In business environments, just as in life, the quality of our decisions largely depends on the documentation and reasoning we have to choose one option over another. Data generation is constant, and the volumes of information we handle are so vast that they sometimes become obsolete before we can process them. Data intelligence is the discipline dedicated to optimizing the process of collecting and managing data so you can make the most of it. Let us explain.
The emergence of IoT, the interconnection of all our devices, the rise of wearables, and digital transformation have led to an exponential growth in the amount of data being generated.
According to an infographic published by IBM, every second, 2.9 million emails are sent; every day, Google processes 24 petabytes of data (one petabyte equals 10 GB to the power of six); and mobile internet users send and receive 1.3 exabytes of data (one exabyte equals 1,024 petabytes).
Much of this information is highly valuable, but in many cases, it becomes challenging to manage and, at times, goes unused. The lack of prioritization and proper filtering criteria can lead to resources being wasted on processing data that adds less value to a company’s decision-making process, while other more valuable data remains stored and becomes obsolete.
Additionally, it’s important to note that to maximize the potential of data, it’s far better to work with information that connects and cross-references data. For example, knowing how many users consume your product or service is not the same as understanding their psychosocial profile. The quality of the data is not comparable, and therefore, neither is the quality of the decision made based on it. This is where the importance of data intelligence lies.
What Does Data Intelligence Entail?
Data Intelligence is essentially the transformation of structured and organized data into meaningful information that can be leveraged by businesses to connect with their customers more effectively.
This intelligence involves interpreting data related to a company’s operations and implementing technological policies that ensure a more refined and efficient decision-making process.
This discipline comprises several techniques that provide a 360-degree view of a business:
- Descriptive: Reviewing and examining data to understand and analyze business performance.
- Prescriptive: Developing and analyzing alternative insights that can be applied as strategies.
- Diagnostic: Identifying possible causes of specific incidents.
- Predictive: Analyzing historical data to predict future occurrences.
- Decisive: Measuring the relevance of data and recommending future actions within a context of multiple possibilities.
Data Intelligence benefits a business in several ways, including the following:
- It provides a clearer picture of the company through visual representation. This is especially useful for larger organizations composed of multiple interconnected departments and sections, viewed as a unified entity.
- It creates new opportunities thanks to technological advancements that allow real-time data access. This enables proactive decision-making and immediate action.
- It helps businesses identify where they are achieving the highest return on investment and where they are wasting resources, allowing them to take necessary corrective measures.
Data Intelligence is Not Business Intelligence
It’s important to highlight the differences between Data Intelligence (DI) and Business Intelligence (BI), two closely related disciplines that complement each other but involve different processes. BI is used by companies to analyze data and make business decisions, whereas DI focuses more on examining internal data to identify trends that can lead to better decisions in the future.
DI could be considered a process of asking the questions that BI answers. Ultimately, both practices revolve around organizing data to improve business practices.
These questions (the 5 Ws of data, as they are written in English) can be summarized as follows:
- Who is using the data? Who created it?
- What does it represent? What is it being used for?
- When was it created? When is it being used? When will it become obsolete?
- Where is it located within the organization? Where is it being consumed?
- Why does it exist? Why is it still being produced?
- How was it created or captured? How is it being used?
Data Intelligence adds another dimension to these questions: the relational aspect—what inherent relationships exist within the data and between the people generating and consuming it?
In summary, Data Intelligence aims to bring a new perspective and philosophy to how businesses operate, using all the data a company generates to ask the necessary questions to improve policies and performance.
The generation of data is not a passing trend but an inevitable activity in businesses, following a growing trajectory. A study by the OBS Business School estimates that by 2020 there will be more than 30 billion devices connected to the Internet, generating a corresponding volume of data. Data Intelligence is a necessary tool to manage this volume and turn it into something beneficial.
