What is data intelligence and why is it important for companies?
What is data intelligence?
What is metadata and how does it relate to data intelligence?
Metadata is information about your data, or “data about your data” that describes key attributes, such as its content, structure, quality, source, ownership and relationships to other data. Metadata-driven insights help you answer the who, what, when, where, why and how questions about your data and provide transparency and visibility into your data assets. Metadata management provides a critical foundation for data intelligence.
Data intelligence vs. data governance
Data intelligence and data governance have a supportive and enabling relationship. Data governance is the overarching organizational framework that guides how you manage your data assets – both to reduce your enterprise’s risk surrounding your data, defensive data governance, and to raise awareness and usage of high-value data assets among data users, offensive data governance. Data intelligence provides the insight about your data and the technology to help you pragmatically implement and succeed in your data governance practices.
Data governance involves the exercise of authority, control and proactive and collaborative decision-making over the management, socialization and availability or accessibility of data assets. Data governance formalizes the management of data assets within an organization to maximize your data’s security, quality and value. Data governance requires people, process, policy and technology to work together to achieve its goals.
Data intelligence supports and enables data governance teams including data owners, data stewards and data users as they collaboratively work together to protect data, raise enterprise data literacy, and make high-value, trusted data easier to find and use throughout your organization.
Data intelligence vs. data analytics
Data analytics and data intelligence are also synergistically aligned. Data analytics is the application of data to generate insights and value, while data intelligence is the foundation of data to ensure its quality and meaning. Data intelligence supports and enables data analytics by providing metadata-driven insights and governance. Data intelligence and data analytics work together to form a critical hub for data preparation, modeling and governance.
Data analytics involves the use of various tools and techniques, such as data mining, machine learning, statistics, visualization and reporting, to process and analyze data from various sources and domains. Data analytics helps you to effectively use your data to drive business outcomes and value.
Data intelligence uses metadata to help you to discover, track and govern access to your data, and to place it within the appropriate business context. Data intelligence software provides the underlying foundation to develop and standardize on the business terminology, business rules, business policies and key metrics around your data that will ensure all within your organization are speaking the same language when discussing data and when implementing data analytics dashboards and reporting for data consumers. Data intelligence also ensures confidence when taking advantage of data analytics tools as it helps organizations to ensure the underlying data is and remains accurate and reliable.
Why is data intelligence important and why do companies need it?
Organizations typically consider implementing data intelligence solutions when they encounter various challenges that impact their ability to effectively and efficiently manage, analyze and maximize the value of their data assets.
In the 2023 State of Data Intelligence report from ESG,1 IT and business leaders cited data quality, data visibility and trust in data as the top three challenges in the strategic use of data.
Some common challenges that trigger the adoption of data intelligence software include:
- Data Silos: When data is scattered across different departments, systems or locations within an organization, it becomes challenging to manage, integrate, analyze and govern. Data intelligence software helps break down these silos by providing a centralized view of data assets and associated data movement throughout an organization.
- Data Quality Issues: Poor data quality, including inaccuracies, inconsistencies and incomplete data, can lead to unreliable analysis, decision-making and resulting costs for companies. Gartner estimates that poor data quality leads to annual company losses of $12.9 million on average. Data intelligence tools providing data quality scoring, data observability and data remediation capabilities can help identify and address data quality issues early and be critical in building data trust among data consumers.
- Data Governance Needs: Organizations realize the importance of data governance to ensure data is managed, protected and used appropriately. Limited data visibility and the desire for more automation and collaborative capabilities to support data governance workflows and team communication needs lead organizations to turn to data intelligence software for end-to-end data lineage, data stewardship tools and data literacy enablement.
- Compliance and Privacy Concerns: With data privacy regulations like GDPR, CCPA, HIPAA and others, organizations must ensure data is handled in compliance with these laws. Data intelligence aids in classifying and tracking data to ensure it is protected and used appropriately and provides companies with the visibility and reporting capabilities necessary for regulatory compliance audits.
- Data Security: Concerns about data breaches and unauthorized access to sensitive information prompt organizations to implement data intelligence solutions to help enforce security policies and guide data access.
- Complexity of Data Ecosystems: As organizations accumulate data from various sources, across cloud and on-premises systems, managing complex data ecosystems become a challenge. Data intelligence tools help organizations gain wide and deep visibility across their data estates to navigate this complexity.
- Lack of Data Awareness or Time to Data Discovery: Employees across different departments may not be aware of the high-value data available to them. Or the time required to find the data they need is greatly impacting their productivity or limiting their time for analysis and use. Data intelligence solutions make high-value governed data easier to find and use, supporting data literacy, raising data awareness and employee productivity.
- Data-Driven Decision-Making: Organizations aiming to become more data-driven recognize the need for tools and processes to identify high-value data and speed the ability to extract meaningful insights from their data. Data intelligence supports this business transformation by providing the necessary visibility, automation, and governed data accessibility to get to insights faster.
- Maximizing Data ROI: Organizations want to maximize data value and the return on investment (ROI) from their data. Data intelligence helps identify valuable data assets, monitor data pipelines for quality and reliability, and provide the business context, governance and accessibility to ensure data is available when needed and used optimally.
- Competitive Advantage: Data is a strategic asset that can provide a competitive advantage. Organizations turn to data intelligence to unlock the full potential of their data for innovative purposes, reduce time to market for new products and services, better meet the needs of customers, identify opportunities to monetize their data and more.
In essence, the challenges that trigger the adoption of data intelligence as a practice, and data intelligence software for enabling that practice, are often related to the need for better data management, quality, governance, security and utilization to support organizational goals and stay competitive in a data-driven world.
What are the key components of data intelligence software?
The key components and capabilities of data intelligence software include:
- Automated metadata harvesting: Automated metadata harvesting allows you to automatically collect and integrate metadata from various data sources into a centralized data catalog. This provides a complete inventory of your data and enables you to further enrich your metadata with data-associated business context and governance guidance. Without automation, metadata harvesting and ongoing metadata management are time-intensive, laborious tasks. Automated metadata harvesting also enables you to automate the reverse engineering of data models from your databases and applications for data intelligence purposes.
- Data cataloging: Data cataloging is the process of creating and maintaining a centralized inventory of data assets, such as databases, tables, columns, files, etc. Data cataloging helps users to discover, understand and access the data they need by providing metadata, such as data definitions, descriptions, classifications, tags, and other useful attributes.
- Data mapping. Data mapping helps IT teams plan for data migration, data integration and other data infrastructure modernization efforts. Data intelligence software facilitates the automation of data mapping eliminating the manual work and costly errors associated when using spreadsheets, reducing mapping time and costs by up to 80%.
- Data lineage and impact analysis. Data lineage provides the ability to see and understand the flow of data throughout an organization. From data sources to target applications and all data transformations in-between. This is extremely helpful in identifying data pipeline issues requiring resolution, planning for future infrastructure modernization efforts, understanding data relationships and more. Impact analysis gives organizations the ability to instantly see and understand the impacts of any proposed data landscape changes to both safeguard data pipeline delivery and more efficiently plan for change.
- Data quality: Data quality is the measure of how fit data is for its intended use. Data quality scoring and assessment includes aspects such as data accuracy, completeness, consistency, timeliness, validity and uniqueness. Data quality capabilities in data intelligence software enable data profiling, scoring, cleansing, monitoring and remediation to ensure data is fit for use and to build data trust among users.
- Data literacy: Data literacy is the ability to read, write and communicate with data. Data literacy software capabilities ensure data assets are easy to discover, understand and use in line with data governance policies by data users of all levels of technical expertise. Data literacy tools like consumer-like online search sites, social and collaboration capabilities to help users share data knowledge, and data literacy visual aids such as mind maps to explain data asset relationships, help users to find, interpret, analyze and use data effectively and confidently.
- Data marketplace: A data marketplace provides one central location for data users across an organization to find, compare and gain access to datasets, AI models and other data assets available for their use. Data marketplaces can provide a governed way to distribute high-value enterprise data in a self-service manner. Data intelligence within a data marketplace can include automated data value scoring and social ratings to help data users zero-in on the best-fitting asset for their use more quickly. A data marketplace helps enterprises to democratize data and foster a data-driven culture.
The benefits of data intelligence
The goal of data-driven organizations is to empower everyone in the organization with reliable, insightful data to make faster, better decisions that move the business forward. Data intelligence helps them do so, and in the process delivers these top organizational benefits:
- Improvements to employee productivity: Data intelligence improves the productivity of data analysts, stewards, architects, engineers and others working with data by reducing the amount of time it takes to discover, assess, understand, manage, score and govern data, leaving more time to maximize its actual use.
- Revenue growth through better decision-making support: Data intelligence raises the visibility of high-value, trusted data that is available and provides the business context needed around the data to ensure it is understood and actionable.
- Cost-savings through better data quality: With the ability to spot data quality issues earlier, along with proactive and reactive data remediation tools, organizations use data intelligence to improve data quality, reducing the sizeable organizational costs attributed to poor data.
- Reduced financial and legal risk through stronger data governance: Data intelligence helps organizations answer six fundamental questions about their data: who, what, when, where, why and how. Understanding the answers to these questions combined with the governance abilities leveraged within data intelligence software help organizations pragmatically ensure data security, quality and compliance and that they are audit-ready when needed.
- Fuels a data-driven culture: Data intelligence enables data literacy and governed data accessibility among users, and connects employees across the organization with a common language and focus around data, leading to better decisions and outcomes.
Data intelligence use cases
While data intelligence can help any organization enhance their efforts to be data-driven, here are a few examples of how organizations in different industries have leveraged data intelligence:
- An energy company identified more than 2 million Euros in business impact after 24 months: An energy provider wanted to use its data to improve its services, operations and compliance. With data intelligence, they created a unified, business-oriented data catalog, enhanced communication and created alignment between their IT and business teams, and empowered data professionals to access and utilize the best data for their needs. As a result, the company was able to reduce their external data management costs by 30% and the time spent on data discovery by 50% as better data availability and quality increased productivity on all data-driven activities across their enterprise.
- A healthcare organization standardized and centralized its data: A medical company dealt with data from 17 different health plans. It had no centralized data dictionary of its data. Data intelligence helped it to standardize and centralize its data. This made its reporting more consistent and efficient. It also improved its data governance, which ensured that everyone in the organization understood the same meaning of the data terms.
- An insurance company automated data lineage and fast-tracked impact analysis: An insurance company with over 10,000 employees was struggling to map data transformations from multiple sources into their data warehouse with tools like Microsoft Excel. Additionally, when a source change occurred upstream from their data warehouse, assessing the impacts downstream were time-consuming and difficult. With data intelligence, they were able to accelerate their impact analysis from a few hours to less than one minute and help their decision-makers to immediately know the impact of any potential changes.
- A retail company cut their analysis time by 30%: A retailer with over 10,000 employees needed to empower their data users with self-service capabilities, while also ensuring proper levels of data governance, consistency and security. With data intelligence, they enabled self-service data analysis for the business teams, reducing the dependency on IT teams and improving the speed and transparency of data. The company estimated that it saved about 30% of time on data analysis and became more data-driven in its decision-making
- Another insurance company saved hundreds of thousands of dollars in custom code development: By using data intelligence, the largest provider of personal and commercial insurance in Canada was able to improve its data mapping and integration process, and to provide end-to-end data lineage. Data intelligence helped the company to standardize the pre-ETL data mapping process and better manage data integration through the change and release process. Data intelligence also helped the company to provide easy and fast web-based access to data mappings and valuable information like impact analysis and lineage. As a result, the company started realizing ROI within 12 months.
- A non-profit started realizing ROI within a year: By using data intelligence, a non-profit organization was able to improve its data mapping and integration process, and to manage its metadata and data dictionaries in a central repository. Data intelligence helped the non-profit to standardize the pre-ETL data mapping process and to make data integration more efficient and cost-effective. Data intelligence also helped the non-profit to manage the business definitions and data dictionary for legacy systems contributing data to the enterprise data warehouse. As a result, the non-profit realized time savings across all IT development and cross-functional testing teams and achieved ROI within a year.
- A pharmaceutical giant reduced data mapping costs by 60%: A global pharmaceutical needed to improve its data integration process and lower its development costs. The data intelligence solutions gave it a central place to store and manage all its data mappings. This made the data integration process quicker and more cooperative. The company also benefited from data intelligence’s custom code automation templates, which reduced a lot of money and time in code generation. The company was able to finish its data warehouse projects more productively and successfully with data intelligence solutions.
How can you advance your organization’s data maturity with data intelligence?
The data within your company presents opportunity. The opportunity to increase income, create new goods and services, enhance customer service, outperform rivals and elevate the overall performance of your enterprise. But, to maximize the benefits derived from your data, it is imperative for you to enhance and develop data-related capabilities, data literacy and a data culture inside your organization
By applying a pragmatic data maturity model to your investments in data intelligence, you can enhance your ability to maximize the value of your data and guarantee a trajectory that yields significant return on investment for your business at every phase of your progression.
Below is a short summary of the erwin by Quest 7 step data maturity model and how it can guide you to maximize the value from your data by leveraging the capabilities of data intelligence and data modeling software.
1 Model
Data modeling drives data maturity and lays a solid foundation for data intelligence initiatives by allowing you to leverage standards, best practices and your institutional knowledge to design your “to be” state. Whether you are planning a modernization effort, a migration project or some other major IT effort, the physical and logical data model is the holy grail for your planned environment.
2 Catalog
The second step towards data maturity is cataloging the data assets across your organization, which is storing all the metadata about your entire physical inventory of data within one central metadata repository. Your data catalog will serve as your launch pad for finding, understanding, governing and actively using the data that is across your organization.
3 Curate
After inventorying and cataloging your data, the next step is to curate it. Curating your data means enriching your data with business and organizational context. The value of your data really comes alive once it is curated and contextualized as it becomes tied back to business value.
4 Govern
A strong data catalog rounded out with business context puts organizations into the best position to more fully tackle data governance. Taking advantage of strong data stewardship tools and employing customized data governance workflows to build and maintain your data intelligence and governance effort ensures the repeatable processes and transparency needed to successfully implement data governance.
5 Observe
Now with the fundamentals in place, your organization is in the perfect position to observe and act to improve. By proactively monitoring your key data pipelines, and pruning and more tightly managing data, you can achieve better operational efficiency. You can also be smarter when making data infrastructure changes and improvements and ensure you are compliance audit ready. Lastly, automating and integrating data quality can help you strengthen the flow and quality of data.
6 Score
With better visibility and automated assessment of your data, you are in a strong position to score data for potential monetization efforts and recommended data usage. Data value scoring ensures high-value data is easily recognized. Automated data value scoring helps organizations to pragmatically produce and keep current a data value score that is well-supported.
7 Shop
High-value, governed data reaches its optimum organizational benefit when it is easily discoverable, understandable and accessible by all across your organization that are in need of it. Providing data users with consumer-friendly capabilities to shop, share and compare available, governed enterprise data is the accelerator to deriving the maximum value of your organizational data.
See our eBook for a more detailed view of the data maturity model.
Where can I get help for data intelligence?
erwin® Data Intelligence by Quest® combines data catalog, data quality, data literacy and data marketplace capabilities to make high-value, trusted data assets easier to find, understand, share and use across your organization. From IT, to data governance teams, to business stakeholders, all have the data intelligence to manage, maximize and protect your enterprise’s most valuable asset - your data.