Home BUSINESS INTELLIGENCE What Is Data Discovery?

What Is Data Discovery?

Definition Of Data Discovery

In order to properly understand Data Discovery, it is necessary to be clear about the concept of Business Intelligence (BI), or Business Intelligence. Basically, BI can be defined as a process in which the data of a company is analyzed. These data, in turn, are converted into knowledge that will facilitate decision-making.

Today, business users are forced to dive into vast amounts of data. This results in them having to wait for days, sometimes weeks or even months, to obtain from the IT department the reports they have required. Taking into consideration that we are in the era of Big Data, in which the speed of data transmission and analysis plays a critical role, these questions cannot remain unsolved.

Indeed, when analyzing huge amounts of data, information and data must be accessible as quickly as possible, otherwise, it may be too late. This is the challenge that companies have to face when they want to find the information they need efficiently and quickly.

And it is here when Data Discovery has come into play, with the aim of solving these types of situations, which occur frequently. It is a relatively young term, introduced only a few years ago, and it is the solution to this widespread question.

The Data Discovery integrates technologies that are oriented towards the users and is based on the discovery of certain patterns and concrete values. Its objective is, therefore, to find the desired information using the minimum possible time in it. Simply explained, this kind of technology provides users with essential data or statistics for decision making.

All these data, which users will have in front of them transformed into diagrams, graphs, etc., make up the result of analyzing huge amounts of information in real-time, providing true business value.

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It can be said that Data Discovery is equivalent to its own software analysis service, which has the following characteristics:

  • The great speed for the user: It is formed as one of its most relevant features. The process of finding the data must be configured to operate quickly and to be able to find the required information almost instantaneously.
  • Ease of use: For this, it is not advisable to involve end-users in the technical details pertaining to the Data Discovery process. Instead, it is advisable to use friendly and simple graphical interfaces that visually show the indications of what to search for, guiding the user through the steps to follow.
  • Of a specific nature: The Data Discovery process must be designed for well-defined purposes, that is, it must be focused on obtaining the requested information within a certain scale, which does not involve having to analyze more extensive data than is strictly necessary.
  • Flexible: As has already been saying, Data Discovery must be designed with a specific effect, but it can also be applied within the company to any other function, in any other department, as long as said department can access the data you want. are subject to analysis.
  • Collaborative: It needs to work seamlessly with other BI processes. which, in this way, will improve the quality of the data and make it easier to access them.

Data Discovery is thus constituted as a tool that enables the end-user all the advantages of integrating Business Intelligence and self-service inefficient coordination.

As an example, a sales manager would have direct access to the data of which are the most profitable products and clients for a period of time, without having to resort to a deep analysis prepared by the Technology department. This, without a doubt, would mean a considerable saving of time, and a greater increase in the productivity of the company.

Transforming Business Intelligence

On many occasions, data discovery falls into the same category as Big Data. since the three elements used to describe this phenomenon come within its scope: variety, speed, and volume. Data Recovery makes it possible for the user claim to be able to handle large amounts of data and obtain results quickly.

The main advantages are:

  • The user is able to explore data, whether structured or not, expanding its scope and optimizing and improving the quality of its reports, analysis, work, and decision making.
  • The variety of sources becomes limitless.
  • Self-service, as mentioned above, is its star motive, which is always enhanced when you have the right tool. Much more when attractive graphic functionalities are available, which offer the possibility of obtaining results extremely quickly.
  • IT is no longer necessary. Traditionally, this department was the one that enjoyed power and veto in decisions that had to do with the acquisition by the company of a new computer platform. However, this has changed. In effect, the weight of the company has increased when it comes time to make decisions regarding the purchase of a software solution, even reaching cases in which, as for example when it comes to freelancers, no longer needs approval from TI.

In short, with Data Discovery, Business Intelligence reaches a new dimension, since, although data discovery instruments have existed for a while, the flexibility of its new approach, oriented towards data analysis, makes it possible for to BI reaches the masses more effectively.

“The balance between agility and completeness in business analysis is disappearing as new technologies bring the speed of data discovery to a complete set of BI tools that ordinary business users can easily take advantage of in their everyday lives.” Southard Jones, The battle of Business Intelligence: Data Discovery vs Traditional BI.

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Advantages And Disadvantages

Many businesses have incorporated data discovery into their routine, due to the many pros it offers to businesses:

  • Total flexibility in creating dashboards and reports.
  • Quick to examine data and reach conclusions. In this way, the business user is able to use these tools and reach their own conclusions, without having to have too extensive training or qualification, and with a very short learning time.
  • The user does not depend on the IT, as was the case long ago. “The balance between agility and completeness in business analysis is disappearing as new technologies bring the speed of data discovery to a complete set of BI tools that ordinary business users can easily take advantage of in their daily lives.” Southard Jones, The battle of Business Intelligence: Data Discovery vs Traditional BI. The pros and cons of Data Discovery 7
  • Friendly environment. The main characteristics of the interface offered by these tools is its ease of use and its intuitive use, making available to the user a multitude of graphics that can be handled without the obligation to program anything.
  • Achieve a broader view of the origin of the data, thus improving its quality and consistency.
  • Find key supplemental metadata about core data assets and identify trends.
  • Be a support for Business Intelligence and relieve IT of workload, allowing it to optimize its efforts, to focus on governance and data modeling.

Despite its benefits, this tool has not yet reached its maximum degree of excellence, and it continues to evolve. Companies from many different industries are looking to experience the potential benefits of data discovery. However, once they have been implemented, the drawbacks of Data Discovery begin to become apparent:

  • The setup time is usually quite long.
  • Its applications present limited options.
  • It is more difficult to use than you might expect.
  • Lack of uniqueness of the data, which, by not being verified, runs the risk of being unreliable or lacking in quality.
  • Encourage the creation of silos in the departments, as each one has its data warehouse, with the consequent risk that these do not coincide.
  • Absence of the data validation process, so there is no guarantee that the information displayed is valid.

However, four rules of thumb can be considered to overcome these data discovery limitations, which are a drag on data quality, depth of exploration, and reliability:

  • Implement fast-cycle iteration mechanisms, increasing the speed of obtaining knowledge with information, as well as value with data.
  • Clear search objective.
  • Don’t set limits.
  • Introduce governance, obtaining independence, flexibility, and speed in the construction of reliable and quality reports.

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