Wednesday, 5 May 2010

How are you Executing your Data Quality Strategy?

There has been lots of talk about Data Quality Strategy - the framework, and roadmap we will use to implement data quality and governance measures within an organisation - and the criteria that defines success. Within our Data Quality Strategy, we laid down our goals, our objectives and our success criteria. We know where we want to be, and we know how to judge whether we have succeeded to get there or not.

But how do we get there?

Defining the strategy is only the first step. The hard work is in its execution. Many well devised and well meaning strategies come undone due to poor execution. I recently watched a great short podcast from London Business School about Strategy Execution, or as they put it 'getting things done'. The professor, Donald Sull, suggested that Strategy Execution can be broadly placed into three buckets:
  • Executing by Power
  • Executing by Process
  • Executing by Promise
Lets take a quick look at an example of each.

Executing by Power

Our Data Quality Strategy states the need to profile enterprise data, and through the utilisation of our data quality profiling tool we will identify rogue elements and improve the quality of the data.

Meanwhile, the issue of poor quality customer address data has been raised to a C-Level audience, with the impacts of poor data quality known to be costing the company around £250,000 per annum in returned mail processing.

Now that the C-Level audience is aware of the issue, and the cost to the business, they are extremely keen to see the data issues resolved. They see it as the responsibility of your team, and deem it to be your number 1 priority to resolve.

Executing by Process

Our Data Quality Strategy states the need to ensure that all data sources have owners, that we have understanding of Data Lineage, and that Data Retention Policy is agreed and adhered to.

Meanwhile, there are a number of Legal Regulations that impact our organisation, and we must ensure continual compliance. As part of this process there is a need to ensure that the flow of data is understood throughout the journey from system to system, and that ownership and single points of contact are in place.

By aligning our strategic objectives such as those mentioned above to the process of achieving Regulatory Compliance we can ensure that they are executed effectively.

Executing by Promise

Our Data Quality Strategy states that we will create a scorecard to measure the quality of data within our organisation. We will then embark upon a continuous improvement exercise, focussing primarily on the key 'weak' areas as identified by our measurements.

Meanwhile, the business community have a number of data quality issues that they need assistance with. They don't know the true cost of the issues so haven't been able to escalate it to a C-Level audience. They are however keen to have all issues documented, and resolved.

At this stage, we know what we'd like - good quality, and fit for purpose data - but we're not sure exactly how we're going to get it. By making a commitment to the business to improve quality of data, we are handing ourselves accountability to get the task completed. This promise allows us to be flexible in our approach but still maintain high standards of service & delivery to aid our reputation.

Which is the best method?

I'd suggest that any Data Quality Strategy will utilise a number of different execution approaches in order to achieve strategic objectives.

As well as having it's strengths, each execution approach has it's own weaknesses, be it the potential of becoming silo'd, the lack of flexibility, or even the potential to dampen peoples initiative. Your task is to select the best method of execution for each objective within your strategic framework.

In essence, ask yourself: How am I going to successfully implement initiatives stated within our strategy?

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