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Organizations can make better decisions with data. Data allows everyone to be more efficient and effective in decision making, which can lead to better outcomes and success.
Most organizations have access to millions of data points, whether it’s sales data, marketing data, operations data, or consumer data. Data can come from multiple data sources and allows an organization to have information to run and optimize its operations.
Years ago, many organizations’ approach to data collection was, “Let’s collect what data is available, and worry about what to do with it later.” Fortunately, the current landscape is evolving in terms of data collection. Gone are the days when organizations could simply collect any data that was available. There are more and more privacy laws that aim to protect consumers by safeguarding the information that is being collected through their interaction with the organization.
Organizations these days are faced with the challenge of collecting data responsibly. This means they have to sit down and strategize what is the question we’re trying to answer and build a data collection strategy around it. Once critical and necessary data is collected, they have to implement and roll out ways to leverage, use, and protect the data they possess, especially consumer data. This whole process is what we call data governance.
Understanding Data Governance and Its Importance in a Privacy-Centric Landscape
So, what is data governance?
There are multiple available definitions online, and here are a few:
“Data governance is everything you do to ensure data is secure, private, accurate, available, and usable. It includes the actions people must take, the processes they must follow, and the technology that supports them throughout the data life cycle.” – Google
“Data governance promotes the availability, quality and security of an organization’s data through different policies and standards. These processes determine data owners, data security measures and intended uses for the data.” – IBM
“Data governance refers to the set of roles, processes, policies and tools which ensure proper data quality throughout the data lifecycle and proper data usage across an organization. Data governance allows users to more easily find, prepare, use and share trusted datasets on their own, without relying on IT.” – Qlik
“Data governance refers to the overall management of the availability, usability, integrity, and security of data used in an organization. It encompasses the processes, policies, standards, and technologies that ensure data is handled appropriately throughout its lifecycle, from creation or acquisition to deletion or archiving.” – ChatGPT
The simplest definition of data governance is below:
“Data governance is a framework that allows an organization to safely collect, use, leverage, manage, store, and protect data in a compliant manner, especially consumer data.”
It is a collection of individuals who are dealing with data, sets of policies and processes that everyone in the organization needs to follow, and tools that everyone can use in the day-to-day operation of the organization.
Why Is Data Governance Important?
As pointed out by InfoTrust’s Chief Growth Officer, Michael Loban, in his Forbes article, The 6 Ps Of Digital Analytics Transformation, there are six ingredients of a successful Digital Analytics Transformation that most organizations are going through. These are Purpose, People, Platforms, Process, Pace, and Payoff. Having a data governance strategy is part of the overall journey, and not having one could lead to potential issues in the future due to ongoing regulatory changes within the industry.
The importance of data governance differs from one organization to another, but it all boils down to the following:
Compliance
Data governance will help organizations comply with different laws, regulations, and standards related to data privacy, security, and usage. It’s a crucial component to avoid legal and financial penalties.
Data Quality
Data governance will help organizations in maintaining and improving the quality of data they collect. This can be accomplished by defining best practices and standards for data collection, usage, and storage. Data accuracy, consistency, and reliability will allow organizations to have good data to make future business decisions.
Risk Management
Data governance will help organizations identify and mitigate different risks related to data in the form of privacy and security incidents. Data governance will ensure that the organization has the right procedures in place that everyone can follow in the event of an incident. It also allows the organization to have guidelines on protecting sensitive information.
Decision Making
Data governance will help organizations with a framework for making decisions based on data through its accessibility, reliability, availability, and usability to decision-makers. An organization with the right data can lead to better business outcomes and competitive advantage.
Efficiency
Data governance will help organizations with the efficiency of data management processes by streamlining workflows, reducing redundancies, and eliminating data silos. This could result in cost savings and improved productivity for the organization.
The Key Pillars of a Data Governance Framework
A data governance framework is a set of procedures, policies, roles, and standards that are designed to guide an organization, especially its management, with the proper usage of data. The framework also provides a structure for collecting, using, handling, and storing data in a compliant and responsible manner.
Below are the key components of the data governance framework:
Data Governance Policies
A data governance framework will have data governance policies that will provide the organization with defined principles and objectives regarding data governance. These policies comprise policies around data quality, data privacy, data security, data ownership, and among others.
Standards and Procedures
A data governance framework will have standards and procedures that will equip the organization with detailed guidelines (standards) and a series of steps (procedures) that support the overall data governance policy. This will allow the organization to have consistency in data management and compliance with regulatory requirements.
Roles and Responsibilities
A data governance framework will have clearly defined roles and responsibilities for effective data governance. Every organization will have data owners, data stewards, and a data governance council.
- Data Owners: Responsible for specific data sets, ensuring their accuracy, security, and compliance.
- Data Stewards: Oversee the daily management and use of data, enforcing adherence to policies and standards.
- Data Governance Council: A senior leadership group that provides strategic direction and resolves conflicts.
Data Quality Management
A data governance framework will have processes and tools (data quality management) required to ensure data quality across the organization. This consists of procedures for data extraction, data profiling, data transformation, data validation, data completeness, and data consistency.
Data Privacy and Security
A data governance framework will have data privacy and security plans and procedures for protecting and handling sensitive information. This includes processes in handling both security and privacy incidents.
Data Lifecycle Management
A data governance framework will have a defined data lifecycle management that every data set will have to go through from the point of data collection or creation to deletion (if applicable).
Metadata Management
A data governance framework will ensure that proper metadata management is in place that will help in providing meaning and context to data for easier use. This involves creating, maintaining, and governing metadata repositories.
Data Architecture
A data governance framework will have data architecture guidelines to ensure that a proper and consistent data structure and data organization are rolled out from the point of data collection to data storage.
Data Governance Tools and Technologies
A data governance framework will have a collection of tools (data governance tools and technologies) required to monitor and support data governance activities, such as data cataloging, lineage tracking, quality monitoring, and compliance management.
Communication and Training
A data governance framework will have ongoing communication and training conducted across the whole organization. This includes ongoing collaboration for every stakeholder involved where everyone understands their roles and is aware of data governance policies and procedures.
Performance Metrics and Monitoring
A data governance framework will have clear sets of KPIs (performance metrics and monitoring) to measure the effectiveness of the data governance program, including tracking data quality metrics, compliance rates, and issue resolution.
In summary, every organization should have a robust data governance framework, which will allow them to maximize the value of their data assets while minimizing risks. It also provides the necessary foundation to manage data responsibly and effectively. The framework ensures data quality, enhances security, and ensures compliance with regulations, thereby driving better business outcomes and sustaining organizational success.
What Are the Benefits of Data Governance?
Data governance is important because as organizations collect increasing amounts of data, it becomes paramount to ensure that the data quality is accurate, consistent, and reliable. An organization will have many parties involved with data collection—centralizing and aggregating all of that data into a single source of truth is the benefit. However, it must be done while ensuring risk is minimized. This is fundamentally the role of data governance.
Good Data Quality
The chief significance of data governance is improved data quality. With a robust data governance framework, organizations can ensure that their data is accurate, consistent, and reliable. This leads to better decision-making, as decisions are only as good as the data they’re based on. In particular, when it comes to adtech, it is critical that organizations follow standard procedures for onboarding a new platform. Who is the new adtech platform’s owner within the organization? What is all of the new data stored? Can the data from the new adtech platform be integrated with existing systems to corroborate or add value to existing insights or generate new insights?
Improved Efficiency and Productivity
Good data governance ensures all of the guidelines, rules, and “institutional knowledge” is centralized in a single place. As an organization expands in size, it is common for it to become decentralized. This leads to the emergence of various power centers, each with different individuals in charge of different aspects of the organization. Premature distribution of responsibility can also cause problems with people operating according to different standards. When it comes to governance, it’s important organizations are at least using the same tools to decrease redundancy.
Regulatory Compliance
In an era of stringent data protection regulations like GDPR and CCPA, as well as the many state privacy laws that are coming online, data governance is no longer optional. It helps organizations comply with these regulations by providing a clear framework for data management, thereby reducing the risk of non-compliance and hefty fines.
Good Data Governance Grants Confidence
All data in an organization has a life cycle. It is not simply a matter of “collecting everything, keep it forever, and then revisit later for insights”. First-party data is time-sensitive. What was trending last year may no longer be en vogue this year. There is always a lag between the time data is collected to when it yields actionable insights and results. A solid data governance framework will help give you the confidence that the data you’re collecting is the entire and freshest picture, so you can then go to work on it. This means it’s necessary to onboard new data, retain it, and discard it when the time is appropriate. Deleting valuable data before it’s necessary can hurt revenue—but keeping data forever and analyzing the wrong sets could also cost revenue.
Enhanced Security
Data governance includes the establishment of data policies and procedures that ensure data is used and accessed appropriately. This enhances data security and reduces the risk of data breaches, which can have devastating financial and reputational consequences.
In summary, data governance is imperative in today’s data-driven world. It enhances data quality, improves efficiency, ensures regulatory compliance, and enhances security. By investing in data governance, organizations can unlock the true value of their data and gain a competitive edge in the marketplace.
Examples of Data Governance Use Cases
Data Governance in Marketing
Marketing is important for five chief reasons: attracting customers, increasing sales, building brand awareness, understanding customer interests, and growing the business. Data governance is germane here because in order to attract and understand the different customer segments for your business, an organization will need to collect data on their customers. If data is being collected, it must be via some source (for example, an adtech platform). Now, the questions become: is the adtech platform compliant with existing regulations? How long is the customer’s data stored? Who can access it? These are all relevant questions concerning data governance in marketing.
Data Governance in Data Analytics
There is a common saying in programming which also applies here to data analytics: “garbage in, garbage out.” Meaning, insights and analytics built upon data are only as good as the data itself. If the underlying data is incomplete, then the insights will be faulty. Data governance is important in data analytics because only via good governance can you ensure the data is fresh and complete. When was the data last updated? By what source? Who can look at the sources? Good data governance will ensure data quality, which is imperative for data analytics.
Data Governance in Banking
Because banking is so highly regulated, good data governance is critical because when banks are audited, being able to show provenance and an audit trail is what the regulators will ask for. Good data governance ensures there is a legible and complete paper trail that can easily be given over to regulators. When did an exact event happen? What change caused it? To this end, data governance is about recall and legibility. In the absence of a data governance platform, when regulators solicit an organization for data, the organization will need to scramble to produce the artifacts. With good data governance, when regulators call, it’ll be easy for an organization to immediately comply with minimal additional effort because they’ve been tracking everything all along.
Data Governance in Healthcare
Healthcare is also highly regulated. Additionally, healthcare in the United States is incredibly siloed. Thus, there are two reasons good data governance matters. One is similar to banking—in case regulators come soliciting, what transpired for what patients can be easily produced. But secondly, healthcare is a huge industry in the United States composing more than 18 percent of GDP in 2021. Hospitals work with medical drug manufacturers and pharmaceutical companies. Data around clinical trials are kept. Since there is so much data flowing between all of the different parties involved, it can literally be a matter of life and death to have good governance procedures to ensure that data is accurate, consistent, and reliable.
Data Governance in Higher Education
In higher education, there are two areas where high-quality data governance matters: the first is around the efficiency and production of research. Because much of higher education receives grants from various foundations or government entities, it’s important that whatever research conducted produces data that is accurate, consistent, and reproducible. If a lab is sloppy with their data governance procedures, that could cause downstream results to be inaccurate.
The second reason is around student data as related to financial and academic records. There are various laws and regulations such as the Family Educational Rights and Privacy Act, the Gramm-Leach-Bliley Act, and the Fair and Accurate Credit Transaction Act of 2003 which require accurate record keeping and only the correct parties access the appropriate records. As records have largely all become digitized, it’s thus critical that higher education institutions practice good data governance.
Data Governance Roles and Responsibilities
The data governance program generally involves business executives, data management professionals, as well as end users. The responsibilities may differ depending on the specific structure, resources, and goals of each organization’s distinct operations. Below are the most common roles and their responsibilities in the data governance process:
| Chief Data Officer | - Plays the lead role in the data governance program - Holds the overall accountability for their organization’s data program and has the authority to make decisions |
| Data Governance Council/DPO | - Ensures strategic alignment of data to organizational goals and compliance - Establishes data governance policies, procedures, and standards - Maintains and manages vendor contracts |
| Data Stewards | - Create and oversee data assets - Ensure data quality and management - Define and implement governance policies, as well as enforce compliance with regulatory requirements |
| Data Custodians | - Deals with the technical environment of database structure - Focus on movement, security, storage, and use of data |
| Data Quality Analysts/ Engineers | - Track data quality metrics, identify data quality issues - Work with the governance team to resolve data errors |
| Data Users | - Produce and leverage data to draw insights from - Drive use-cases for business decision-making |
Getting the right people into the right positions is key to a well-designed data governance program. By successfully assigning these roles and even customizing functions as needed, organizations can create an effective framework leading to the accomplishment of its data-driven goals.
Data Governance Best Practices
Data governance is an essential practice that helps organizations ensure compliance and protect user privacy while maximizing the value of their data assets. Though the benefits of good governance practices—such as building trust, avoiding legal issues, and enhancing operational efficiency are clear, figuring out how to implement these practices can be challenging. Here are a few things to keep in mind when taking on a data governance initiative.
Data Privacy and Compliance
Understanding Legal Requirements
Every day, data privacy laws such as General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA) are becoming more and more stringent. To avoid hefty fines and legal repercussions, organizations must have a thorough understanding of what data is subject to these laws, what rights a user has, and what mechanisms are available to factor these rights into their organizations data collection practices.
Implementing Privacy by Design
Privacy by Design (PbD) is the practice of being privacy-centric when developing and operating IT systems and business practices. Conducting privacy impact assessments, minimizing data collection, and ensuring robust security measures from the onset can set an organization up for success.
Data Security
Encryption and Anonymization
When paired together, encryption, which makes sensitive data unreadable, and anonymization, which strips personal identifiers from datasets, are powerful tools that make it impossible to link data to an individual user. When employed correctly, they provide a way to safely collect data without compromising privacy.
Access Controls and Authentication
Implementing restrictive access controls such as multi-factor authentication (MFA) and role-based access control (RBAC) helps to ensure that only authorized personnel have access to data, and only the data they need to see. Regular auditing of access logs helps to maintain credentials and prevent unauthorized access.
Data Governance Framework
Establishing Policies and Procedures
There are many facets to designing comprehensive data governance procedures that span the entire data lifecycle. It is important to start with a foundation of policies centered around legal requirements that cover things like data collection, storage, processing, sharing, and disposal. Establishing a procedure that tracks new data collection requests, reviews them for approval, and processes for discontinuation of collection allows for an effective way to control what data is being collected and by whom.
Defining Roles and Responsibilities
Aligning roles and responsibilities with specific tasks is an essential part of turning the necessary actions into a more manageable procedure. Key roles in this framework often include process owners, team members who are responsible for overseeing governance procedures, and data owners, who are responsible for data collection, management and security. A legal advisor is also a vital part of the governance process that advises based upon their interpretation of user privacy regulations.
Data Transparency and User Consent
Clear Communication with Users
Transparency is a cornerstone of user trust. An organization must communicate clearly with its users about how and why their data is being collected, used, and shared. A great place to start is providing easily accessible and understandable privacy policies and ensuring users are aware of their rights and choices regarding their data.
Obtaining Informed Consent
When an organization obtains informed consent, that means that the user is made aware of what they are consenting to in a clear way. The user’s consent should be specific, clear, and easily revocable should they change their mind. This can be accomplished by implementing Consent Management Platforms (CMP) that are specifically designed to provide a mechanism for users to manage their consent preferences.
Continuous Monitoring and Improvement
Regular Audits and Assessments
As part of their governance framework, an organization should conduct regular audits and assessments of their data and collection practices. Internal audits, third-party assessments, and governance monitoring tools help shore up gaps and search for areas of improvement.
Adapting to Changes
The data governance landscape is constantly changing and an organization’s practices need to evolve with it. Staying updated on industry trends, as well as investment in robust governance procedures and education, can lay the groundwork for a user data governance plan that is effective in the long term, but once the process is in place, it needs to be reviewed and improved proactively to maintain its value.
What Are the Phases of the Data Governance Process?
The data governance process can significantly vary depending on the organization, due to factors such as regulatory requirements, data complexity, organizational culture, and business needs.
As data is heavily influenced by activities involved in the business, highly regulated industries such as finance and healthcare will have a more complex data governance structure and often require strict controls over their data to ensure compliance and protect sensitive information. On the other hand, industries such as technology and e-commerce may prioritize data governance processes that focus on data analytics, customer insights, and active data management to support rapid innovation and competitive advantage.
Even within the same industry, the data governance process may still look different. In terms of size and structure, large corporations with vast amounts of customer data will likely have a more formal and structured data governance program with dedicated teams and resources across multiple regions. Smaller companies under the same sector might have a simpler and localized approach, with data governance responsibilities spread across different departments.
Despite these variations, there are important aspects of the process that apply to every organization. These are generally accepted phases, which provide a framework for implementation:
Data Governance Maturity Assessment
This phase of the data governance process involves evaluating the current state of an organization’s data management practices and identifying areas for improvement. The assessment helps in understanding how well data is used, managed, and secured within the organization. It typically involves a data audit—analyzing data points and risks to determine the maturity level of the organization’s data capabilities and to design a roadmap for enhancing internal governance processes.
Building a Data Governance Strategy
Developing a comprehensive plan and framework is a crucial phase of the data governance process. This is where the structure for data governance is defined, and data policies or standards are established. This process also includes assembling the correct team, as each member will have their essential role in the following phases.
The data governance strategy should create a process that aligns data management practices with business objectives, regulatory compliance, and best practices, ensuring that data is treated as an asset and managed effectively across the organization.
Data Governance Implementation
In the implementation phase, the data governance strategy is put into action. The policies, processes, and standards established in the earlier stages are integrated into the organization’s daily operations, fostering a culture of data accountability and responsibility while driving continuous improvement in data management practices.
Data Governance Measurement, Monitoring, and Iteration
The organization should also be able to set industry-standard metrics to measure the effectiveness of its data governance program. This is the phase where the ongoing monitoring of data quality is in place, along with compliance with regulations, and the overall performance of data governance initiatives.
Based on the insights gained from monitoring, the organization can iterate and continuously optimize its data governance practices and ensure that they remain effective in the long term.
Data Governance vs. Data Management vs. Data Security
While related when it comes to data, management, governance, and security are all distinctly different concepts. Today, we’ll unpack these terms.
- Data management is the broadest category. It refers, simply, to all of your organization’s data—data that your organization produces, data that your organization consumes, and how it all flows through your organization. When it comes to data management, you’re largely concerned with data pipelines (flow), preparation, and storage. What data do you wish to keep? What is ephemeral and fine to discard? What data from which source do you wish to derive insights? Extract, transform, load, and operations can also be discussed here.
- Data governance is one component of data management and specifically refers to ideas around who owns and accesses the data? Here in governance, we begin by talking about normative judgments: namely, the organization should or should not collect specific customer data. And under what circumstances. Additionally, data governance is concerned—not just with selecting what sampled data should be collected, but then also—within the organization, who can access that information? And if necessary, what steps (such as obfuscation or anonymization) are required to desensitize data so more general audiences can read from it to derive insights. Also in the realm of governance is ensuring that the organization is compliant with whatever laws and regulations relevant to various regions as well as what other third parties the organization is downstream and upstream of regarding data streaming. If the organization has partnerships or agreements with third-party vendors, governance is also relevant here: who owns the relationship and the data involved thereof?
- Data security has to do with how data is secured at your organization. What security measures are in place to prevent unauthorized access and consumption? There are various industry standards to ensure data is secure and not vulnerable to unauthorized access or attack. In the realm of data security, access controls, encryption, and network security and compliance are relevant. Finally, recoverability and architecture is key: if there is an attack, how to prevent data loss and compartmentalize the damage? These are also relevant areas of data security.
For any organization, proper data management, governance, and security is critical. As services become increasingly fungible, collecting user data and aggregating insights from that data is increasingly the chief differentiator and competitive advantage for an organization.
As regulations around the world tighten, data is also increasingly moving from third-party to first-party collection and retention. The advantage of owning first-party data is that the organization will be able to centrally collect all data on their properties (via a technology like server-side). Once all of the customer data is collected directly to the organization, they can then decide what third parties (Meta, Facebook, etc.) to downstream the data to. One downside at least in this initial stage is that the adtech industry will likely increasingly consolidate as third-party cookie deprecation happens and more customer data is centralized in just the hands of a few players. For this reason, it is more important than ever to maintain proper data management, governance, and security practices.
Cloud Data Governance
As an organization trying to maintain good data governance practices, having to account for cloud platforms adds a layer of complexity. Besides the normal governance concerns, cloud platforms require special consideration regarding minimizing security risks. These considerations take the form of things like encryption, access controls, security groups, audit trails, and application access rules, which can all amount to added peace of mind, or added headaches when data is being sent to and from the cloud.
The key to successful data governance for any organization starts with having a clearly defined set of roles and responsibilities that combine to form a data governance framework. But what does that mean specifically for managing data stored in the cloud?
With the ability to pool all of an organization’s data in one place, the origin of that data can become murky, making the data itself unreliable. Having people assigned to know where the data comes from is essential to validate its credibility, as insights pulled from the same source can become increasingly self-referential when left unchecked. But that alone is not enough. The distributor of that data is also accountable for understanding the data lifecycle, including where it came from, how it changed over time, who was responsible for those changes, and whether it still accurately represents the source material.
Drawing insights from unreliable data leads to unreliable results. Establishing data quality metrics that measure data accuracy, consistency, completeness, and integrity across all systems, as well as quantifying the increase in dividends that result from improving those metrics, helps organizations to understand and prioritize data accuracy and improvement. By actively improving these metrics and being transparent about them with the public, an organization can increase trust and loyalty with their consumers.
It is possible that there exists a comparable set of positions to those in charge of understanding the data lifecycle, with equally important stewards who are responsible for sensitive data ownership. If no one knows why sensitive information is being collected, then it does not need to be. While data stewards are responsible for identifying, escalating, and remediating sensitive information, data owners are accountable for the collection and access to sensitive data points. Together they ensure that sensitive data is protected, and only those who need to have access to it are allowed if a business need justifies it.
It is important to note that once the data is confirmed to be valid and necessary, having data stored in the cloud can help to solve data governance issues as well. Regulations such as CCPA and GDPR prohibit the transfer of data across borders, but by being able to handle direct queries from anyone, anywhere, and at any time, the cloud removes the need for manual sending of data and files.
That is not to say that any user should be able to access all cloud data all of the time. With the increased availability of data via the cloud, it is imperative that organizations restrict access and protect data through the use of things like role-specific access, multi factor authentication, and data masking or encryption. Creating strict and enforceable security policies that are implemented at all stages in the data collection and user management processes can help organizations avoid a wide array of threats, such as data leakage, data corruption, and criminal manipulation that can lead to monetary, legal, and/or reputational consequences.
Lastly, perhaps the most noteworthy component of cloud data storage, and data collection in general, is the data that is not collected or kept. By collecting data that is not needed, organizations increase their vulnerability. The more data is being stored, the more effort is required to maintain it, keep it secure, and ensure its integrity. Only collecting information that is relevant to key business insights and purging information that is no longer used makes the entire data governance process more manageable.
Data Governance Challenges
At this point, most organizations are aware of the importance of data governance for verifying that their data is consistent, trustworthy, and safe. Despite this knowledge, it is common for organizations to face significant challenges when trying to implement and manage effective data governance practices:
Lack of Clear Vision and Strategy
To have good data governance practices, an organization must develop a clear vision and strategy. More often than not, organizations will have difficulty defining what their goals are for governance, which can lead to efforts that are fragmented and inconsistent, causing confusion, inefficiencies, and false starts. An effective plan is one that aligns data governance initiatives with business objectives and establishes roles and responsibilities that all stakeholders involved understand.
Resistance to Change
Change is often uncomfortable, and implementing a governance framework undoubtedly requires significant changes to existing processes and practices. Employees are likely to see it as a signifier of increased workload, which can increase their resistance to the necessary adjustments. By effectively communicating the value of the new data governance procedures to their employees, an organization is less likely to be met with friction in adopting the new procedures.
Data Silos
When data is isolated and not shared across an organization, it can often lead to inconsistencies, inefficiencies, and redundancies that increase the difficulty of maintaining data integrity and accuracy. To break down these barriers, an organization needs to shift towards a culture that places a value on cross-departmental collaboration, with an emphasis on data unification and standardization.
Complexity of Data Environments
Having robust data management tools and technologies that can adapt to a wide variety of sources can help to alleviate some of the difficulty that is posed when trying to standardize and enforce governance policies. Anymore, it is common for an organization to have data not just in on-premise databases, but in cloud services and third-party applications as well, and being able to manage them all easily is key to successfully upholding a governance framework.
Ensuring Data Quality
Many organizations find it difficult to maintain high-quality data, which is foundational to impactful data governance. When trying to gain meaningful insights, running into inaccurate, outdated, or incomplete data can undercut well-intentioned governance programs and cripple decision-making. Only with strict quality standards that include regular audits, monitoring of metrics, and data clean-up processes is it possible to ensure that data maintains its integrity.
Regulatory Compliance
Staying up-to-date with regulatory standards, such as GDPR, CCPA, and HIPAA, is a difficult but essential part of any organization’s governance framework. New updates to these regulations happen frequently, which requires processes that are dynamic and adaptable. Failure to comply can result in hefty fines and legal action, which can ultimately tarnish an organization’s reputation.
Lack of Skilled Personnel
Expertise in data governance is a niche that requires an understanding of both the technical and business sides of data management, and people with this particular skill set are in short supply. To build a competent team that can drive data governance initiatives, organizations need to invest not only in hiring people with experience in the data governance domain, but in the training and development of existing team members to help offset the shortage of available candidates.
Measuring and Demonstrating ROI
Data governance is not necessarily something that is easy to see an immediate return on investment. Organizations are familiar with measuring key performance indicators in other areas, but things like improved decision making or compliance may not appear to be as easily quantified. Measuring these benefits may not seem clear-cut at first, but ultimately having metrics to gauge the success of a governance initiative is imperative to securing continued support.
Make Data Privacy, Strategy, and Governance Easy with InfoTrust
Data has become the lifeblood of organizations across industries in today’s era. From customer preferences to market trends, businesses rely heavily on data to gain a competitive edge and drive business growth. However, it also comes with significant challenges as data privacy regulations are evolving and risks of breaches are present. At the same time, customers themselves are increasingly concerned about how their information is used. This complex data landscape is where InfoTrust comes into play, empowering organizations to strike a balance between leveraging the power of data and protecting user privacy.
InfoTrust is a leading provider of expertise and technology solutions. We work closely with organizations to assess current processes and identify potential risks as well as opportunities to develop data privacy strategies and governance frameworks aligned to their objectives. In every engagement, InfoTrust assists its clients in discovering the hidden value of their data and supports them on how to capitalize on these resources effectively.
In addition to data privacy and strategy, InfoTrust also specializes in enhancing data governance capabilities. Organizations can utilize cutting-edge technologies like Tag Inspector—a tool designed to audit and monitor data collection and usage. The valuable insights derived from the tool enable them to streamline compliance with privacy protection policies and regulations such as GDPR and CCPA. Moreover, InfoTrust guides clients on the importance of tag management and augmenting data security measures to mitigate risks associated with poor data governance.
A range of organizational goals can easily be within reach through proper data governance. Regulatory compliance is already one, where adhering to legal requirements and industry standards not only saves a business from penalties and fines, but more importantly, builds trust with customers. Likewise, a culture of integrity is also achieved through risk assessments, which ensure legal, transparent, and secure data. By implementing best practices, organizations can minimize the risk of data leakages and unauthorized access to sensitive information. Most of all, effective data governance improves data quality. Partnering with InfoTrust equips organizations with such data-driven potential—and with accurate and consistent data at their disposal, they can make better and confident decisions they need to succeed.
