Data Governance Roadmap Example: A Strategic Approach
Data Governance Roadmap Example: A Strategic Approach
Quality data is a critical success factor to the development of the business worlds most forward-thinking capabilities, such as data analytics, machine learning and artificial intelligence. As a result, data has a vital role to play when it comes to digital transformation.
Without proper data governance, however, organizations can end up building corrupt models, making inefficient decisions, or even breaking the law. To quote business thought leader John Ladley: some very hard and dangerous lessons are happening as bad data drives bad models that drive bad actions, all as the result of a deceptive or biased AI model.
Unfortunately, its harder than ever to establish a data governance capability. The amount of data gathered today is growing exponentially, with more and more sensors and sources of data. Furthermore, organizations are increasingly shifting their data to cloud platforms, which raises concerns over privacy, sovereignty, security and regulatory compliance ).
This means that organizations need to start paying more attention to data management and the sooner the better. Developing a data management and data governance strategic roadmap is an important step in helping you to understand why you need to manage your data, what needs to change, and visualize how you should implement your changes.
Healthcares Urgent Demand For Balanced Data Governance
According to the HealthIT.gov, by 2015 more than 80 percent of U.S. hospitals had figured out how to collect their clinical data. But, just as health system data has increased, so has mistrust in their data. According to a 2018 survey, less than half of healthcare CIOs have strong trust in their data. They often dont know how their analytics are derived or on what data they are based, and they rarely have any easy way to alleviate their concerns.
Balanced data governance builds trust and effectiveness. In years past, analytics teams emphasized data security. Protecting data was paramount, and it continues to be important today. But what good is data if only a few people can use it? To paraphrase Henry David Thoreau, that governance is best which governs least. Effective organizations secure their data from misuse. Beyond that, their job is to liberate data and facilitate its best use by everyone.
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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Get The Right People And Organize Them Appropriately
Data governance programs involve a lot of people. Even if your actual data governance team is small, your project will impact large numbers of employees, customers, partnersin short, anyone who depends on your data. Many of these people will have opinions, and some will voice them loudly. Dont be fearful of this. Embrace their passion but make sure to organize it.
Use a responsibility assignment matrix like RACI . This ensures that the right people provide inputand approvalsat the right time, and that everyone understands their individual responsibilities.
The person responsible is likely to be an experienced project manager who manages schedules, assigns resources, and builds the case. The person accountabletakes ownership of the major decisions and the results of the program. This is likely to be an executive-level person who owns the resources, and who has veto power. Consulted are the business and IT subject matter experts who will help you provide the necessary context to achieve your goals. And the informed are the people who will be affected by your data governance effort, but who dont have a direct say in the direction of your initiativesomething youll need to make clear from the start.
What Is Data Governance And Why Does It Matter
What is data governance?
Data governance is the process of managing the datas usability, security, availability, and quality within an organization using internally set and enforced rules and policies.
Why does data governance matter?
Data governance is a must for any organization that seeks to use their data for analysis. It creates an environment where data can thrive as a source of useful insight that enables the organization to prosper. Without it, data may fail to meet the quality standards necessary for usable insight extraction or be exposed to security threats that compromise its integrity thereby putting the organization at the risk of being sued.
Data governance provides consistency across all the organizations businesses thereby making efficient data integration possible. For instance, a suppliers name may be specified differently in the procurements office and the factorys database. During the integration of data, this may pose a challenge for the analyst who has never interacted with the supplier. It ensures that there is uniformity and that the analyst doesnt need to consult the departments generating the data in order to gain an understanding of the data.
What are the Goals?
Benefits of Data Governance
Data Governance Strategy Components
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Why Is A Data Strategy Needed
A data strategy is needed because, without a centralized vision and foundation, different parts of the company will view data-related capabilities differently. This inevitably leads to duplication of both data and data systems across the organization and thus makes it quite difficult to determine the truth from ones data while driving up costs associated with operational efficiency and effectiveness. A data strategy provides the basis for enterprise planning efforts connected to data-related capabilities and is a tool that allows for unification of Business and IT expectations for all enterprise data-related capabilities. The more detailed and comprehensive the data strategy is, the better the chance that the business and technical parts of the organization will fully understand each other. There is no better place than a data strategy to define the metrics or service level expectations that should apply across the enterprise. The data strategy is the best place to explain thoroughly how management of enterprise data can be leveraged to support organizational mission objectives or processes.
Phase : Do The Groundwork For Data Governance
As a groundwork for data governance, its essential to start from the very basics by answering the following questions:
An organization should first define the vision and mission of its data governance plan. An organization must also define the goals of the data governance programincreasing revenue, better decision-making, or transparency. Also, it should determine how to measure the success of the program. A clear vision helps employees and other stakeholders see how this data governance initiative is going to impact their day-to-day work life and how it is going to help them.
Assigning roles and responsibilities is a crucial step. This step defines who will be primarily responsible for different tasks involved in the implementation of the data governance framework. Often, organizations adopt a three-tier approach to set up data governance teams. The steering committee, data governance office, and data governance working group are three main components in this approach. Together, these groups decide the next steps in the implementation of the data governance framework.
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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.
Unclear Data Stewardship Protocol
Your procedures for handling and organizing data should be easy to understand. Training can help clear up some confusion, but you may need to revisit your governance protocols if there is a wide-scale issue. With sufficient documentation, you should find a way to reword or clarify your procedure so it’s less challenging to understand.
However, if it’s an ineffective procedure, additional revisions may be necessary. You can work with your data governance committee to create a more functional solution.
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Enable Early Adopters To Become Enterprise Data Governance Leaders And Mentors
As the first couple of teams achieve their data governance objectives, it may be time for them to generalize their processes for broader adoption. This shift in perspective from local to organizational naturally coincides with each early adopters transition from team leader to enterprise data governance leader. With new success under their belts, they are well-positioned to champion data governance generally and to recruit and mentor others their message is not just conceptualits based in their own hard-won experience, which gives them instant credibility and a host of concrete examples to drive home the how and why of data governance.
What Is The Difference Between Msp And Mssp
An MSSP like IBM Security offers security as a service on IT and information security systems: threat monitoring, infrastructure management, availability, capacity management, proactive protection and response capabilities.
A managed services provider generally provides only operational support to keep systems and applications at an agreed-upon service level agreement .
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What Is Master Data Management
Master data such as customer, product, asset and location data are embedded in a number of applications in a typical enterprise, only one of which is its ERP system. The field of master data management was developed in order to try to get a handle on this.
The idea behind a master data management strategy was to either copy data out from other systems into a trusted master data store that could then be used by other systems or to map where it was stored and document the differences e.g., a product might be classified in a simple hierarchy by one department or business unit but a more detailed way by another.
For example, a marketing department might care about the brand of a product, its packaging and whether it is on special offer, but a logistics department cares about the number of products in a palette, its dimensions and weight and where to deliver it. These different needs drive different categorizations, which in turn become embedded in different computer systems.
What Types Of Security Devices Can An Mssp Like Ibm Manage
Security information and event management tools, endpoint detection and response solutions and traditional network security tools are just some. Secure access service edge , cloud access security broker , container security software, and even cloud-native cybersecurity solutions within AWS, Azure, GCP, and IBM Cloud® can also be part of the engagement.
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Assess Projects After Completion
After each data governance project is completed, you shouldn’t merely pat yourself on the back and move on. After all, if the project wasn’t successful in achieving your goals, it will need to be adapted for the next initiative. Run some tests on your data to note changes and discuss with your team what processes should be streamlined and which ones need to be readjusted.
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Importance Of Data Governance
Data governance is a fundamental part of any organization that works with big data and results in consistent, common business processes and responsibilities across the map. It highlights the type of data that needs to be carefully controlled through the organizations data governance strategy. It sets clear rules relating to the roles of individuals with access or who are responsible for data, and the rules must be agreed upon across different departments of the organization.
For example, maintaining the privacy of patient information and health records is especially important in the healthcare industry. For organizations, such as hospitals or individual doctors offices, it is certainly necessary to manage patient data securely as it flows throughout the business.
Benefits Of Data Governance
The most notable benefits of data governance include providing improved data quality, lower data management costs, increased access to needed data across the organization, lower risks of errors being introduced, and ensuring that clear rules regarding access to data are both set, enforced, and adhered to.
Ultimately, data governance helps improve business decision-making by giving the management better and higher quality data, resulting in competitive advantages and increased revenues.
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Identify The Data Governance Priorities
Step 2 in developing a new data governance program is to identify the data governance opportunities that overlap with the organizational priorities step 1 identifies. These organizational priorities may even have been developed into improvement initiatives already.
All analytics depend on timely and reliable data, so its likely that finding overlapping data governance opportunities will be easy, especially in organizations whose data governance is still maturing. For example, if decreasing adverse drug events were one of the organizations priorities, proposing the creation of an accompanying data validation process to ensure the accuracy of the analytics would be an easy sell. Look for an implicit data governance prerequisite such as this and call it out
What To Include In Your Data Governance Strategy
As discussed, data governance is a broad category. Consequently, there are a number of things included in a strong data governance framework. As a start, it should include policies, rules, procedures, and structures for data management.
To help ensure that these procedures are focused and working towards the right objectives, its a good idea to create some foundational documents to guide the process. These should include:
- A mission statement
- Metrics by which the goals will be measured
- Clear guidelines detailing who is responsible for various aspects of data governance
Further, throughout every phase of this process, its important to document all parts of the framework and share them throughout the organization.
In addition to the above policies and procedures, some enterprises make the decision to utilize data governance software to help with implementation and enforcement. While this isnt a requirement, many organizations decide to utilize this software to help support:
- Program and workflow management
- Development of policies
- Process documentation
Such software can be an effective way to ensure that plans are effectively developed, implemented, and enforced.
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Quick Review: What Is Data Governance
Data Governance is the set of processes, and procedures organizations use to manage, utilize, and protect their data. In this context, data can refer to a subset of a companys digital or hard copy assets. Defining what data means to an organization is one of the core data governance best practices. Once you define what data means, you can formulate ways to use your data in ways that advance your business.
- Think of data governance as the who, what, when, where, and why of your organizations data.
Another key aspect of data governance is protecting both company and customer private data. Data breaches are near-daily occurrences and governments are constantly enacting laws and regulatory frameworks like HIPAA, GDPR, and CCPA. A big part of data governance is protecting the private data of customers and citizens. A good data governance program builds controls to protect data and help organizations adhere to compliance regulations.
Establishing Guidelines For Data Analysis And Application
Similarly, your data strategy should define guidelines for how employees should analyze and use data. Data governance can address this, and your business goals should inform how you interpret and apply your data. Although learning new rules may slow developers down at first, the long-term benefits will make up for this initial learning curve.
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Assess Your Current State
People. Think about the people in your organization and how they use data to make increasingly better decisions. Assess the current state of people and their roles in data governance by asking the following questions:
- Who is currently making decisions on your platform relating to data connections?
- Who is currently making architecture decisions for your platform?
- Who is currently overseeing access and security in your platform?
- Who is currently responsible for data/platform engagement?
Processes. Proper data and analytics hygiene help make better business decisions and better business decisions lead to a better ROI. Building proper processes that provide rules for data quality help build a better understanding of metrics definitions. Improper use of data can lead to unwanted litigation , churn, and loss of market share. One question can reveal a lot about the current state of an organizations processes:
- What are the current pain points in our processes?
Technology. The technology you use defines who can use the data and what they have access to. Technology makes your data secure and accessible, but its a moving target given fluctuations involved in updates, advances, turnover, etc. Technology also makes a difference in how the story of your data is told. Dashboards can either bore or inspire, confuse and complicate or create alignment and action. Ask these questions to assess the current state of your technology:
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