23 Tips for Enterprise Data Quality Success, Featuring Jay Zaidi
Enterprise Data Quality Management – this is the ultimate data quality challenge but where do you start to manage such a vast, complex initiative?
Jay Zaidi of Fannie Mae was recently interviewed as he and his team have helped to deliver a successful Enterprise Data Quality initiative in one of the largest organisations in the world.
This post provides some key tips and lessons learned from that interview.
Skills for Launching an Enterprise Data Quality Initiative
There are multiple skills that are required to initiate or launch such projects at the enterprise level, here are some of the most critical:
Influencing skills: to sell the business case and benefits of the program to Senior Management and gain sponsorship for the program
Negotiation skills: to negotiate the best deals from vendors and service providers
Interpersonal skills: to foster and develop a data quality mind set within the enterprise
Execution skills: to ensure adoption and timely delivery of the platform capabilities
Action Points: Where do you need to improve your skills in these areas? Influence by Robert Cialdini is a classic in this field.
Centralising Expertise and Leveraging Re-Use
Create a DQ CoE: Use a small core team in a Data Quality Centre of Excellence to identify patterns across multiple lines of business so that data quality solutions can become portable and heavily re-used.
Repeatable Processes: Creating repeatable processes results in streamlined operation and reduced time-to-market lead times.
Re-Use Technology: Create re-usable rule sets, web services, data validation, data standardization, data cleansing and normalization components, based on generic data quality requirements
Leverage one-source validation: Create generic web services to vend to various applications, you can deliver a considerable amount of data quality management capability, without having to build or change a multitude of different applications and interfaces
Action Points: How is your organisation identifying areas for re-use? Are there common standards, policies, rules, datasets, procedures for data quality that can be leveraged elsewhere in the organisation?
Selecting a COTS Enterprise Data Quality Product
Do your due diligence prior to initiating the actual program itself
Select the right product is an art form, requires prior experience in evaluating COTS products and the ability to ask the right questions
Capture all the data quality use cases within the enterprise
Understand the data quality challenges faced by the business, operations and technology teams
Develop the evaluation criteria and the logical architecture for the data quality platform
Try and establish an ability to access EDQ-related metadata, EDQ results, EDQ metrics, transaction and exception logs and other relevant data, stored in the tool’s data repositories, many COTS products have this kind of information trapped within a proprietary database, so be sure you can get at it easily
Action Points: Don’t select a data quality based on an industry analyst review alone, benchmark the technology based on your actual needs and your data.
Engaging With The Business and Getting Buy-In
Don’t assume that business users are as savvy and educated about the various tools and technologies as we are, not always the case
Educating business users on the value of data quality to their organization and training them in the tool set is important
A lack of education and tool training can often result in user frustration, which if left unchecked can have a negative impact on the program and ultimately result in its failure
Constantly monitoring feedback from end-users and proactively addressing issues is important
Risk Management, Governance, Compliance, Audit and Finance organizations have a vested interest in supporting the program, suggest targeting these organizations early in the process
The ability to automate data quality monitoring and scorecarding activities, share and re-use data quality rules across the business unit, deploy generic data quality services and proactively manage data quality typically resonate with the user community, especially the business teams
The EDQ data quality platform should be bundled across the organisation with consulting, technical support and best practices documentation
Your Data Quality Centre of Excellence should provide documentation templates, design patterns and development frameworks, to reduce time-to-market
To reduce barriers to adoption the EDQ platform should bundle services and best practices to business units free of charge, helps make a more attractive proposition
Action Points:
Nothing kills your EDQ initiative faster than “dumping” data quality on an unsuspecting business unit. Are you measuring the user satisfaction with training, education and technical support? Don’t become an extra administrative chore for business leaders, make the transition as seamless as possible by creating wikis, how-to guides and excellent training modules.