An Ultimate Guide to Data Intelligence


Data Intelligence consists of the various analytical tools and methods utilized by companies to obtain a better understanding of information which is collected to improve services and investments.

It mainly focuses on both analysis and interaction with the information collected in such a way that it enables the company to promote better decision-making in the future.

Data collection by businesses and organizations for intelligence purposes involves a variety of sources such as business performance metrics, data mining of both customers and users, along with various other descriptive sources.

The Difference between Data, Information, and Intelligence

1.   Data

Data are the raw facts that are individual as well as unarguable consisting of quantities, characters, or symbols in the operations which are performed by a computer.

This data can either be stored and/or transmitted in the form of electrical signals and it can be recorded on either magnetic, optical, or mechanical recording media.

2.   Information

Information can be defined as the combination of data info a form which can provide an answer. It is also seen as the resolution of uncertainty while the concept of information can have various meanings in different contexts.

Information is associated with data due to data representing values which are attributed to parameters and subsequently, information is data in context with meaning attached to it.

3.   Intelligence

Intelligence is defined as the combination of information into a form that depicts a story and informs decisions. Intelligence can be placed in various contexts such as the capacity for logic, understanding, self-awareness, learning, and numerous others.

Data Intelligence versus Business Intelligence

It is imperative to note that there is a distinct difference between Data Intelligence and Business Intelligence, and that the two should not be confused.

Business Intelligence focuses on organizing data and subsequently presenting it in a way that will make it easier to understand and to be able to derive business intelligence insights whereas Data Intelligence is concerned with the analysing of the information itself.

The Components of Data Intelligence

To analyse information, Data Intelligence experts focus on five major components including:

  • Descriptive
  • Prescriptive
  • Diagnostic
  • Decisive, and
  • Predictive data.

These components, or disciplines focus not only on understanding data, but also on uncovering any alternative explanations, uses in resolving issues and the identification of future trends which subsequently improves decision-making.

Data Intelligence incorporates both artificial intelligence with machine learning tools as it allows organizations and businesses to analyse vast amounts of data faster and reliable instead of having it done manually.

The generation of Data Intelligence

There are numerous methods employed to generate resources mainly through analytics tools and data mining which allows for the extraction of data and information from sources such as:

  • Social Media
  • Company Websites
  • E-commerce platforms
  • Company databases, and
  • Online advertising initiatives.

The data derived from these sources are processed through complex algorithms and/or artificial intelligence into a way which is more meaningful and can allow for the data to form into more insights.

These insights can then be used to refine internal processes for efficiency, improvement in offerings, and more. Data Intelligence also allows companies and businesses to identify any gaps in the market or to anticipate certain business opportunities that may arise in future.

When considering big data, which is a field which treats ways through which to process data sets which are ordinarily too large to be done traditionally, Data Intelligence can be seen as an extension to those traditional ways through which data is seen and digested.

When combining AI and the deep learning capabilities of machines, organizations are provided with the ability to analyse substantially large datasets in a way that is more reliable and notably faster.

In addition, the data is also arranged by models which have been established for the purpose of storing such large datasets. Some industries which have the largest need for big data as well as data intelligence includes:

  • Cybersecurity
  • Law enforcement
  • Insurance
  • Finance
  • Internet of Things, or IoT
  • Health, and numerous others.

Data Intelligence provides organizations with the ability to adapt quickly to industry trends that may show patterns of change in a truly short, or over reasonably longer spans of time.

In making use of the analytics provided by data intelligence, organizations and businesses can view patterns, changes and trends and develop ideas and directions which are information-based.

By using both big data combined with the use of AI, Data Intelligence can deliver structure to management and arrange that specific data.

Data Intelligence is essentially main actor in the transformation of data as it can transform large chunks of data into both an empirical and an ever-growing foundation of knowledge.

While AI and other machines were designed with the purpose of digesting large quantities of data and therefore cannot choose the data, Data Intelligence refines such data.

It can sort it and distinguish between relevant and irrelevant to improve the quality of information and transform it into usable information.

Types of Data Intelligence

Certain organizations will collect certain types of data for intelligence purposes and each type provides different insights which are needed for various business metrics.

Big Data

As mentioned earlier, there are numerous companies and organizations which produce substantially large amounts of data continuously and consistently and although the name may be confusing, Big Data is not concerned with quantities.

The point in using Big Data is not to have a large volume of information, but to store the data for later use where it can be used in different analysis. Big Data is in the term and in the practice.

Most organizations focus on structural architecture when collecting and storing data since unstructured data is considered as irrelevant where decision-making in the organization is concerned.

Most data owned by organizations and businesses is unstructured and it makes it severely difficult to perform accurate analysis on this data and subsequently deriving valuable insights and turning them into future actions becomes difficult as well.

When there is a constant and rapid flow of large amounts of data through sources, there must be structural architecture as it allows for more efficient and accurate analysis of data when the data needed for intelligence purposes is stored in a meaningful way.

Data Mining

As soon as large amounts of data has been obtained and big data has been stored, it needs to be analysed but before relevant and meaningful information can be drawn from such data, it needs to be differentiated and grouped into meaningful categories.

Such categories allow for further analysing, a process referred to as Data Mining. Data Mining involves large sets of data which are analysed with the purpose of identifying patterns which can be categorized accordingly to help with future data analysis.

The object of assembling data into categories and subsequently sorting it is for an efficient analysis which is done through intelligent methods. By using Data Mining, organizations can predict and follow trends in the industry that show in the future.

The benefits associated with Data Mining are often found present in situations where a need arises for the prediction of consumer behaviour as well as predictive analysis which patterns are uncovered which allow organizations to make business beneficial predictions.

Event Processing

Once the data has been categorized, beneficial conclusions need to be acquired and this involves event processing. Event processing tracks and processes information pertaining to events which can be derived from the collected data.

The goal of event processing is to reach a conclusion which can help to predict, through patterns, which important events may require attention, whether they are positive or negative.

The system performing the event processing must always possess the ability to respond to any of the identified events at any and as their appearance may be unpredictable despite the event being marked as important.

Online Analytics

Organizations and businesses can make use of online analytics to capture and measure web data. Although this process is not only used for measuring online traffic or deriving success statistics from it, but it allows the organisation to leverage online analytics for the following:

  • Track the effectiveness of online campaigns
  • The business or organization’s online presence
  • Brand awareness
  • The site performance, and numerous others.

The focus is placed on improving customer experience by making use of the website of the business or the organization and the data thereof as another source of data intelligence.

The data derived is analysed when patterns are discovered and identified specifically where customer behaviour and web traffic is concerned. This allows organizations to identify gaps and improve so that more customers can be attracted to the company’s website.

Some of the tools which are most frequently used when online analytics are performed include, but is not limited to:

  • Google Analytics
  • Google AdSense
  • Hotjar
  • Alexa
  • Crazy Egg, and numerous others.

How Data Intelligence transforms various industries

In providing useful and insightful information, Data Intelligence has aided numerous markets and industries when considering that tools such as AI, Machine Learning, and Data Mining are used to collate and analyse large amounts of data more efficiently.

Industries and the way that they operate are transformed using Data Intelligence which enables organizations and businesses to understand the information that they derive through Data Intelligence.

This aids organizations and businesses to develop and implement more understandable business models and aggregate trends so that better decisions can be made more proactively rather than reactively.


In using Data Intelligence, the travel industry has identified improved quality and a range of products and services which can be offered to travellers along with optimizing travel pricing strategies for future reference.

Historical trends can easily be analysed especially during peak travel seasons and customer key Performance Indicators, or KPI’s, can be used to adjust their services, amenities, and their packages to better suit the needs of their customers.


A more valuable learning experience can be provided along with an improved environment for students by employing Data Intelligence in the Education Sector. By using the tools available, educational institutes can provide their teachers with a holistic view of the academic performance of their students.

This allows for the identification of gaps, or areas of improvement, and teachers can provide students with more support in these areas.


Data Intelligence has been used by numerous hospitals and healthcare institutions in services and operational processes.

Hospitals are making use of dashboards which provide summative information on hospital patient trends, the cost of treatment, waiting times, and more.

In addition to this, Data Intelligence can also be used to provide facilities with an encompassing view of both the hospital as well as important care data which can be used to improve both the quality and the level of service along with increase economic efficiency.


Data Intelligence is constantly being used by the Retail Industry in developing tools which allows businesses to forecast and plan accordingly where supply and demand trends are present along with their consumer Key Performance Indicators.

Regardless of being large or small retail businesses, both have made use of dashboards which monitor and illustrate transaction trends as well as the consumption rates of products.

Such dashboards provide a substantial amount of insight into the patterns associated with customer purchasing and the transaction value that businesses are leveraging and this in turn allows retail outlets to provide better products and services to their customers.

Internet of Things (IoT)

When viewed in an extremely broad sense, yet simply defined, the term IoT consists of everything which is connected to the internet. In more recent times, it is being used increasingly to define objects that can communicate amongst themselves.

IoT is therefore made up of devices, despite how simple they may be to the largest, which are all connected. The combination of these devices, which have automated systems, can gather information, analyse it, and create an action from that.

This may help someone with a task, or it may help them learn from a specific process. It is all connected through networks, among devices, and makes use of data which is shared between those devices through closed private internet connections.

IoT brings it all together and provides these devices with a means to communicate not only within proximity, but also across different network types which creates a more connected world.

There have been a substantial amount of arguments and questions raised whether certain devices which ordinarily would not need to be connected to the internet should be allowed to connect, but it comes down to the simple principle of data collections for a purpose.

These purposes may be useful to a buyer and it may subsequently impact a wider economy. When used in industrial applications, sensors on product lines can increase efficiency along with cutting down on waste.

IoT provides the opportunity for improved efficiency in how things are done, and it can save time and money in addition to saving emissions in the process.

It is a step in the direction of becoming more efficient, and to work towards a more sustainable way of living while ensuring that the needs and expectations of consumers can be met.

Final Thoughts – The benefits derived from Data Intelligence

Traditional business models and processes can be both tedious and detrimental when considering that we live in an evolving data-driven society. By introducing modern data sciences and data intelligence tools, businesses can enhance and fine-tune the products they offer and the processes that they employ.

Some of the benefits that businesses and organizations can expect when making use of data intelligence includes, but is not limited to:

  • An improvement in consumer profiling and segmentation.
  • A clearer understanding and subsequent enhancement of company investments.
  • The application of real-time data in marketing strategies.
  • The enhancement of logistical and operational planning.
  • Enhancing customer experience, and more.

Data Intelligence is not merely cast in the shadow of Artificial Intelligence, Machine Learning or Data Mining. It is ever evolving and growing, and it is a tool which is recommended for organizations and businesses despite their size.

It is invaluable in handling data in an intelligent way along with its ability to digest large amounts of data and subsequently draw precise conclusions which will help organizations and businesses to gain more insights into both creative and beneficial future strategies.

Big Data

Business Intelligence

Data Automation


Machine Learning