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.
The foundation of enterprise intelligence is data intelligence. Intelligence about data supports and informs every data-driven learning, analytics, decision, action and outcomeStewart Bond Vice President, Data Intelligence and Integration Software, IDC
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 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.
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:
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.
The key components and capabilities of data intelligence software include:
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:
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:
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.
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.
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.
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.
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.
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.
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.
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.
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.