Why dirty data can stop quote-to-cash processes in their tracks
Modern quote-to-cash processes must be based on a data-first approach. Get insights on the dirty data challenge from MGI Research and DigitalRoute.
Author Stephen Hateley
Why dirty data can stop quote-to-cash processes in their tracks
It’s been 15 years since mathematician Clive Humby uttered those famous words: “Data is the new oil. It’s valuable, but if unrefined it cannot really be used.”
Fast forward to today, and a number of businesses have made uneven progress towards that vision.
Many organizations have built a considerable data infrastructure primed to gather, analyze and leverage vast volumes of operational data. But few have matched that progress in terms of data hygiene in their quote-to-cash process.
Whether it’s a sports car, an athlete or a space shuttle (or indeed, a usage-based subscription model), fuel dictates performance. You can’t win a marathon with cheeseburgers. You can’t reach the moon with diesel. And you can’t optimize your quote-to-cash process with dirty data.
I recently spoke to Igor Stenmark of MGI Research during a webinar specifically about solving the dirty data challenge in your quote-to-cash process.
What do businesses want?
As Igor said early on, there are big wins on the table for the companies that can leverage the data at their fingertips. You could:
- Support a reduction in revenue leakage
- Consolidate financial systems
- Accelerate processes (such as billing cycles) via automation
- Increase auditing transparency
- Plus a whole bunch more.
But there’s a key requirement to realizing any of these outcomes that goes beyond technology: they all need quality data.
Premature enthusiasm means businesses run before they can walk. Few are ready to structure, format, summarize and clean the massive amounts of data needed to support a business as it moves forwards and innovates its quote-to-cash processes.
(For an even more in-depth look at tackling data in the move towards usage-based monetization, you can read our comprehensive guide here.)
The first step in modernizing your quote-to-cash infrastructure is to build a sturdy foundation of data quality – to adopt processes and technologies that identify dirty data and clean it up for use at scale.
But what do we mean by dirty data?
Dirty data is a foundational obstacle for businesses looking for real change in their processes, from improved visibility and speed through automation to offering new services. But the term is still a little vague for some. This is explored in comprehensive detail within the webinar itself, but to summarize, data can be “dirty”, or “crude” in four different ways:
- Timeliness: How fast is your data? Is it static, or outdated?
- Content: Is everything needed available? Is there enough depth to the data? Has it been contaminated by another data stream?
- Syntax and semantics: Have you got a uniform structure and format to the data you’re looking to use?
- Management: Is the data compliant? Is it dependent on particular applications rather than being universal? Are you only dealing with data that won’t land you in compliance hot water?
Companies face these data problems because of an imbalance in their investment. Many will pour resources into tools and expert staff, but far less into ensuring their data is high quality, rich and accurate. As Igor says, using unsanitized data is like putting a “fuel of unknown origin and/or uncertain quality” into a vehicle and hoping it’ll run.
The impact of crude data
Crude data has big costs. We’ve written blogs on the issues caused by poor data quality, namely revenue leakage, incorrect invoicing and a subpar customer experience.
Likewise, independent and strategic advisory firm MGI Research have studied subscription businesses and the impact of data on monetization initiatives extensively.
In this webinar, we take a deep dive into MGI Research’s work, to hear about the impact of data quality on revenue operations first-hand. And given over 70% of organizations cite data challenges as one of the key causes of billing project failure, it’s something you won’t want to miss.
The issues could not be starker, since more than 70% of businesses have to manually clean their data to make it usable.
Plotting a course towards sparkling clean data
Crude data impacts data initiatives across the whole organization, but it’s particularly disruptive to key monetization processes, like quote-to-cash.
We attack this problem head on in our webinar, Solving the dirty data challenge in your quote-to-cash process – with MGI Research. There is a wealth of insight from Igor that digs deep into the drivers, capabilities and options for cutting-edge data refinement, as well as some case studies that I simply couldn’t sum up in a single blog.
- The costs of inconsistent, incomplete data in billing systems
- How dirty data impacts an organization’s ability to launch new services
- Effective strategies for improving data hygiene in monetization processes
- What different types of data processing solutions to use and when
- Examples of companies that have successfully transformed quote-to-cash processes.
If you’d like to dig deeper you can also download MGI Research’s report, Mediation 2.0: Taking on the Data Challenge in Agile Billing to explore:
- The key challenges in managing a data ecosystem for monetization
- Effective strategies for improving data hygiene in monetization
- What Mediation 2.0 is and why it matters.
For any other questions, please get in touch through our contact page.
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