisions to be made take reality into account .
Therefore, data analysis is the best way to guide the decision-making process because it is based on facts.
Essential steps of data analysis
Another way to gain a deeper understanding of what data analysis does is to study its fundamental steps.
Of course, we could go into much more detail, as each of them can be subdivided into several other stages.
In fact, these are steps common to most data analysis processes , which generally follow a kind of common script.
See below.
Exploratory analysis
The simplest example of data analysis imaginable is what is done in the exploratory phase.
It is applied with the aim of describing the general characteristics of a data set , so that one can advance to the following phases.
Therefore, understanding it is essential to understand what data analysis is, since every process in this sense requires at least an exploratory analysis.
Data modeling
Once the data has its nature and possible limitations identified, we then move on to its modeling.
Here, the aim is to organize the data , normally in graphical patterns, so that it can be read and interpreted.
In data modeling, information is organized according to its coherence, relating it according to its causes and effects.
Report building
Most data analysis processes lead to more than one conclusion or inference.
It is not possible to measure what data analysis is without the results of that analysis being formatted and displayed in an intelligible way .
Therefore, the final stage of an analysis is always the construction of the report, a document that will be used to explain the results to the public or interested parties.
Benefits that data analysis brings to companies
Check out other advantages that data brings when prioritized in decision-making processes.
Always informed decision making
It is not wrong to decide based on feeling , because, even in the corporate environment, there will always be room for creativity and improvisation.
What cannot be done is to make this the standard to guide decisions at the business level, since intuition is always subject to error .
Data is an “antidote” to this risk, because, if well analyzed and structured, it is infallible in expressing the truth and pointing to the greatest probabilities of success.
Process optimization
We know what data analysis is and whether a company is data-driven also when the processes are well mapped and nothing escapes the control of its managers.
Processes are nothing more than the logical sequence of steps in an activity with the purpose of obtaining a certain result.
Data analysis is perfect for optimizing data because it uses methods and calculations that are 100% based on logic to determine patterns or point out trends.
Development of market intelligence
Business Intelligence (BI) is the term used to refer to market intelligence, which is the keen eye on a given context and the ability to derive valuable insights from this reading.
It is also a way to develop an understanding of what data analysis is, based on the competitive advantage that comes with the development of BI.
Risk reduction
Data analysis is, as we have seen, one of the best ways to mitigate risks.
If we define what data analysis is from this angle, we will see that, without data, a business is too exposed.
Competitors, fees, taxes, operating costs and even human error become dangerously more present elements when decisions are not made based on structured information .
So data is the safest protection to minimize it.
Smart pricing
One of the challenges faced by even large companies is finding the perfect balance when setting their prices.
After all, the price is nothing more than the production cost plus the profit margin, taking into account the market average.
In certain contexts, establishing a fair price requires advanced calculations and complex analyses, which require large volumes of data.