The process of digital transformation has brought us many new developments, and more businesses are becoming data-driven as they are not only able to monetize that data directly, but can also utilize it for future decision-making.
With AI, machine learning, big data and business intelligence, the amount of data available and its handling has become a center-point for new developments and technologies.
We at SecurityTrails are ourselves data-driven, striving to be the go-to hub for professionals or anyone interested in big data services. The sheer magnitude of data we wrangle can appear overwhelming, so making our product comprehensible and accessible is crucial. We want you to leverage it in the best way possible.
We work diligently to become the biggest treasure-trove of cyber intelligence data around, and while we’ve written about the different types of digital intelligence, it’s important to tackle the subject of data intelligence itself.
What is data intelligence?
Artificial intelligence and machine learning are widely used not just as terms, but as technologies that are sneaking into all industries and business fields. Well, “sneaking” would be an understatement; with all the attention they’re getting, AI and machine learning are making their big entrance, right through the door.
Many developments have resulted from the use of AI and machine learning, and many more are to come. Data intelligence is one of those developments.
Data intelligence is the interaction and analysis of diverse configurations of data in a way that is meaningful, for transforming the data into forms that will provide insight for a company’s or organization’s decision-making for future undertakings.
With big data and the volume of information needed to be consumed for different types of investigation and analysis, data intelligence is an extension to the traditional way in which we see and digest data.
Combining AI and machine learning provides organizations with the ability to analyze massive datasets much more reliably and noticeably faster. Besides that, the data is arranged by established models for storing datasets of that size. These datasets provide insights that help organisations discover and develop new opportunities within the marketplace.
Some of the industries with the biggest need for big data and data intelligence are
- Law enforcement
The most reliable analysis of data itself is done with focus on five components:
- Descriptive data
- Prescriptive data
- Decisive data
- Predictive data
Big data and its introduction into all areas of business and key industries needs a primary way of conducting, and data intelligence provides exactly that.
Using data intelligence, organizations have the ability to adapt more quickly to industry trends. By following the analytics that data intelligence provides, they’re provided with insights into patterns, changes and trends, so organizations are able to develop ideas and directions that are information-based.
With big data and the usage of AI, data intelligence delivers structure to management and arrangement of that data. Additionally, data intelligence is the main actor in data transformation, as it transforms huge chunks of data into an empirical and ever-growing knowledge foundation.
AI and other machines designed to digest large quantities of data do not pick and choose the data. Data intelligence, however, sorts out the good data from the bad, relevant from irrelevant, and with that quality of information transforms the data and knowledge into usable information that provides organizations a more valuable overview of their field of analysis.
Data intelligence = data enrichment.
SecurityTrails has a set goal of providing you with the best possible database of cyber intelligence data. The magnitude of data can seem overwhelming, but we worked on making it comprehensive and relevant to your needs, giving you the greatest possible advantage by using it.
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Types of data intelligence
There are certain types of data that organizations collect for intelligence purposes, and they provide the different insights needed for different business metrics:
Companies and organizations produce massive amounts of data, and they produce it constantly, at all times. Big data, even if the name implies differently, is not only about the quantity of data.
The point of big data is not in having a large volume of information, it’s in storing the data that will later be used for different analysis, the way we do with our passive DNS technology. That is what actually makes big data, the term and practice itself, as popular as it is, the new buzz word you hear everywhere.
Structural architecture is a predominant feature that organizations look for when collecting and storing data, since unstructured data is not relevant to decision-making processes.
Almost all of the data organizations own is unstructured, making it hard to perform accurate analysis, derive valuable insights and turn them into future actions.
Large amounts of data with a constant and rapid flow through data sources needs that structural architecture. Analysis of data for intelligence purposes is performed much more efficiently and accurately when the data is stored in a meaningful way.
Once we obtain large amounts of data, and store that big data, we need to analyze it. Before we are able to draw relevant and insightful information, the data needs to be differentiated and grouped into meaningful “categories”—structures that allow further analysis.
Data mining is the process in which large sets of data are analyzed with the purpose of finding patterns which are then categorized to help with future data analysis.
Assembling data into categories and then storing it is performed with the object of efficient analysis via intelligent methods. Data mining allows organizations to predict and follow future trends in the industry.
The benefits of data mining are mostly found in situations where there’s a need for predicting consumer behaviour, and predictive analysis altogether, as the patterns it uncovers allow organizations to make predictions that benefit their business.
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Once we’ve categorized the data, we need to acquire beneficial conclusions from them.
Here is where event processing comes into play, as it tracks and processes information about events that is derived from collected data.
The analysis of such events is performed with the goal of reaching a conclusion that can help predict, by patterns, the important events that will need attention.
Important events can be positive, such as good new business opportunities, and negative, such as possible security threats. For this reason, a system that performs event processing must be able to respond to these events at any and all times, because even if events are marked as important and noteworthy, their appearance is unpredictable.
Using online analytics, businesses can capture and measure web data. The process itself is not only used for measuring online traffic and deriving success statistics from it, it allows businesses to leverage online analytics for market research purposes and to track effectiveness of online campaigns, online presence, brand awareness, site performance and more.
Online analytics focuses on improving customer experience by using the organization’s website data as another source of data intelligence. The data is analyzed by discovering patterns in customer behaviour and web traffic, allowing organizations to attract more customers or create new retention programs.
Some of the most frequently-used tools for online analytics are:
Infosec data Intelligence
Infosec professionals that deal with threat intelligence should take notes from data intelligence as well. Data intelligence is more enterprise-oriented, but as more and more organizations are introducing infosec teams to assist on their security infrastructures, security teams are becoming aware that every piece of the puzzle, every piece of data obtained, can be used in both identifying future threat actors and helping in product development or marketing.
Including infosec professionals in the data intelligence process will allow them greater access to data, in contrast to their sometimes limited access during standard security audits.
Even the act of reconnaissance in penetration testing and tracking security incidents adheres to the model of data intelligence where collecting relevant data provides insight into possible means of future attacks.
By accessing big data and methods of data intelligence, infosec professionals will have more data to go with, and a more accurate representation of future threats or exploits will follow.
In conclusion, data intelligence is not only a buzz word accompanying AI, machine learning and big data. Data intelligence is growing as a must-have tool for organizations and businesses no matter the size. Its value in handling data in an intelligent way and its ability to digest large amounts of data and draw precise conclusions will help businesses gain insight into creative, beneficial strategies for the future.
Some industries are already benefiting from data intelligence, but with time, more and more fields will adopt these methods—as our world as a whole gets more and more populated with data.
SecurityTrails is a modern tool that provides intel gathering and all relevant data with just a few clicks. We want to empower experts by deepening their understanding of intelligence data. Kick off your cyber investigation and schedule a demo with us so you can see everything our SurfaceBrowser is capable off or sign up for API and experience what else our product offers.
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