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Data Quality Maturity Assessment

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People

People are critical to the effective roll-out and implementation of a robust data quality strategy.

Process

A structured set of activities that have a well-defined and documented outcome.

Technology

The tools employed by an organisation to help perform a specific action or activities more efficiently.

Who owns data within your organisation?

No single party owns data within our organisation. Ownership of data is solely the responsibility of individuals within business silos. Data is a jointly owned asset between the IT department and key business stakeholders. We have a single point of ownership that is responsible for a business-wide view of all data assets at board level.

Has your organisation experienced significant issues as a result of poor data quality?

We have experienced a wide range of issues that significantly impact our business, internally and/or externally. We experience some significant challenges but it is up to each department to solve them. We experience some issues, but are able to trace the root cause of any challenges that arise and come to solutions. We have not experienced or no longer experience any challenges due to poor data quality at our organization.

To what extent do data specific roles exist in your organisation outside of the IT department? E.g. Data Governance Lead, Data Steward etc.

Our organisation has no data specific roles. Our organisation has a limited number of data specific roles working in silos, supporting individual department objectives. We have some data specific roles that have responsibilities across a variety of departments to champion and deliver data-dependant projects. Our organisation has a range of data quality ‘power users’ that permeate the culture of the business aligning objectives and actions across functions.

To what extent does your business have a clear data quality strategy?

We do not have a data quality strategy in place and there is a sense of apathy towards data improvement initiatives across the business. We take an ad-hoc approach to data quality; individual departments reactively implement fixes for data quality issues as they arise. We have a data quality strategy with a range of performance measures that lets us proactively drive business-wide improvement initiatives. Our data quality strategy can clearly link the value of accurate data to financial performance and it is reported at board level.

Are data improvement initiatives championed at a senior stakeholder level?

We have no data improvement initiatives championed at a senior level due to lack of buy-in. We have limited numbers of improvement initiatives championed at a senior stakeholder level, with inconsistent reporting and communication. Most data improvement initiatives are championed at a senior stakeholder level although they often lose support in the long-term. All data improvement initiatives are championed at a senior stakeholder level with strong support for the on-going recording of results to provide actionable insights and business change.

How well do IT and business users collaborate to prioritise data quality issues?

There is no collaboration between IT and business functions when data issues arise. There is some collaboration between IT and Business users, but data issues are dealt with in departmental silos with no business-wide benefit. IT and Business users combine to tackle data quality issues frequently across multiple departments but the impact is often temporary and on-going monitoring inconsistent. Data quality issues are scoped out collaboratively between business and IT functions with clear prioritisation on the issues that matter to business performance.

How well do you understand data quality processes across the business?

Our organisation has not mapped out any data quality processes and ownership is unclear. We have mapped out a limited number of individual data quality processes within department silos in order to resolve short-term issues or deliver a bespoke use case (e.g. one-off database cleansing for a marketing campaign). We have clearly mapped the flow of data across the organisation and understand its impact on downstream line-of-business applications, with a view to driving a more consistent and collaborative approach to data improvement. Data processes are clearly documented and rigorously maintained, performance monitoring is in place as a business-as-usual activity.

How does your organisation identify issues within your data?

We have no way of uncovering the unknown issues that exist within our data as business users find workarounds rather than reporting problems. We have no way of proactively managing data quality issues and they only come to light when raised by the business on an ad-hoc basis. When issues are reported we proactively work to understand the root cause of the issue and seek to implement longer term fixes. We have clearly defined issue resolution processes in place which have become business-as-usual activities, which continue to evolve.

How would you describe the way in which issue resolution and workflow management is audited across your organisation?

Naïve and apathetic. Ad-hoc and immature. Defined and proactive. Measured and collaborative.

What consideration is given to data quality processes when purchasing new technology?

Little to no consideration – we have no plans in place to acquire technology. Limited consideration – we are often interested in fixing a data quality issue within a business silo. Broader consideration – we scope our project to be inclusive of wider business needs. Full consideration – we take the time to understand the strategic fit of new technology and how it can be utilised across the entire organisation.

Does your organisation have measures in place to report on data quality proactively?

We don’t currently measure data quality. We struggle to report on data quality proactively although can demonstrate improvement on a project by project basis. We measure the impact of data quality across multiple departments. Corporate-wide data quality is monitored as a business-as-usual activity and reported at board level.

How reliant is your organisation on manual tools and techniques when detailing out data quality issues?

We are heavily reliant on manual techniques when detailing data quality issues. We have tactical point tools within individual department applications but still rely heavily on manual analysis for issue resolution. We use data quality software for root cause analysis of issues, gaining a clear view of all enterprise-wide data assets, and put proactive measures in place to manage data quality across all applications. We take an integrated platform approach to profiling, monitoring and visualising data quality on a corporate-wide scale which highlights data quality issues that require resolution.

To what extent are you leveraging technology to manage data across its full lifecycle?

We’re not managing data across its full lifecycle as it’s not deemed strategically important by the business. We are focussed on improving data accuracy as a result of a specific issue. We analyse data in order to ensure improvement in data accuracy is measurable and demonstrable. We manage data across its full lifecycle by analysing, improving and controlling information assets over time.

When selecting technology providers how important is support for managing multiple data domains?

We are not currently prioritising investment in any data domains. We select technology on a case by case basis depending on the data domain we’re targeting to improve. We prioritise technology that can help us understand the connection between customer data and other data domains (e.g. product/financial/asset etc.) We will only buy data quality technology that is data agnostic.

To what extent are point data quality tooling decisions the norm for your organisation?

There is no current requirement for either point tools or integrated platforms. There is inconsistent adoption of point data quality tools across the organisation leading to multiple vendor relationships. We invest in a range of point tools and technology platforms and try to integrate them where possible. We take a platform approach with any discreet best-of-breed point tools integrated as tightly as possible.

When investing in data quality technology, how broadly is it used across the organisation?

We don’t use data quality technology. The technology that we deploy is for one specific department or project. The technology we deploy is for multiple departments or projects. The technology we deploy is utilised across the organisation.

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