Data Stewardship

Data is the lifeblood of companies at the leading edge. As your data uses and needs grow, there is a critical need for responsible management and governance. Data stewardship answers that need. It embodies the principles and practices to ensure ethical, secure, and effective data handling throughout its lifecycle. I will share my experience, the components, challenges, and best practices for data stewardship.

You will find several articles on data stewardship when you research modern data management strategies. Data stewardship results from guidelines created by an organization on how to clean, use, and store their data properly. For an organization to embrace its data culture, there must be trust in the integrity and reliability of the data, which is the core of data stewardship. The other end of data stewardship is confidentiality and the ability to work your data in a very strict era of regulations and possible outside exposure. I have worked with data involved in HIPAA and CCPA, and the consequences of not following the appropriate guidelines can be expensive. I have read articles where phone numbers were incorrectly mapped in a database, and a child received thousands of text messages. Each of these violations is hundreds of dollars. Compliance is a non-negotiable, as legal risks and fines can destroy your company.

Key Components of Data Stewardship

Governance Framework: Your company must establish a robust governance framework with defined roles, responsibilities, and accountability structures. This framework is fundamental to effective data stewardship. It describes the overarching policies, standards, and procedures governing an organization's data usage, access, and protection.

Data Quality Management: Data stewardship entails implementing processes and mechanisms for maintaining data quality throughout its lifecycle. This involves data profiling, cleansing, validation, and enrichment activities to ensure data assets' accuracy, completeness, and consistency.

Metadata Management: Metadata is the descriptive information about data elements. I can't share enough how vital metadata is for efficiently using your data. I wanted to run and build early in my career and had no desire to slow down and document. This process of building and not taking the time can leave your company in a rough place in the future if your developers leave you. Your analysts also have nowhere to go to understand data context, lineage, and usage. Data stewardship initiatives focus on comprehensive metadata management strategies, facilitating data discovery, integration, and governance across disparate systems and platforms.

Data Security and Privacy: Safeguarding sensitive data against unauthorized access, breaches, and misuse is one of the primary drivers for creating data stewardship. This involves implementing robust security controls, encryption mechanisms, access management policies, and privacy safeguards to uphold data confidentiality and compliance with regulatory mandates.

Data Lifecycle Management: Data stewardship entails managing the entire data lifecycle, from the time you first load a new data set to when you archive your old data. Your company must define data retention policies, storage strategies, and archival mechanisms aligned with business requirements, regulatory obligations, and ethical considerations.

Challenges in Data Stewardship

Data Proliferation and Complexity: As your company grows, you will need new data to help increase the accuracy of your models. There is an exponential growth of data volume. You also begin to see diverse data sources and formats that are challenging for your data stewardship efforts. Managing disparate data sets scattered across on-premises and cloud environments requires sophisticated tools and strategies to ensure coherence and consistency. I am a proponent of Microsoft products because the look and feel are the same across all their applications. Microsoft offers a lot of the tools in Fabric.

Regulatory Compliance Burden: Compliance with evolving data protection regulations adds complexity to data stewardship initiatives, requiring organizations to adapt their practices continuously to meet stringent legal requirements. Navigating the intricacies of GDPR, CCPA, HIPAA, and other regulatory frameworks demands dedicated resources and expertise.

Data Governance Silos: If you have read my other blog posts, you know of my disdain for companies that fall into data silos. Different departments want to play "hero ball" or isolate data sources so their analysis can't be validated. Fragmented data governance structures come from this. Another issue is disparate systems across the company. Suppose you can't relate two data sets together for a few minutes. This inability is a sign of improper data stewardship. Overcoming organizational barriers and fostering cross-functional collaboration is essential for harmonizing data management practices and maximizing the value of data assets.

Data Privacy Concerns: Heightened concerns surrounding data privacy and consumer rights necessitate proactive measures to safeguard personal information and mitigate privacy risks. Building transparent data privacy frameworks, obtaining explicit consent for data processing activities, and implementing anonymization techniques are critical aspects of responsible data stewardship.

Best Practices in Data Stewardship

Establish Clear Governance Structures: Define clear roles, responsibilities, and decision-making authority within a centralized data governance framework to ensure alignment with organizational objectives and regulatory requirements. Small and medium businesses usually don't have the budget to hire a Chief Data Officer. This role can be critical for proper data stewardship, and large companies need to make this position a must-have. They understand the IT needs for security and regulation but also understand the business end of complexity and cleanliness. Small and medium companies can create a Council of Excellence (CoE) to spread these responsibilities among the people who are senior enough to make decisions but understand the needs and usage of your data.

Prioritize Data Quality: Implement data quality management processes, automated data validation checks, and data profiling tools to maintain high standards of data accuracy, completeness, and consistency. If you invest in a reporting tool, take the time to invest in a quality check report. Every morning, I get up, make coffee, and check out my "EDP Health Check" (Enterprise Data Platform) to ensure the data looks good. Were our sales where we expected them? Are the basic counts for key tables in an appropriate range? Did yesterday's KPIs pass the sniff test?

Embrace Metadata Management: Invest in robust metadata management tools and practices to capture comprehensive metadata attributes, facilitate data lineage tracking, and enhance data discoverability and usability. Microsoft and many other companies offer these tools, but if you are not ready to make that leap, get an Excel document and make lists. SQL databases have code so you can pull table names and dates of creation. Pull those out, assign them a primary owner, and list their purpose. This process can feel like busy work, but it is vital for your company as it protects you from turnover disasters.

Strengthen Security and Privacy Measures: Adopt a defense-in-depth approach to data security, incorporating encryption, access controls, identity management, and threat detection mechanisms to protect sensitive data assets from internal and external threats. The areas that I am least familiar with are security and privacy. Your IT teams are typically educated in these areas. However, including a legal council on your CoE will save you time, as you will eventually have to ask them, "Can we use this data for that purpose?"

Foster Data Literacy and Awareness: Promote data literacy initiatives and training programs to empower employees with the knowledge and skills to make informed decisions and adhere to data governance policies and best practices. In my other blog, I talk about the different meetings and groups I have found beneficial in supporting this. Don't be afraid to hold quarterly or bi-annual meetings on SQL or Power BI training. Anytime I have done these, I get more people emailing me than I can handle about what to do next. And I love it! Initially, SQL and report building are scary, but a good business intelligence team can help de-mystify the process.

Embrace Agile Data Governance: Adopt agile methodologies and iterative approaches to data governance, enabling organizations to adapt quickly to changing business needs, regulatory requirements, and technological advancements. Good data can slowly die in an over-engineered process. I believe in the two-week sprint cycle, and that is short enough to allow you to be agile but long enough to prevent your team from being crushed by the weight of the ad-hoc data request monster.  The monster is real; I have seen it! It has the face of a CEO, breathes fire like a COO, has a thousand arms (one for each question) like the CMO, and will crush you with no remorse like a CFO!

Conclusion

In an increasingly data-centric landscape, data stewardship is a linchpin for ensuring responsible, ethical, and effective management of data assets. By embracing data stewardship principles and implementing robust governance structures, organizations can foster trust, integrity, and transparency in their data practices, unlocking the full potential of data-driven innovation while mitigating associated risks. As the digital ecosystem continues to evolve, the imperative for data stewardship remains steadfast, guiding organizations toward a future characterized by data-driven excellence and ethical leadership.