Integrate Data Governance Into Every Department
Beyond transparency, you want stakeholders to buy into data governance. So once you have your framework in place, it should be integrated into every department at your business. This will ensure a consistent stream of data collection and will provide your marketing, sales, customer service, and product development teams with insights that will help them achieve goals.
For example, your data governance office should be informing your product management teams about consumer behaviors and product usage reports. This information should be readily available and influence how you design product updates. With data governance, customer insights are streamlined to ideal endpoints, improving productivity across your entire organization.
It Governance And Metrics
The IT governance mechanism ensures that stakeholder needs, conditions and options are evaluated to determine balanced, agreed-on enterprise objectives. IT governance also ensures that direction is set through prioritization and decision making and that performance and compliance are monitored against agreed-on direction and objectives. Management plans, builds, runs and monitors activities in alignment with the direction that is set by the governance body to achieve the enterprise objectives.
The IT governance processes are evaluate, direct and monitor . Metrics are a monitoring mechanism and help management monitor the achievements of the enterprises business-related goals and IT-related goals. Appropriate metrics help the governing body provide direction that is based on defined goals and an evaluation of metrics. Metrics help enterprises answer valuable questions, such as:
- Is IT performance better than last year?
- What is the enterprise getting from IT investments?
- How can the enterprise benchmark performance?
- What should the enterprise do in the absence of measureable metrics? Can it use risk management, loss expectancy, attack vectors or correlation?
But What About Data Governance
Metrics are no less important to your data governance program than they are in the rest of your business efforts. In fact, there are two reasons why data governance performance metrics are a must-have:
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Communicate And Manage Change
Communication is your best friend. At a conference afew years ago, the IDC was mentioning that data governance is 90%communication. Maybe thats a bit much, but still, successful data governancerequires a lot of communication.
As I mentioned in an earlier key to data governancesuccess, data governance usually brings quite a bit of change and that needs tobe managed through:
- Executive support,
- Training, and so on.
Dont underestimate their importance and thats why Ithink this is probably the most important out of the 9 keys to data governancesuccess.
The Ratio Of Data To Errors
This is the most obvious type of data quality metric. It allows you to track how the number of known errors such as missing, incomplete or redundant entries within a data set corresponds to the size of the data set. If you find fewer errors while the size of your data stays the same or grows, you know that your data quality is improving.
Practitioners And Planners Have Different Views On How To Approach Data Governance
Best practices in data governance are still rare. However, there is widespread agreement on one point: technology is not the limiting factor. Businesses currently planning their data governance endeavor tend to focus on administrative tasks: they favor the development of a data catalog as their top-rated measure, followed by roles and processes. Practitioners concentrate more on practical execution, such as data quality monitoring and training. This way of working helps generate business demand. Challenging and developing users in data governance issues is a promising approach as it addresses the most widely identified challenges today and in the future.
Build Data Governance Plans On Gantt Charts
Before you can manage your data you need to implement your data with a Gantt chart. Our interactive Gantt chart collects all your essential tasks to better implement your data governance. You can link dependencies, set milestones to help you track progress and even filter for the critical path. Once done, set your baseline to monitor variance and make sure youre always on track.
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Let Governed Data Accelerate Your Business Growth
Governed data or data that is managed and secured by a specific department in order to ensure employees are only using credible and trustworthy data can improve your company’s efficiency, collaboration, and customer trust. Start leveraging these benefits by implementing a data governance framework today.
Editor’s note: This post was originally published in June 2019 and has been updated for comprehensiveness.
Create A Data Governance Framework With Independent Professionals
An effective data governance strategy is crucial in making sure that your company has access to secure, readily available, high-quality data. Aside from being a requirement for compliance, good governance also boosts productivity and cost-efficiency.
To ensure that data remains a competitive business advantage for your company, invest in an effective data governance strategy, and select the right team members to help you manage it.
Find your people with Upwork. Engage with top independent professionals in data collection, management, and governance from the worldâs work marketplace.
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Make Better Decisions With Better Data
Your data governance is only as mature as the data underneath it is good. To become truly data-driven, you must ensure you have a single, high-quality source of truth to work with.
Avo helps you create this single source of truth and make the right data-driven decisions to move your business by allowing you to prioritize, inspect, analyze, and future-proof your tracking to create reliable data now and tomorrow.
Determine A Data Governance Model
The next step is to create a data governance model for your team to work off of. This model should describe the hierarchy for who can view and distribute different types of data. This ensures that sensitive data is placed in the hands of your most trusted employees and isn’t shared without authorization. You can view one example of a data governance model below.
You should also describe your rules and regulations for data collection. Outline your standards for securing data as well as which channels you’ll use to obtain it. This will create consistency in your data collection which will lead to more reliable and accurate takeaways.
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Technology Is Not A Limiting Factor For Implementing Governance
Technology is not a limiting factor for implementing governance. Compared to other challenges, lack of tool support or wrong tools in use is of relatively minor significance to our panel. Unsurprisingly, data cleansing , data quality management and data integration are identified as the most needed functions to support data governance.
Assess Your Current Data Governance Strategy
As a business, if you collect and store information from clients and suppliers, you are already operating with a data governance system. Your next step is to find out what data governance strategies are being practiced in your company. This will help your team determine where the current system produces positive results and which ones need improvement.
If you need skilled professionals to consult or provide an assessment of your data governance strategy, Upwork has the largest pool of independent data analysts on the market.â
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Four Make Sure You Have The Right People For Data Governance
This should be a two-pronged approach:
For a data governance strategy to be effective, you need to engage with the key stakeholders as well as the data controllers and data users.
For larger organizations, this may be a team, or in smaller cases, it may be a single person. This team should be able to support the activities and governance across different areas of the business. They should also be able to engage with the key stakeholders and be able to process suggestions and manage support.
Interventions And Feedback Mechanisms
The results of these analyses lead to a determination of what interventions are needed, and those interventions feedback to the decision-making process. If the strategy is to increase awareness, then the impact of outbound email, the call center, or other actions can provide clues. Understanding the mechanics of the tools and how user behaviors can be influenced requires a level of fluency that can be difficult to gain given the rapid changes in customer experience technology. The choice of technology should always be guided by the engagement strategy at each step of the journey, which in turn will guide the analytics for each process and help identify what is important about each objective.
Imagine that customers coming to the site cant find what they need. It may be that the company is not segmenting audiences properly, or not using terminology aligned with their objectives. This will ultimately impact conversion. One lever to pull might be how the taxonomy is designed and how navigation is presented. Or customers may be navigating to a certain point and then not making selections. This outcome could result from how the product attributes and characteristics are presented.
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Centralized Data Governance And Decentralized Execution
The final model combines different aspects of the systems listed above. In this model, there’s an individual or team that controls the master data, but each team creates their own datasets to contribute information. This means that both management and team members are responsible for collecting and sharing internal data. This is great for larger businesses who are looking to streamline data to their management teams.
Now that we’re familiar with what data governance is and how you can implement it, let’s talk about some best practices to consider when creating a data governance framework.
Choose Technology That Fits Your Strategy Not The Other Way Around
It’s recommended above that you create a directory of data assets and the people associated with them — an informal catalog, not a full data catalog. Data catalogs that provide a single, consistent interface to curated data for use cases across an entire enterprise are useful tools. The same goes for analytics catalogs, which help manage how data is used through objects like dashboards, reports and data visualizations.
However, don’t build a data governance strategy around technology. Instead, select tools that specifically serve the strategy and your organization’s needs. The governance program will then correspond with the corporate culture and goals rather than work against them.
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Metrics To Measure Data Quality
What does data quality assessment look like in practice? Following are examples of metrics that typically help a company to measure data quality efforts.
|How much does it cost to store your data?||What is your data storage provider charging you to store information?|
|Data Time-to-Value||How long does it take for your firm to get value from its information?|
Implementation Of Data Governance
The first step in implementing data governance in any organization is deciding on the owner of the process, the custodian who will oversee it, otherwise known as the data steward. This person or team will help define processes to store, archive, back up and protect the data from either internal issues, theft or attack.
A set of standards and procedures are developed to figure out how the data is used by authorized personnel. Also, a set of controls and audit procedures are put in place to make sure oversight is ongoing and compliant with company and governmental policy.
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Number Of Empty Values
Empty values which usually indicate that information was missing or recorded in the wrong field within a data set are an easy way to track this type of data quality problem. You can quantify how many empty fields you have within a data set, then monitor how the number changes over time.
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Unlocking The Full Potential Of A Data Governance Framework
It’s important for business units across the organization to recognize the data governance team as a friend and ally in their business processes. After all, governance is about more than data protection and control of sensitive data. Data governance policies give business users access to the data they need, when they need it. Data governance is ultimately a tool to help optimize decision making.
Change management can help create a supportive culture that values data governance. You can head off concerns about new data governance policies by reassuring your business units that, implemented correctly, data governance processes dont slow business processes or prevent access to needed data. Rather, these policies ought to improve data access by enabling self-service delivery of trusted data to the right people in the right format at the right time all while ensuring data privacy and regulatory compliance.
Give data end-users positive reasons to appreciate data governance by communicating how it benefits their business processes. For example, automation of data privacy rules can help data users focus on data analysis and decision making, rather than spending time worrying about whether their workflows protect sensitive data. Providing examples of familiar use cases and tangible business outcomes will help build buy-in for any new processes you implement.
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The Most Striking Benefit Of Data Governance Is The Effective Use Of Data Based On A Unified Understanding
Targeted data governance creates strategic and operational added value. It is a mechanism to become more effective and efficient in the use of data. Creating a unified understanding of data can raise this effectiveness to a higher, overall strategic enterprise level and help a company along its path to digitalization.
However, if these actions are targeted at the data warehouse, their achievements will be of limited usefulness. Business value is ultimately generated in core business processes.
Increased Alignment With Business Goals
As data management continues to evolve, it will become more integral to business operations. Data governance will continue you to move away from being an IT-controlled area to become better aligned with business goals. Companies will continue to recognize the value data can have for them and incorporate it more seamlessly into their daily activities.
As data governance moves to the forefront, IT and the business will have the same goals, and IT will cease to be the data gatekeeper. This will also enable data governance to become a more consumer-focused initiative.
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What Are Good Metrics
Good metrics generally satisfy the following criteria:
- Consistently measuredMetrics must provide similar analysis over a period of time.
- Easy-to-gather dataThe cost of collecting data for metrics should be low, and the data must be collected through routine operational processes. However, this data collection must satisfy the requirement of being contextually specific. Some service metrics for IT may need more effort and, hence, cost to get data, e.g., how many customers could not be serviced due to an ATM that was not working?
- Expressed as numbers, percentage or unit of measureNumbers and percentages are easy to understand and compare therefore, as much as possible, metrics should be represented as a number or a percentage.
- Contextually specificIT metrics must measure the achievement of goals or objectives of business therefore, the metrics must represent the context.
Success Factors For Data Governance
Due to the complexity of the enterprise landscape and the often limited availability of human resources, companies need to find innovative ways to boost efficiency in governance tasks. Awareness is growing that machine learning and AI can play a central role in this area.
Participants identified convincing stakeholders and management support as major challenges. Consequently, management support and the identification of priorities based on corporate strategy are top of the list when it comes to critical success factors for data governance programs. The third priority making data governance a substantial element of all projects and processes appears difficult to realize in practice. The predominant focus of data governance initiatives on BI and data warehouse environments demonstrates that there is still a long way to go.
When these prioritized success factors are compared with current measures and established roles, it becomes apparent that data governance has not yet transferred from theory to practice. For example, only 24 percent of participants believe that internal marketing of data governance is important to convince potential stakeholders from lines of business. However, this is a crucial measure to secure management support. Overall, the findings of this study suggest that much of the potential of data governance is yet to be discovered.
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Identify Benefits And Opportunities
Focusing on the benefits that data governance provides can help you in creating your data governance strategy and help motivate people within the organization to improve how they manage data. When beginning to develop your data policies, take a look at your current practices and opportunities that improving them could provide. You can then develop your strategy around taking advantage of those opportunities.
Implementing a significant change within an organization is challenging, and having buy-in from others in the company is critical for success. Identifying the potential benefits of data governance can help get buy-in from upper-level management, which is necessary for launching such an initiative. You also need buy-in from others who handle data at all levels of the organization. When people understand the reason for implementing a change, they may be more motivated to do the work needed to make it.
Some of the benefits of data governance include improved data quality, better decision-making, enhanced operational efficiency, regulatory compliance and increased revenue.