Today, obtaining actionable insights from data is a task much like mining: Immense amounts of material must be processed to get a diamond. Luckily, technology is on our side to optimize these processes.
In this article, we will tell you why investing in data modeling and mining is essential to improve your level of analytical maturity and what types of analytics exist.
What is data modeling?
It is a process that seeks to enable organizations to view and understand the information they have. To achieve this objective, a roadmap is created to define the type of information collected, the relationship between datasets, and the methods used to store and analyze the data.
Data modeling is determined by the needs of each business; thus, it must function as a “living document” capable of evolving and adapting to new scenarios.
Facilitating the design and creation of databases and strengthening the consistency of information throughout the company are some of the benefits obtained from data modeling. Still, its greatest strength is its ability to leverage multiple forms of information exploitation.
Why would data mining or exploitation put you on the path to analytical maturity?
The goal of data mining is simple: to convert large amounts of information into actionable knowledge. This is achieved using technological tools, such as artificial intelligence, to identify patterns and trends.
Thanks to this practice, many companies already use their data for procedures ranging from predicting their users’ behavior to revealing relationships that were previously very difficult to detect.
Remember that only some of the analytics we can extract from data mining will be the same. Several techniques and analytics types can be performed depending on the business objectives pursued by an organization.
What types of analytics can be obtained from data mining?
Each of the following analytics answers specific questions an organization can ask anytime.
As we will see later, being clear about the purpose we pursue is essential for organizations to create value through data mining.
They focus on improving our understanding of some aspects of the business. For this, they use statistical methods and viewing tools.
Based on historical data, this analytics is beneficial for retrospectively evaluating what happened under certain circumstances, detect trends and explain dips and spikes in demand.
This type of analytics allows companies to anticipate user needs and manage increasingly timely and accurate campaigns.
It is based on artificial intelligence and is generally used to anticipate events taking into account business rules and large amounts of data.
Thus, if a company has data on the behavior of its customers in recent years, this analytics could shed some light on what can be expected to happen shortly.
If predictive analytics tells an organization what might happen, prescriptive analytics goes a step further and suggests how it should act to get the best benefits.
Here simulations, machine learning-based recommendation engines, and complex event processing are critical.
Some of the ways data mining obtains actionable insights for companies.
What should I do to have successful data exploitation?
We summarize in four steps the actions that any organization should take into account to obtain value from data analytics:
1. Have a purpose
For data mining to create value, any data exploitation process must have a clear objective as its starting point.
For this reason, data specialists must work hand in hand with business strategists to identify precisely the challenge they want to solve through the insights they will look for in data.
2. Choose the relevant information.
Not all of the organization’s data will be relevant to building a solution to the challenge we have set ourselves.
In addition to identifying helpful information, it is also essential to purge the data in this step, that is, to ensure that it has followed the policies and guidelines we have established to provide that our information is reliable.
3. Use technological tools
When we have an objective and a set of data capable of giving us the answers we seek, the next step is to apply tools such as machine learning and artificial intelligence to help us transform data into knowledge.
There are many data analysis techniques. Some, such as associations, are designed to examine historical data and detect, for example, consumption habits that were not easy to see with the naked eye.
We also find analysis techniques focused on segmentation or making predictions.
As we can see, each analysis technique has a specific objective. The success of our data exploitation will depend on choosing the appropriate analysis method for our purpose.
4. Move from knowledge to action
The information data analytics gives us is valuable but only reaches its true potential when we translate it into actions.
As in all the steps we have seen so far, the important thing here is always keeping sight of the initial goals we set for ourselves. This will also help us measure the effectiveness of our efforts and make any necessary adjustments.
Are you looking for a partner to take the use of your data to the next level?
At Pragma, we have years of experience working with companies at various levels of analytics maturity, and our teams of data professionals are ready to get down to business.
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