What Is Data Intelligence?
A framework for conveying dependable, reliable information is known as information insights. It comprises information insights, frequently known as metadata. “Information insights make a difference firms reply six fundamental questions around information,” agreeing to IDC. These are the request:
- What data is being used by whom?
- What data is there, and where did it come from (provenance and lineage)?
- When is data accessible, and when was the last time it was updated?
- Why do we have data in the first place? What is the purpose of keeping (or discarding) data?
- What is the current state of data use, or what should be the state of data use?
- Relationships – what are the relationships that exist between data and data consumers?
Human comprehension is aided by data intelligence. DI presents a picture of why people might use a given data asset by answering essential questions like who, what, where, and when it was created, teaching on its dependability and relative value. Insights regarding how an asset has been utilized in the past can help determine how it should be used effectively in the future.
In the fable The Blind Men and the Elephant, six blind men arrive at six different, yet correct, conclusions about the same creature. Data intelligence, likewise, provides a ‘big picture’ view of a system that is too complex for any single human to comprehend. By merging multiple perspectives into a shared ‘bird’s-eye view,’ it shows the elephant system as a whole.
Data intelligence, on the other hand, is more than a system for evaluating a particular asset. It raises much bigger questions about an organization’s relationship with data, such as “Why do we have data?” and “How do we use data?” What is the point of retaining data in the first place? These questions can help enhance operational efficiencies and inspire a variety of data intelligence use cases, such as data governance and self-service analytics.
Origins, Evolution, and Use Cases of Data Intelligence
Data intelligence first appeared to aid search and discovery, mostly to increase analyst productivity. Enterprise analysts have been struggling for years to find the data they required to create reports. The problem was compounded by the rapid expansion of data collection and volume. The first DI use cases relied on metadata to display assets that were most beneficial to others, such as popularity rankings reflecting the most used data.
Finding data, however, is only the first step. Before enquiries, analysts have questions. They need to know: Who has previously used this data? What did they do with it? If I’m allowed to use it at all, how should I utilize it?
As a result, data intelligence has grown to answer these challenges, and it now supports a wide range of applications. The following are some examples of Data Intelligence application cases:
- Data management
- Transformation to the Cloud
- Data Migration to the Cloud
- Risk, Privacy, and Compliance
- Analytics for Digital Transformation
Let’s take a closer look at DI’s function in the data governance use case.
Data Governance and Data Intelligence:
To return to the analyst example, the first incarnations of DI assisted analysts in locating important data. And that was the end of the help. Analysts, on the other hand, needed to know how and whether they should use the data. “I want to make sure the data underpinnings in that report are robust if I’m preparing a report for an executive audience to assist key decision making,” they stated.
Data governance has emerged as a viable solution to this problem. It answers important questions from a variety of perspectives, allowing all data users to know: Is this the proper dataset to utilize…
…from a compliance standpoint?
…from the standpoint of quality?
…from a business standpoint?
Data governance formalizes responsibility and authority around data, ensuring that roles are clearly defined and that everyone knows who has access to what information. People are also directed to the best data and its most suited use. Data governance promotes confidence and openness in this way.
What role does data intelligence play in governance? Metadata is crucial once again. The following are some examples of governance features that make use of data intelligence:
- To unify teams on essential terminology, create a corporate glossary with automatic data classification.
- Reports on data lineage and effect analysis to indicate transformation over time
- PII Deprecations, which warns users if a dataset has been designated deprecated, are examples of trustflags that indicate a dataset contains sensitive information.
- A stewardship dashboard to track the assets that are most ripe for curation and the progress of the curation.
An example of a stewardship dashboard for governance progress tracking.
A Behavioral Analysis Engine will crawl all data and metadata, discover patterns, and apply remedies as part of an active data governance framework.
Data Strategy and Data Governance:
Data governance has traditionally been used as a defensive tool to ensure compliance and pass audits. Compliance is critical, particularly in regulated industries, but the command-and-control governance strategy that often accompanied this defensive stance built walls between people and data, as well as jeopardized workplace culture.
Enlightened governance leaders are now aware that governance can support both an offensive and defensive data strategy. To put it another way, leaders are prioritizing data democratization to guarantee that everyone has access to the information they require. People are alerted to sensitive data where it dwells thanks to data catalogs that incorporate compliance at the point of consumption.
Metadata and Data Intelligence:
A data catalog’s high-level metadata categories include:
- Behavioral: Keeps track of who uses data and how they use it.
- Technical: Displays the definitions of a schema or table.
- Business: Policies governing the proper handling of various types of data.
- When a new version of a dataset is developed, the provenance shows the relationship between two versions of data objects (also known as lineage.)
Behavioral metadata is extremely significant since it represents an organization’s human wisdom around data.
It demonstrates how people utilize data to gain insights — and how they may learn from one another.
The animating spirit of an organization’s distinctive data intelligence is formed by how employees use data across the organization.
You’ll be exposed to more types of metadata as you use a data catalog more extensively:
- Consumers, curators, stewards, and subject matter experts are all examples of people that deal with data.
- Metadata for search: Supports tagging and keywords to aid in the discovery of relevant information.
- Metadata processing: Describes the transformations and derivations that are used to manage data throughout
- its lifecycle.
- Supplier metadata is important for data obtained from external sources since it contains information about the sources as well as subscription or licensing restrictions.
Finally, behavioral metadata is used by data catalogs to gain insights about how humans interact with data. This category combines multiple metadata types to help with proper usage in a variety of scenarios.
What Is Data Intelligence Software?
Data intelligence software promotes a data-driven decision-making culture. Data culture is supported by DI software in the same way that customer relationship management (CRM) software is supported by CRM software.
Data intelligence shows itself in a variety of methods, technologies, and use cases as an organizational discipline. It is maintained and optimized using data intelligence tools. Typical features of this software include:
- Data dictionaries and business glossaries (to store definitions)
- Tools for profiling
- Dashboards for stewardship
- Features of data lineage
- Natural language processing, for example, is a data cataloging function.
Enterprises are overwhelmed with both data and metadata as data gathering and volume increases. As a result, data intelligence software has increasingly used artificial intelligence and machine learning (AI and ML) to automate curation processes, ensuring that reliable data is delivered to those who use it.