Choose A Metadata Storage Option
Traditionally, departments within a business have their own databases for metadata management. This has led to siloed data which limits the sharing and reusing of metadata assets. Choosing a storage option that centralizes metadata is key for:
- Collection across many platforms
- Visibility into data history
- Effective governance and stewardship
Centralized metadata ensures scalability and flexibility needed for analytics. It also helps different departments understand the value of data lineage.
Communication To Gain Adherence
No business process succeeds if the people in the organization dont believe in it. Thats why, before any data governance process is imposed, there needs to be communication to the entire organization about the motivation behind the process and the importance of the process to the future of the business.
Sharing that understanding of the purpose of the data governance process allows the people assigned to data governance projects a common understanding of what is to be accomplished by the project. It also makes sure that everyone else in the organizationthe people who create, use, share, and protect the dataunderstands that the responsibility for data quality is one of their key job functions.
You can emphasize the importance of adherence by identifying a set of metrics that will be tracked and making sure theyre reported not just up the management chain but also down to the employees whose actions generate those metrics.
Identify And Prioritize Existing Data
To implement a data governance strategy, a company needs to know what data it already has. As part of this process, the organization should start by:
- Inventorying data: Create a complete record of information resources with relevant metadata
- Classifying data: Analyze structured and unstructured data to organize it by relevant categories
- Curating data and knowledge: Organize and manage datasets with active metadata management and data catalogs
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Build A Team With The Right People
Your data governance process cant succeed if there arent people dedicated to its success. Start with a small group that spans the business to incorporate multiple viewpoints and address the special needs of each department. The business needs to drive the effort, as they are the ones who know what the data represents. Once the team has defined the overall data governance strategy, you can assign individuals the responsibility as data stewards to own the data quality tasks.
The initial team should be predominantly composed of business employees, not technology. Ultimately, though, technology will be needed to support the data governance efforts. Once the business team has defined the data governance priorities and goals, add representatives from technology who can identify tools that will support the data governance efforts.
Start With A Small Sample Size
It’s best not to kick off your data governance program with a complex or long-term project. You might make errors or lose motivation from the team. Rather, begin with a smaller, more manageable project, like analyzing data for one team. Assess the state of the data, specifically its collection, storage, and usage, then decide how much of your budget will be invested in the initiative.
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What Is The Current State Of Data Governance
For a multi-domain MDM program to succeed, data governance and data stewardship practices need to be closely orchestrated. This effort needs to start with an effective data governance process aligned with the MDM strategy and implementation plan. Chapter 6 provided strategies and examples for how to establish a consistent, transparent governance model across MDM domains and indicated that if a sufficient data governance process and structure does not already exist for the MDM program to use, the MDM program itself will need to become the driving force behind the implementation of the necessary data governance processes. Similarly, if an appropriate data steward model does not already exist, the MDM program and data governance will need to become the driving forces behind the implementation of the data steward model. Without well-aligned data governance and data steward models, the MDM program cannot succeed.
Prior to a company having a multi-domain MDM strategy and plan, there are likely to be existing instances of data governance and data steward practices that have resulted from locally or functionally oriented data management initiatives. For example, in one functional area, a data administrator or a support engineer may be acting in a data steward role to control a specific set of master data, such as validating sources to target data loads according to certain acceptance criteria and monitoring error log activity associated with any data mapping or integration issues.
People Process Technology And Information: The Four Components Of A Successful Business Strategy
A few weeks ago, a fellow Cooper and I were enjoying some good, Cooper-colloquy regarding the role that data and information play in organizational strategy. The following day, he forwarded me an invite to participate in a LinkedIn group discussion. The discussion started with one of the participants stating that Enterprise Architects need to have a better appreciation for the value and importance of information in regards to strategic transformation.
Implementing a successful business strategy in today’s competitive global environment requires a holistic understanding of the enterprise and how the four components of the Strategy Pyramid interact to achieve strategic value. Additionally, based on the indisputable impact that accurate, relevant, and timely information has on success, it is imperative that information management requirements be a priority throughout the strategic planning and design process. Learn more about our Strategy & Decision Support Practice.
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Data Governance Vs Data Management: Are These The Same
Data governance is not the same as data management.
For example, if we are to compare data with customers, front-line customer service agents implement a company’s customer service policies, while the customer service leadership team designs the strategies, policies, and processes. Implementation flows from having a clear strategy, processes, systems, policies, and procedures.
The same applies to data governance. Data management is the practical implementation of data governance principles, and is primarily seen as a function of an IT department. Data management encompasses the lifecycle of a company’s data assets.
Whereas, data governance is the creation of a set of standards, principles, processes and systems that oversee the management of data within an organization.
With an effective data governance model, organizations experience numerous benefits, including:
- Ensuring data consistency across an organization, and that data is trustworthy and doesn’t get misused
- Managing and navigating data compliance and privacy laws worldwide
- Breaking data out of silos so that it adds more value across an organization
- Supporting leadership and front-line teams with more effective access to data to help optimize operations and drive data-driven decision-making
Business leaders make better decisions when they have access to the right data, at the right time, in a format they can use. An effective data governance model founded on the principles of data governance makes this possible.
Identification And Classification Of Personal Data
One of the first steps of data governance is data classification, so the organization can quickly identify by labeling what qualifies as personal information. It enables organizations to understand how they use personal data and apply security measures by defining access rights based on data sensitivity levels.
As a data subject, you have the right to share or withhold your personal information when you access enterprise data. Remember the accept/decline/ manage permissions screen you see when accessing a website? Thats GDPR in action.
Information governance, based on data management, includes:
- Facilitating data discovery.
- Correcting inaccurate and incomplete assets.
- Purging redundant information and discontinuing data processing.
Similarly, GDPR gives data subjects the right to request rectification and update personal information and discontinue data processing. It includes the Right to be Forgotten a data subject can request that the organization erase their personal information.
Enterprises need the capabilities and the technology to respond to these requests.
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What Is A Data Governance Framework
A data governance framework is a collaborative model for managing enterprise data. The framework or system can set soft guidelines or firm boundaries around data creation and manipulation. Often companies assemble a data governance team to ensure proper use of data, data quality, and policy compliance. Executing a data governance framework impacts all parts of your data management process, including architecture analytics and data models. Proper execution makes it easier to make smarter decisions, faster. Once you have a solid understanding of data governance and the impact it can have on your organization, look for opportunities to use templates, models, and best practices that are available on the market. Data governance best practices can be found in software tools, frameworks, libraries, or consultants, and you can look at Tableau Blueprint to understand how Tableau can help you move towards successful implementation. While every organization is different, there are some basic best practices to help guide you when youre ready to move forward.
Adopt A Data Governance Office
Once your goals are set, you’ll need employees to achieve them. While you could assign one or two people, the most effective way to implement data governance is with a complete team.
Your team should include management, data stewards and liaisons, and any other company stakeholders involved in obtaining or securing data. These people will be considered your “data governance office” and will be in charge of making important managerial decisions.
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How To Develop A Data Governance Strategy
What does the business seek to do with data? Your data strategy should be clear and easy to communicate in simple language. If only a data scientist can understand the strategy, its unlikely that strategy will be successful, if everyone is to get onboard. Governance plays a key role in supporting that strategy at every step.
Where does your business sit? For companies in highly regulated industries, like financial services or healthcare, data strategies are most often compliance focused: defensive. They may set a data strategy focused on protecting private health data and passing compliance audits. In this case, governance ensures key processes are documented for future audits. Increasingly, even across these industries, the CDOs success is determined by the positive value delivered. CDOs are increasingly de-prioritizing defensive strategies as a result.
Conversely, the rise of personal data regulations is causing these industries to re-prioritize defense. These industries are having to increase their scope to cover their defensive needs, in addition to the value-add initiatives that support a competitive edge.
But before governance can support a strategy, data governance must be implemented. Lets take a look at the seven key steps for implementing data governance:
Metrics And More Metrics
As with any goal, if you cannot measure it, you cannot reach it. When making any change, you should measure the baseline before to justify the results after. Collect those measurements early, and then consistently track each step along the way. You want your metrics to show overall changes over time and serve as checkpoints to ensure the processes are practical and effective.
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Key Challenges To Effective Data Governance
The power of data in driving business growth is well known today. Effective data governance allows organizations to get maximum benefits from their most valuable asset. With high-quality data, businesses are able to gain insights for better business decisions and increase efficiency and productivity.
Moreover, data governance also protects the business from compliance and regulatory issues which may arise from poor and inconsistent data.Gartner predicts that through 2022, only 20% of organizations investing in information governance will succeed in scaling governance for digital business. Here are some common challenges organizations face while establishing data governance frameworks and policies:
Why Do I Need A Data Governance Framework
A data governance framework enables the business to define and document standards and norms, accountability, and ownership. In addition to setting out roles and responsibilities, this involves establishing key quality indicators , key data elements , key performance indicators , data risk and privacy metrics, policies and processes, a shared business vocabulary and semantics, and data quality rules.
A data governance framework includes discovery of data to create a unified view across the enterprise. This includes not only the data itself, but data relationships and lineage, technical and enterprise metadata, data profiling, data certification, data classification, data engineering, and collaboration.
A data governance framework supports the execution of data governance by defining the essential process components of a data governance program, including implementing process changes to improve and manage data quality, managing data issues, identifying data owners, building a data catalog, creating reference data and master data, protecting data privacy, enforcing and monitoring data policies, driving data literacy, and provisioning and delivering data.
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Focus On The Operating Model
An operating model is an asset model that outlines how an organization defines roles, responsibilities, business terms, data domains, etc. This, in turn, affects how workflows and processes function. It impacts how an organization operates around its data.
The operating model is the basis for any data governance program. The idea here is to establish an enterprise governance structure. Depending on the organization, the structure could be centralized , decentralized, or federated .
The Process Part Of The Golden Triangle
A process is a series of actions or steps that need to happen in order to achieve a particular goal. People are ineffective without processes in place to support their decisions.
Some steps to take when you implement processes:
- Make sure people know how they fit into the workflow.
- Identify the key steps.
- Have a system for review in place before beginning any new process.
- Consider how you will measure the success of a process.
What most companies dont do when they implement a new technology is look at processes that could go away, Morris says. Most of his clients run at about 30-40 percent waste, and eliminating unnecessary processes can reduce that percentage and therefore, increase efficiency. In order to create a new process, we need to find two processes that can go away.
Automation can increase consistency, but someone still needs to oversee the effort.
Morris says that arbitrary deadlines can negatively impact processes and cause people to panic about an impending deadline rather than concentrating on the work that needs to be accomplished. Often, managers pick deadlines randomly, without knowing how long processes will take. Morris says that when he asks managers why they picked certain deadlines, they often say they simply picked a date on the calendar, such as the end of a month, quarter, or budget term. That can be a mistake and put unnecessary stress on workers.
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What Is A Data Governance Program
A data governance program is a collection of practices and processes that form an approach to manage the data assets of an organization. But more importantly, data governance is how you add accountability and follow-through to ensure data management is being executed true to the plan defined in your data strategy.
If your data strategy is properly aligned with business strategy then data governance will be the mechanism to ensure data and analytics initiatives are aligned to driving business value.
You can have a more centralized approach to your data governance program, which is more traditional and has strict rules in place about who has access to what data and how they can use it. Or, you can have a more democratized approachthat still requires securitywhere all necessary business users are granted access to data and enabled to self-serve analytics, and ultimately lead to faster decision-making.
Although there isnt a one-size-fits all approachand it usually depends on the size of your organization and the resources you have available to commit to a data governance programa democratized approach will provide just enough data governance to see quick wins and measurable ROI, as well as allow you to start small and scale as you learn more.
From Disorder To Data Governance And Alignment: A Case Study
As life science organizations grow, so does the need for data governance. There is a requirement in this industry to ensure patient safety, and that entails a focus on the quality of products and compliance, which necessitates exceptional data quality. The challenge becomes how to best leverage people, process, and technology to gain control.
Every year, poor data quality costs organizations an average $12.9 million. Apart from the immediate impact on revenue, over the long term, poor quality data increases the complexity of data ecosystems and leads to poor decision making, according to a Gartner report released in July of 2021.
The solution to eliminating data-driven mistakes, is a robust data governance program. Simple to grasp but, challenging to implement. For many organizations, achieving Data Governance requires aligning the cross-functional stakeholders along with organizing and optimizing systems and processes, or as our client states in this case study, herding the cats and pulling the spaghetti apart, and bringing in the building blocks needed.
This case study illustrates how a biotech company leveraged ResultWorks expertise at creating alignment and implementing an effective framework for data governance to achieve criticaland sustainable success.
HOW RESULTWORKS ENABLED SUCCESS
The company engaged ResultWorks to both:
THE RESULTWORKS IMPACT KEY BENEFITS
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Weave It Into Business Processes
Treating a data governance strategy as a separate project doesnt work and can slow down compliance. Incorporate it into business workflows governance is a long-term practice that will reap rich dividends over time.
When comparing data management solutions, its easy to get overwhelmed by all the features and functionalities. Get our requirements template to shortlist your business needs and select the right tool for your organization.