In this second article in the series on tips for a data quality business case, I’m going to explain the importance of creating a “Lean Pilot” and “Roadshow” for getting senior stakeholder buy-in.
Note: You can read part one in the series here: “How can buyer triggers impact your data quality business case?“.
Building a business case for data quality can be a challenging undertaking, particularly if it’s your first attempt.
Sometimes it feels like you’re pitching on Dragon’s Den (think Sharktank if you’re overseas) to get your data quality initiative off the ground.
You present a great story about how data quality is good for profits, customer loyalty, productivity and any number of other measures, but you just can’t quite sway the senior stakeholders into committing the budget.
So what’s the problem?
One of the big issues I see is that decision-makers are pitched new initiatives all the time. These projects also have equally passionate evangelists driving them, so you are pitching in a competitive environment. What’s more, a lot of these other initiatives can sound a lot more valuable (and exciting!) to executives who are sceptical about “quality drives”.
In my last post, I discussed the importance of a “Trigger Discovery Process“ to uncover the personal and corporate drivers of potential sponsors.
But what else can you do to shift the balance in your favour?
From experience, you need to create a data quality “show and tell” session. You need to cut through the confusion and mistrust that often pervades the topic of data quality and demonstrate, in clear terms, just how effective your data quality program will be.
For this, I recommend the “Lean Pilot and Roadshow” approach.
What is a Lean Data Q/uk/resources/product-brochures/experian-pandora-for-data-quality-leaders/uality Pilot?
The Lean Pilot approach to data quality is a simple concept but one I’ve seen applied many times.
The reason I like it so much is that it solves the ‘chicken and egg’ frustration that many people experience with data quality management funding: how can you demonstrate the value of data quality improvement without getting the budget to implement data quality in the first place!
The key is to create a pilot in tight delivery cycles that consume practically no capital investment at all, apart from some minor expense for the time of a handful (or less) of key staff. I find that 5-10 working days is an ideal “Sprint” duration for a Lean Pilot.
Your goal is to demonstrate the value of data quality improvement by building an ROI model to support your future data quality business case.
The way I approach the Lean Pilot is first to understand the personal motivations of potential buyers and stakeholders (which we covered in my previous post). This groundwork will help guide your focus on the initial pilot and ensure you’re improving things that sponsors care deeply about.
Once you have some idea of who your likely decision-makers are and what motivates them, you then need to get creative with your resourcing. You need to create a low-cost pilot to ensure it ‘flies under the radar’ of budgeting approval.
To deliver your Lean Pilot, you’ll need to get creative with resourcing and tools.
In the past, I’ve drawn on the help of data entry staff and other knowledge workers to resource my data quality analysis. In one firm, I offered a free training initiative during workers lunch breaks in return for some analysis and improvement tasks. The classroom was full!
In another organisation, we arranged a trial period with a data quality software vendor to create a proof of concept. This analysis allowed us to gather data and findings to support a business case that would ultimately include the selection of the software product.
I’ve interviewed aspiring data quality leaders who have leveraged interns and low-cost outsource resources, anything they could to keep the costs low for the initial pilot scheme.
The key in the Lean Pilot phase is to cycle through the data landscape with your data quality assessment finding issues that align to stakeholder motivations.
I like to back up the data quality assessment piece with an analysis of business performance so that I can spot correlations and get a view of overall trends.
- Are tasks taking longer now than they did three years ago?
- Is the cost of an engineer site visit going up?
- Will the demand for services likely increase in the next year?
By understanding the financial and operational performance within the area under investigation, you will have far greater chance to ‘talk the language of the stakeholder’ and impress upon them why your initiative requires funding.
Create Value During the Data Quality Pilot
During the pilot phase, I always aim to make some form of data quality improvement. If you don’t, then any subsequent data quality business case will be like a two-legged stool: you have the proof there is a problem and the expertise to solve it but you cant prove what the likely improvement benefits will be.
When making changes, you don’t need to boil the ocean (In most cases you lack the budget anyway!). Instead, get super-focused by examining perhaps one key aspect of an information chain, product line, customer segment or process step. Find the low-hanging fruit that you can quickly enhance and observe the improvement of external metrics such as lead time, costs, staff utilisation and other measures for that subset of data and process you’ve enhanced.
- Have you moved the needle?
- Is there a tangible benefit as a result of the improvement?
If the benefits are minimal, commit to another cycle with another segment until you’re having a visible impact.
When you can demonstrate cause, impact, resolution and benefit, you’re ready to move on to the Data Quality Roadshow stage.
Creating Your Data Quality ‘Show and Tell’ Experience
During my interviews with leading data quality practitioners, I began to notice that many of them were not content with just presenting their data quality business case using a bland slide-deck in a closed meeting room, they instead took the “show on the road”.
“One of the things we did well [on our pilot] was run roadshows demonstrating the tools and techniques we’d used. We were showing people how quickly we’d made progress because it was a very similar story in other datasets within the firm. Our roadshow showed it is possible to solve these issues and it can actually be very simple. You just need to make some small commitments in terms of time, resources and funding.”
James Phare, Managing Director, Data to Value
The idea is an obvious one; the more people that get to learn about the improvements you’ve made and the impact you’ve had, the greater chance you possess for getting investment in your data quality vision.
Just because your initial pilot was delivered with “guerilla resourcing” and a shoestring budget, it doesn’t mean you can’t showcase what you find and what others in the organisation can replicate.
I have spoken to other practitioners who have held meetups and “show and tell” sessions with a cross-section of the organisation that ultimately helped build a real community of evangelists for data quality. They were able to get buy-in and support directly as a result of these sessions.
Others have posted results in washrooms, kitchens, hallways, the company intranet and newsletters – anywhere to get some traction.
“We measure everything and communicate the results at every opportunity. We post the results of our measures (and we use trend graphs) everywhere we can, beside the printers, in the washrooms (whatever works!), in the kitchens, on our internal web site and within the wiki.”
Jill Wanless, Sr. Enterprise Information Management Advisor
Another benefit of sharing your findings with others is that you can start to replicate the process in other parts of the organisation that are experiencing similar challenges.
As James Phare found above, the same problems are endemic in most organisations so by encouraging others to solve similar issues elsewhere you create ‘corporate proof’. This allows you to build up multiple use-cases that clearly outline the benefits of your approach, making it easier to create buy-in for longer-term initiatives.
Summary and Next Steps
In my experience, it’s not the technicalities of a data quality business case that prevent buy-in. With modern technology and techniques, the data quality assessment and improvement element of a pilot is relatively straightforward.
The real challenge is aligning the right buyer motivations and demonstrating the value you bring, all in an engaging format.
Hopefully, this series has gone some way to outlining these challenges and giving you some pointers for the future.