How does data governance affect your customers?

This has been the cornerstone of my work so far. Inaccurate or incomplete data is rife at any organisation, so fixing even the smallest issue can seem like magic to those using it. If you can save your colleagues time on manipulating spreadsheets or hand-entering data, they will trust you to tackle their bigger or more systemic issues.

Data governance impacts my customers’ business operations directly and indirectly. Some notable ones: 1. Data quality: Enhanced data quality means fewer functional issues, clearer understanding of data and how it enables the business, lesser time in fixing low quality data pipelines 2. Compliance/ Regulatory: industries where compliance and regulation is paramount depend hugely on data governance, else they end up paying huge fines/ loss of customer confidence 3. Explainability and reduction of technical debt: the better the data tagging/ metadata management and data lineage, the clearer the overall understanding of what data is needed, what’s not, what needs to be secured, and what can be sunsetted.

Let it be represented by a number of diverse team members . Customers, technically involved and engaged employees. They will unearth through design thinking ideas and solutions that would normally be missed or not looked into with much depth.

Outdated or erroneous records frustrate customers when we can't reach them or send irrelevant communications. But complete, accurate profiles let us deliver the personalised, timely interactions customers want. My advice is to make data quality an ongoing priority and process. Assess critical data fields and metrics like accuracy, completeness and timeliness. Implement data cleaning and monitoring to uphold standards. Provide training and tools to staff entering data to get it right from the start. Seek customer feedback to catch quality issues. Link data governance to business goals, with executive sponsorship. This transforms data from a liability into an asset for serving customers.

Breaches erode trust and loyalty, destroying the relationship. But robust security preserves confidentiality and demonstrates our commitment to protecting sensitive information. I advise a multi-layered strategy. Access controls limit data to authorised users. Encryption protects in-transit and at-rest data. Security monitoring quickly detects and blocks threats. Staff training establishes security habits and awareness. Regular audits identify vulnerabilities to address. While threats persist, vigilance, governance and the right tools can lock down data and maintain customer confidence. By upholding data protection as a top priority, we show customers their partnership is valued.

Being open in sharing data with other stakeholders is one example I have seen with great success if regulation allows it. By doing this, you actively contribute to a growing ecosystem and foster relationships.

When information is fragmented across systems, it's challenging to retrieve and share for coordinated service. My advice is to pursue integration. Migrate data to a centralised repository, data lake, or cloud data warehouse. Implement data virtualisation layers to unify distributed sources. Apply Master Data Management to maintain consistency. Build APIs and data pipelines to connect systems. With established accessibility and governance, teams can readily pull comprehensive customer data for analysis and action. Quick, integrated access empowers employees to deliver informed, seamless experiences. Data should enlighten and enhance interactions, not obstruct them.

I have always taken into account the expertise on industry professionals by implementing data structure. Not only does it save time in the overall process, but it also enables other use cases to possibly benefit from it in the future. This may even have an indirect Return on Investment if executed properly.

Information that lacks context or actionability wastes potential. But tightly aligning data models, structures and metrics to strategic priorities maximises usability. Maintain continuous collaboration with stakeholders. Identify key business objectives, questions and decisions. Design data systems to directly inform those needs. Use business glossaries and data dictionaries to standardise terminology. Employ agile practices to frequently gather feedback and validate direction. With a focus on relevance and actionability, data becomes an engine for generating customer and organisational insights.

Rigorous governance framework ensures adherence to critical regulations like GDPR and CCPA, avoiding those pitfalls. In my experience, take a proactive stance. Maintain a regularly updated inventory of compliance obligations. Automate policies through data tools. Train staff thoroughly on requirements. Perform audits and risk assessments to identify and address gaps. Stay current on emerging regulations. Document your compliance program and due diligence. While complex, compliance is not optional. Through vigilance, transparency and the right systems, we can operate securely and ethically while earning customer trust.