Data Governance Strategy

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Data governance refers to the overarching framework and processes that organizations put in place to manage and control their data assets effectively. Whereas data management involves the day-to-day process of collecting and storing information, governance sets the rules on exactly how you and your team can use, share, and protect said data.

Just like with any other area of business, you must develop a data governance strategy that will guide your organization's management practices. These should be tailored to your overall business goals so that you can make informed decisions that will pave the way for your company’s growth.

Let’s explore why you should put a data governance strategy in place, what it should look like, the tools you’ll need, and the challenges you may face along the way.

Why Your Company Should Have A Data Governance Strategy

Your governance strategy is your game plan on how everyone in your organization can manage data effectively. Here are three specific reasons you should make time to create a comprehensive strategy:

Comply with regulatory standards

A data governance strategy is your first line of defense in complying with regulatory standards. Whether it's GDPR, HIPAA, or any other data protection laws, a well-defined data governance program makes sure your company complies with these rules.

Moreover, it also prepares your organization for future regulatory changes. This proactive approach safeguards your business against legal implications and also builds trust with your customers and stakeholders.

Drive better decision-making through accurate and reliable data

High-quality data is the foundation for strategic decisions. With a data strategy in place, you can make sure every aspect of your business benefits from improved data quality.

For instance, if you’re planning to expand your operations to a new market, having trustworthy data about your current market's demographics, consumer behavior, and competitors can help you identify opportunities and avoid unnecessary risks.

Aside from making the best decision every time, you can also feel more confident that you have the right data to use with the right controls in place as opportunities present themselves such as in mergers or acquisitions. 

Protect sensitive information and manage risks

Cybersecurity remains a top concern for organizations today, especially small and medium-sized businesses. Moreover, cybercriminals now use AI technology to make malware less detectable.

As such, organizations need a data governance strategy to protect sensitive information and manage risks effectively. It’s where technology and tools come into play, providing layers of security and control over your data assets through auditing, encryption, access controls, and data masking.

What Does An Effective Strategy for Data Governance Look Like?

An effective governance strategy is not just a set of rules; it's a roadmap to maximizing the potential of your company's data assets. Let's break down what this strategy looks like in practice.

Identify and prioritize existing data

The first step in a solid data governance strategy is to do an audit of the data you have, where it resides, and its importance to your business objectives. This way, you can find areas of improvement and prioritize which data assets require the most attention.

Have clear objectives and goals

An effective data governance program is goal-oriented. You must know what you want to achieve through your business data and create steps that lead to that ultimate goal.

Let’s revisit the market expansion example from earlier. For you to achieve this, you will need to set clear objectives such as identifying target markets, understanding customer preferences, and evaluating potential competitors. These goals will guide your data governance strategy and make sure that you collect and analyze the right data to support your expansion plans.

Develop detailed data policies and standards

A key component of data governance is having detailed policies and standards in place. These policies should cover aspects like data access, data quality, and data privacy. They are the rulebook that serves to guide everyone on how to handle data responsibly.

Implement a comprehensive data quality control process

As mentioned, high-quality data is important for strategic decision-making and planning. When you lay out the roadmap for managing and monitoring data, you need to include a comprehensive data quality control process.

It should cover procedures for data cleansing, data validation, data integration, and data reconciliation. These aspects of data governance help identify and correct any errors, inconsistencies, or duplications in your data to ensure its integrity and reliability.

Integrate technology for efficient data management

It’s estimated that a whopping 181 zettabytes of data will be produced by 2025. While you may not be interacting with such information size, your business must be prepared to handle receiving large volumes of data each day. After all, you won’t have the time or workforce to process and manage all that data manually.

Leveraging data management platforms, data integration tools, data analytics software, and other technologies can automate your data processes.

Prioritize continuous monitoring and improvement

Since technology is always evolving, your data governance strategy will require continuous monitoring and regular improvements. It means staying on top of emerging technologies, industry best practices, and regulatory changes so that your data governance program remains effective and up-to-date.

Foster a data-driven organizational culture

Finally, an effective data governance strategy cultivates respect and appreciation for data across the organization. It supports an environment for evidence-based decision-making, rather than through assumptions or personal opinions.

To achieve this, you must include training on data literacy and interpretation in your strategy. Encouraging collaboration across departments to share and leverage data effectively can also provide a holistic view of the business and a sense of engagement with data-driven initiatives.

Data Governance Technology: 6 Essential Tools To Use

Having the right tools can simplify your data governance processes and make sure that you implement your strategy effectively. Here are six tools you should invest in:

Data quality management tools

Data quality management tools offer functions like profiling, cleansing, and matching data. They keep your data accurate, consistent, and reliable by identifying and resolving any errors or inconsistencies.

Informatica is a great tool for data quality management with its advanced data profiling and cleansing capabilities. Another solution is from Ataccama, which leverages machine learning algorithms for data standardization, enrichment, and deduplication.

Data cataloging and metadata management systems

Data cataloging and metadata management systems help you organize and manage your data assets. They provide a central place for storing metadata, such as data definitions, data lineage, and data classifications.

Collibra is a popular tool because of its collaborative approach to data cataloging. It offers an interactive environment that allows you to crowdsource data asset annotations and generate a knowledge graph.

Meanwhile, Alation stands out for its AI-powered data discovery and stewardship capabilities. It provides recommendations, search enhancements, and automated insights into data usage and quality.

Data security and privacy solutions

Data security and privacy tools help you protect sensitive data through encryption, access control, and data masking. With these, you can make sure that only authorized users can access and view sensitive information. They also aid in complying with privacy regulations.

IBM Guardium is a widely used tool for data security and privacy. Its standout feature is that it can provide real-time surveillance and analysis of data activity across all your databases and data stores through its advanced analytics capabilities.

Symantec Data Loss Prevention (DLP) is also a great option. It uses deep content inspection and a context-aware detection engine to detect potential data breaches or leaks accurately in real time.

Data integration and ETL platforms

Data integration and ETL platforms consolidate data from multiple sources and transform it into a usable format. These tools allow you to get data from different systems, cleanse and transform it, and load it into a target destination.

Talend offers a unified, open-source platform to simplify the data integration process. Its open-source nature allows for flexibility and customization without requiring in-depth technical expertise.

Another popular alternative is Informatica PowerCenter, which supports various data integration requirements. This ranges from high-volume, batch-oriented data warehousing to real-time, transactional integration.

Data governance reporting tools

Having a tool for data governance reporting can help you see where your data governance strategy stands and monitor its progress. These tools provide visualizations and reports that give insights into data quality, compliance, and overall governance effectiveness.

IBM InfoSphere Information Governance Catalog uses sophisticated scanning and indexing techniques for users to explore data definitions, lineage, and relationships easily. This way, organizations can have a clear understanding of their data landscape and maintain proper governance across all data systems.

If you’re looking for a data governance framework, you can check out SAS Data Governance. With this cohesive approach, you can manage all aspects of data governance in a single platform, from data quality to metadata management and data lineage.

Data lineage software

These solutions help you track and understand the journey of your data, from its origin to its destination. It shows you how data flows through your systems and helps you identify gaps or potential issues in your data processes.

Manta automates data lineage mapping to analyze data management environments. As such, it provides a comprehensive view of how data moves through your systems and helps you identify any gaps or issues in your data processes.

Another option for data lineage software is erwin Data Intelligence, which offers end-to-end data lineage capabilities. It features automated metadata harvesting and advanced algorithms to trace data flows for comprehensive visibility and understanding.

5 Common Challenges Companies Face in Implementing a Data Strategy

Having a data governance journey can change your organization’s processes and decision-making for the better. However, implementing your plan comes with its own set of challenges. Here are five common challenges that companies face:

Addressing the complexity of multi-source data integration

Integrating data from multiple sources and making sure they actually work together is a common headache for many data teams. Each data source might have its own format, standards, and quality. As such, creating a cohesive and accurate view of the data becomes difficult or, at the very least, time-consuming.

If not managed properly, this challenge can lead to inconsistent data, which can negatively affect your decision-making and analytics. Data integration solutions can aid in combining and standardizing data from multiple sources. These tools transform and cleanse the data by taking the raw inputs from various sources and converting them into a common format.

The key here is not just to merge data but to do it in a way that maintains its integrity. You can start by identifying and understanding the nature of your data sources. Then, invest in a data integration platform to automate and simplify the process. You should also do audits and quality checks regularly to keep data consistent and reliable.

Another solution is to store information in data lakes. These store data in their raw form, which allows you to apply transformations and data cleansing as needed or when the data is actually being used.

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Complying with evolving global data regulations

General Data Protection Regulation (GDPR) and other national and local consumer privacy acts are always changing. As such, navigating through these and ensuring compliance can feel like you’re trying to hit a moving target. Worse, non-compliance can lead to hefty fines and reputational damage.

To stay ahead of the curve, you should create a dedicated team to monitor regulatory changes. Part of their role would involve assessing the impact of these laws on your organization's data governance strategy.

Regular training and awareness programs for employees are also crucial for everyone to understand the importance of compliance. Of course, you should leverage tools for data security and privacy to protect your data and stay compliant with regulations through encryption, access controls, and data masking techniques.

Preventing data silos and departmental barriers

It’s so easy for organizations to create data silos with the different departments within the company. Each department may have its own systems and processes for holding valuable information but they are not connected or shared with  other teams.

This separation often leads to inconsistencies since different departments might end up working with incomplete or outdated information. There’s also the risk of having data duplication, which makes your processes inefficient.

The first step to overcoming this challenge is to understand how data silos can form. These are often a result of departmental autonomy and a lack of communication between teams.

That’s why you should have an overarching data governance strategy to promote collaboration and data sharing across departments. In your data plan, you can emphasize the use of a comprehensive data management system, which includes capabilities for data integration, data quality, and data lineage. With this platform, all departments can have access to the most up-to-date and accurate information regardless of the system they primarily use.

Furthermore, developing clear data governance policies and procedures can help prevent data silos. These serve as a guide for defining and enforcing how data is managed and shared across your organization.

Managing big data and high-volume data streams

The sheer volume, velocity, and variety of today’s data can be overwhelming for most businesses. The challenge lies in storing, processing, and analyzing big and high-volume data effectively.

If not addressed, it will lead to missed insights and opportunities. Dedicated technologies, such as Apache Hadoop, can help you handle large datasets. These technologies offer distributed storage and processing capabilities through their software frameworks that allow for parallel and scalable data processing.

Additionally, cloud-based platforms like Amazon Web Services (AWS) provide scalable storage and processing solutions for big data. These are useful if you foresee the need for flexible and scalable infrastructure to handle your data requirements in the future.

To effectively manage big data, you should first clearly identify your current data storage and processing needs. We also recommend forecasting your future data growth for scalability. Based on your requirements, you can then choose the right platform or system for your company’s needs.

Enhancing data literacy and compliance across the organization

Building a data-literate culture is necessary but challenging. Even if you have the right tools, your team can’t maximize these resources if they don’t understand the importance of using data responsibly.

Regular training and awareness programs can boost data literacy in your company. You can start by organizing workshops and seminars to discuss the importance of data and how to use it effectivelyAgain, here’s where having a data governance strategy comes into play. You can outline the specific steps for teaching your teams about data access, data privacy, and data security.

Another key step is to embed data governance into the company's core values and daily operations. You can achieve this by creating clear, accessible policies and encouraging open discussions about data usage.Furthermore, fostering a data-driven culture requires leadership support. Encourage managers and executives to lead by example and prioritize data-driven decision-making.

By doing so, you prove that the organization’s commitment to good data governance isn’t just a ploy to comply with regulations but a fundamental aspect of how the company operates.

Integrating Data Governance with Existing IT Infrastructure

If you’ve been in business for some time, you may already have existing IT infrastructure. As such, you may be wondering how to integrate data governance into your current systems. Here’s how:

Audit your current IT infrastructure

Start by checking the data processing applications that you’re currently using as well as the data sources and storage systems you have. Doing so will give you an idea of where you stand and how they can help you implement your data governance strategy.

Identify gaps and areas for improvement

Once you have audited your IT infrastructure, identify any gaps or areas for improvement. Look for potential vulnerabilities where your current systems may fail to meet regulatory compliance requirements and other business needs. This could include outdated software, lack of data security measures, or inefficient data storage methods.

Assess compatibility with data governance tools

Next, evaluate the compatibility of your existing IT infrastructure with data governance tools and solutions. Determine if your current systems can integrate with data governance platforms or if you need to make any adjustments or upgrades for seamless integration.

Develop a data integration plan

Create a plan for how data from different systems will be integrated. You may need to use middleware or data integration platforms to aid the exchange of data between your existing IT infrastructure and the data governance tools. This plan should outline the steps and processes for data extraction, transformation, and loading into the data governance platform.

Research different data governance tools

Look for the best tool that works with your current IT infrastructure while meeting your data governance needs. There are various data governance tools available in the market, such as Collibra, Informatica, and IBM InfoSphere. Research and evaluate these tools based on factors like functionality, scalability, ease of use, and cost.

Standardize data formats

Once you’ve chosen a data governance tool, you can standardize formats through data transformation processes to match legacy system data with newer ones. By doing so, you start the implementation of your data strategy correctly.

Automate data governance processes

Reduce the manual effort required for data governance by automating processes wherever possible. This can include automating data quality checks, compliance monitoring, and reporting.

Data Governance Metrics and KPIs

To know whether your data governance efforts are leading you closer to your overall business goals, you should know the metrics and KPIs to track:

Data quality score

This metric involves measuring the accuracy, completeness, and consistency of the data in your systems. It helps you determine the overall health of your data and whether it meets the required standards.

Accessibility rate

This metric checks how easily users can access data. It gives you an idea of how quickly and efficiently users can retrieve the information they need for analysis or decision-making.

User satisfaction rate

User satisfaction rate focuses on your team’s sentiments toward the data governance processes and tools in place. It helps gauge how well the data governance efforts are meeting the needs and expectations of the users.

Data compliance score

The data compliance score is a KPI that evaluates how well the policies and procedures in the data governance strategy align with regulatory requirements. It involves checking factors like data privacy, data security, and data retention.

Issue resolution time

This metric measures how long it takes to address data governance issues. It helps you understand the efficiency of your processes and the effectiveness of your data stewardship team in addressing and resolving these problems.

Number of data breaches

Tracking the frequency of data breaches is crucial in evaluating the effectiveness of your data governance strategy. This KPI measures how many times unauthorized access or exposure of sensitive data occurs. Monitoring it helps you identify vulnerabilities and take proactive steps to prevent future breaches.

Data storage efficiency

This metric measures how well your organization maximizes its data storage resources. It checks factors such as data compression, deduplication, and storage utilization to determine if you've optimized data storage infrastructure.

Data governance ROI

Tracking the return on investment (ROI) of your data governance efforts shows the value it brings to your organization. This metric measures the financial benefits of improved data quality and increased operational efficiency.

The Future of Data Governance and Strategy

Developing and implementing a data governance strategy can help you leverage the full potential of your data assets. It's not just about managing and monitoring data. Rather, it's about turning it into a strategic asset that drives your company forward.