Finest Stating Tools Business Intelligence – A data governance tool is defined as a tool that assists in the process of creating and maintaining a structured set of policies, procedures, and protocols that control how an organization’s data is stored, used, and managed. This article introduces you to the basics of data management tools, their key features, and the ten best tools available on the market in 2021.
A data governance tool is a tool that assists in the process of creating and maintaining a structured set of policies, procedures, and protocols that control how an organization’s data is stored, used, and managed.
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In today’s environment, data is the core of any business. Well-maintained data can be used to make intelligent business and marketing decisions, setting an organization apart from the competition. A 2020 Capgemini Research Lab report shows that 39% of participating business leaders have used data-driven insights for sustained competitive advantage. These figures, despite being in the minority, saw a significant benefit of 22% higher profitability.
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The key to data-driven decision making is data integrity. Organizations, especially enterprises, are increasingly investing in cross-functional platforms. Infrastructure can be on-premise, cloud or hybrid. Information constantly flows between multiple applications and services. With such a large amount of data passing through the system, ensuring data integrity becomes a complex and ongoing task. This is why data management is critical for any organization. Data governance frameworks typically inform and document the what, who, why and how of data in an organization. When organizational data is structured and bound to predefined rules and policies, it increases consistency and accessibility throughout the process.
The goal of data governance is to create policies that define data ownership, roles, authorities, and policies by involving stakeholders from different levels and departments in the organization. The goal is to achieve a unified understanding of each data silo in the system by creating common data definitions and formats. After all, when done right, the goal of data governance is to enable consistent and confident decisions based on trusted data assets.
1. Defining the mission: As with any project, every data management activity begins with a mission statement. Why is the organization doing this? How much budget was allocated? What are the long-term goals of this project? Will the organization focus on marketing decisions? These are some of the questions that need to be addressed at this stage.
2. Defining success metrics: At what point do we declare a data management program successful? This is a metric that should be determined before building the framework.
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3. Identification of data assets: The various access points where data enters and leaves the system must be identified, along with detailed information about the data itself. For example, what are the various operations that require the storage and use of unique customer IDs? Where and how is sensitive information such as social security numbers stored? Every attribute of every piece of data in the system must be carefully studied and documented.
4. Creating data rules and definitions: This involves getting input from employees in various departments and also from subject matter experts. Data rules are defined based on the goals set in the previous phases. Data from each access point and asset is defined by common terms used throughout the system. At this stage, the following participate:
5. Assigning rights and data ownership: Once all the rules are in place, each data asset identified in the steps above is assigned to a specific owner. This person has control and ownership of this piece of data and all changes must go through them. They are also responsible for any unexplained data changes.
6. Designing Control Mechanisms: Control mechanisms are processes that define the necessary steps to be taken to create, maintain and delete various data assets. This ensures consistency throughout the system.
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7. Creating Roles: Roles are an important part of data management. Most data management activities involve the creation of the following teams and roles:
8. Create knowledge transfer and training programs: Once the required rules, roles and teams are in place, the various customer-facing teams, analyst groups and programmers should be trained on data management policies to ensure that there are consistent data standards and terminologies are used.
The scope of data management is extensive and includes several activities. It covers data quality, data modeling and architecture, data warehousing, data operations, business intelligence, data pipeline, documentation, data integration and data security.
It is easy to see that this complex operation requires robust data management tools to help streamline policies and automate its many functions. Data management tools are needed to eliminate human error, which can be high because of the large number of stakeholders involved.
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Choosing a data management tool requires some groundwork. There are some steps to take before even considering tools:
Once all these details are ironed out, it’s just a matter of matching the requirements with the product features. Some of the key features to look for in a data management tool include:
A data management tool must be able to discover and compare data across the organization. This involves scanning various data stores and cataloging them with categories and tags. Cataloging data makes it easier to discover data, which in turn strengthens business analytics. The catalog serves as a bird’s-eye view of each data entity, its profile, relationships, origins, and business glossary (with certain common terminology).
If your organization does not yet have MDM in place, a data management tool must be able to track and manage data management activities. This includes managing data quality metrics, master data rules and tasks, and data configuration.
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It encapsulates the data integration application and controls the data lifecycle and tracking of each data pipeline. A good data management tool also extracts and documents basic metadata, making cataloging easier. Metadata-driven ETL plays an important role in data integrity. Metadata also allows for greater searchability and connectivity.
A data governance framework fails without the concepts of data ownership and stewardship. Data owners are not only responsible for data entities, but also decide when BUs get access to their departments and whether it’s worth the security and integrity risk. Data controllers are responsible for maintaining data quality in terms of accuracy, completeness and consistency. A data management tool allows owners and administrators to do their jobs.
Self-service tools are essential for organizations whose data management goals are more business-oriented. These tools must provide an intuitive and uncluttered presentation of all data, including reporting and alerting capabilities. The self-service station enables consistent and clear decision-making. The tool should also allow for easy review and monitoring.
The driving force behind any robust data management tool is the graphical representation of data. This doesn’t just mean presenting a line of data, relationships between data and anomalies. Data management tools also need to visually represent policies, issues, and data channels.
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The data lineage tracks the origin of each data entity, the changes it has undergone, and its movement within the system. It helps in tracking and detecting any errors flagged by the system. Manual data line maintenance is cumbersome and error-prone. Thus, data management tools must provide the ability to automatically track lineage using code analysis, ETL, SQL log parsing, and machine learning.
The starting point of any data management plan is the creation of common data definitions and formats. Creating a common glossary of business terms helps maintain consistency.
For example, an active order may mean a product waiting to be shipped to a warehouse employee, while it may also include products in transit to the customer relations team. Intelligent management tools also provide the possibility of importing already existing business conditions. Then, all of the organization’s data assets, data quality, and data provenance must be synchronized with this data dictionary.
Data management tools are usually not available as stand-alone products and are often bundled with other tools. Before deciding on a data management tool, it’s a good idea to review existing capabilities and only pay for those features that complement the data management framework.
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This means that the tool your organization chooses must be flexible and adaptable. It must also address existing infrastructure, such as providing options for on-premise deployment or cloud deployment.
A data management tool should provide external and internal audits, especially if compliance is one of the key management objectives. Data-related regulatory laws such as HIPAA and GDPR require data to be securely stored and maintained.
Documentation of the data dictionary, data rules, and access controls goes a long way in demonstrating compliance. This can be done by creating intuitive reports or by enabling specialized roles for external auditors with limited data exploration capabilities.
Data management tools should include configuration and management of policy controls. When controls are set up, they are expected to automatically enforce policy management. This greatly assists data controllers in performing their work.
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