Looking for a data quality strategy or framework to copy? Let this post help you create something a little more bespoke for your data quality needs.
How to Create a Data Quality Strategy Without Cookie-Cutting
by Dylan Jones, Editor
With so many books, articles and case studies available on the subject of data quality strategy it is easy to to “cookie-cut” past strategies or frameworks and adopt them as your own.
In this article, I want to explain why this is not necessarily the best approach and what you can do to create the perfect strategy for attaining data quality goals specific to your organisation.
Searching for the Perfect Data Quality Strategy
I routinely monitor the inbound search engine keywords for Data Quality Pro and it’s clear that many people are looking for past data quality strategy documents to help shape their own projects.
I also get occasional requests for project plans, strategy documents and other materials from people who are trying to shape future data quality strategies for their own projects.
One of the first interviews we published on Data Quality Pro was with Mandy Mackay who was then at the New Zealand Ministry of Justice.The interview also included the data quality framework that she had helped develop at the ministry and it became an incredibly popular download on the site for many months, still is in fact.
Clearly, there was a lot of value in this document and much of it could be adopted in other sectors, particularly local government.
However, documents like these are often very specific to that particular organisation and the unique data quality challenges they face.
Having worked both in consulting firms and on behalf of clients who are reviewing tenders from prospective consultancies I’ve often seen this issue. The consultancy creates their standard framework for data quality management and puts it forward as a showpiece on any new client proposals. The client, witnessing the depth and completeness of the framework may in fact grow confident that this must be the partner of choice for them but again, this is not a data quality strategy. This is typically a framework or blueprint for a generic process, not the strategy that is unique to that clients specific problem.
Data Quality Tactics – Where to Start?
The best way to explain this is with an analogy of war.
Consider your data quality project as though you’re fighting a war. The resources and processes found within your Navy, Army, Air force, medical units, administration divisions and all the additional governance that goes into running an army can be seen as your data quality framework.
This is very different to your strategy for winning a war.
A war strategy has to answer many questions:
- What is the desired outcome – how will we know we have won?
- What are the plans for action?
- How do these plans connect together and do we have any fallback plans?
- What is the timeline for each plan?
A military leader would be unwise to enter into a war simply using the same strategy they have adopted in previous campaigns. Each situation requires a unique perspective.
There is a similar danger here of simply adopting a data quality framework from a renowned expert and converting this into your data quality strategy.
To your peers and seniors a published framework may look detailed and thorough but is it right for your organisation?
Having read practically all the leading data quality books, and indeed spoke with many of the authors, it’s interesting just how much their opinions and data quality strategies differ.
Can You Really “Paint by Numbers” for Data Quality?
No. There is no “paint-by-numbers” strategy for data quality.
In fact, many data quality project strategies are doomed to failure before they even begin for this very reason because they are trying to “paint by numbers” when they don’t know what the numbers are yet!
How to Create a Bespoke Data Quality Strategy or “How to Navigate Through Data Quality Fog”
Central to the book is the premise that projects, IT projects in particular, will fail if they don’t apply the correct project strategy to the type of change being implemented.
Most project strategies that I witness in terms of data quality have followed a fairly linear path. “We’ll do A, then B, then C, then D, which will of course cost no more than X, delivering benefits Y and will delight sponsor Z “.
The fact is that for most organisations, unless you’re way up the information quality maturity scale, this linear strategy is a recipe for disaster because you lack the experience of delivering these projects.
Eddie Obeng actually creates a formula for project success that may help you benchmark your own project:
Change with the right PURPOSE, matched to the PROJECT TYPE, kept on track through effective LEARNING and REVIEW, set up with good PLANNING and COORDINATION, plus effective STAKEHOLDER MANAGEMENT, shared amongst the TEAM, multiplied by (your) effective LEADERSHIP, equals SUCCESS.
– Eddie Obeng, Perfect Projects
In the book, Eddie Obeng walks through each of the highlighted issues but the one most companies fall down on is adopting the wrong strategy for the PROJECT TYPE they are involved in.
Obeng states that projects are simply “chunks” of change but these chunks differ depending on the level of learning and experience we possess at the start of the project.
In a perfect scenario, according to Obeng, we would know:
Why we are doing what we are doing, what precisely the outcome will be, and understand how it is to be carried out – including the methodologies and technologies required.
Obeng points out that most projects fail this criteria and instead fall under the following categories:
- Clear goal – missing method
- Clear goal and method – lack of clarity on outcome
- No goal and no method and project exists because “something must be done”
We now have 4 different types of change which Obeng classifies as:
- Paint by numbers
So where most organisations are focusing on a paint by numbers strategy, they should instead by adopting a quest, movie or fog strategy because they have not yet developed the required learning and experience.
In many cases I believe organisations should be adopting a fog based strategy because they don’t truly understand the direction and method required. Even if the method has come from a “guru”, if it isn’t based on learning and experience from their particular situation it may not be suitable.
They’re typically reacting to a perceived need because “something must be done” about the data quality issues they are facing and the true vision and goals of the project are often poorly understood as a result.
The approach for a fog style project is to move forward in cycles of learning and review, much as you would do if lost in the fog.
You must inch forward testing the terrain and reviewing your strategy based on what the conditions are telling you.At each step you develop the learning and knowledge required to help you plan the follow-on phase.
In short, you are creating an iterative strategy that is far more likely to guide you to a successful outcome than a paint by numbers approach that can get you hopelessly lost because you’re following the wrong method or the goal lacks clarity.
With a fog approach you are constantly asking the question – what is the true goal and outcome we require and what specific methods (based on our recent discoveries) will get us there?
(For detailed instructions on how to manage fog style projects and the other styles mentioned above then obviously purchase the Perfect Projects book, it’s a sound investment).
I’m sure if you presented a business case to your sponsor that likened your strategy to wandering around in the fog you would soon be ushered/thrown out of the room. However, in many cases this really is the best approach for companies who are starting out with data quality.
Yes, there may be fear and apprehension from the sponsors because you lack a complete set of steps from “cradle to grave” so in this situation you need to explain that this approach is likely to deliver far less risk and could in fact save money and time through a more pragmatic, staged approach where the strategy evolves and adapts based on what you discover along the journey.
How to Create a Data Quality Strategy – Your Next Steps
When you next come to build your strategy for a data quality initiative, take a step back and ask some key questions:
- Do you really know the precise strategy at this stage?
- Are all the steps laid out perfectly before you so you can connect the dots and deliver a complete vision?
- Do you know exactly what you are doing, why and how every single task has to be carried out?
- How are you defining the methods for the project? Is your methodology adapted from frameworks in published texts or based on your learning and experience of the present situation?
- What are the real outcomes for the project? Are they known? Have they been validated?
Answering these type of questions will help you define the type of project you’re involved in. If it’s a paint by numbers style project, fine, create a linear strategy and go for it. However, if there is uncertainty and confusion on goals, method and outcomes then look at the alternative approaches provided above.
Only when you understand the type of project required can you start to develop the right data quality strategy for moving forward.
What do you think? A clear path forward or still stuck in the fog?
Data Quality Strategy Resources
You may find the following of value for building a Data Quality Strategy
Pentacle Website (they now publish other books by Eddie Obeng for free on their website, well worth a read)