ERP Implementations: Four steps to ensure data readiness


Completing an enterprise-wide software implementation on time and on budget is one of the most complex endeavors faced by any company. Data readiness is a major challenge for many Liberty clients, especially on ERP programs. Too often, we see go-lives delayed or extended because general ledger transactions, or details about customers and vendors, will not load. Even worse, a go-live “finishes” but then products cannot be shipped because the dependent master data didn’t load properly, resulting in real financial impact in the form of a held truck, customer penalties, and – perhaps worst of all – a damaged reputation.

These problems originate long before that first sales order won’t post or the system even goes live. In many cases, these problems began years earlier, when that data was first entered into the legacy system. At that point, no governance procedures were put into place, and none of the stakeholders fully understood the true lifecycle of data. As a result, today’s data can be stale, dirty, and flat-out wrong.

The worst thing you can do is start your brand new, multi-million-dollar ERP system with the same crap data you’ve been living with for years. By taking four important steps – long before the official program launch, and agnostic of the software to be implemented – your company can speed up timelines and avoid costly impacts once live. These steps are data definition, data deactivation, data cleansing, and data governance.

Step 1: Data definition

Every company has key data that supports its business. Identify this key data. Let’s say you’re a consumer products and goods (CPG) company. Vendor, customer, product, and location data are likely at the top of your list. Working with your stakeholders, start by defining what that data truly means in business terms.

For example, is a customer simply an entity who buys a product? Is a customer a person or a business? Are these treated differently? What about intercompany sales? Do government customers receive special treatment in terms of flags or attribution? Document these differences and how they are reflected in your data. Additionally, take note of how you reflect a “current” customer within your data.

Finally, how does your company manage the creation and modification of data? Do you have a clearly defined process? If so, write this down because you’ll need it later.

Step 2: Data deactivation

Once you’ve defined your data and have a better understanding of how it operates in your current systems, it’s time to move to the next step.

Liberty has clients whose transactional systems range from brand new to over thirty years old. In all cases, companies struggle with keeping old, stale data inactivated. A simple exercise is to define in business terms what is an “active” piece of data. For example, an active customer may be one that has placed a sales order or has made a payment in the last 12 months. Or perhaps an active part or material is one that has had some kind of “movement” in the last 24 months. Once these rules are defined, profile your data and deactivate anything that doesn’t fit your rules.

It’s not uncommon to see up to 75% of data stale. By definition of the business rules you’ve set up, this data is no longer relevant to you, so ignore it for future clean-up steps. Once you’ve reached the ERP implementation, this deactivated data will no longer be in-scope for the data conversion process saving precious time in mock and production data conversion cycles. Most importantly, you will start your new system with the freshest, most relevant data possible.

Step 3: Data cleansing

Now that we have a curated list of active data, we can begin the next step. Your company may use the Address Line 2 field several ways, causing inconsistent customer invoicing. Maybe there was a customer classification exercise that was never finished because the summer intern left early. Marketing may have asked for a product classification project so they could stop maintaining an access database no one else seemed to understand. These are all types of data cleansing and now is the time to complete these initiatives. Data cleansing is the vital step to make your data better and truly reflect your business.

Talk to the primary consumers and stewards of your key data sets. Document the cleansing opportunities and problems caused by poor data. Create a list of initiatives and prioritize your list by low effort and high impact. Start kicking off cleansing initiatives; excellent tasks for summer interns, co-ops, and temp labor. Be sure to arm your cleansers with the proper instructions and an appropriate level of decision-making ability.

Step 4: Data governance

We now have defined, active, and clean data for your most critical data sets. This last component is arguably the most important, defining strong governance over this data. The last thing you want to do after spending weeks or months cleansing your data is to leave the business process that created the messy data in the first place unchanged. You’ll be right back to where you started in a matter of weeks.

Take the data processes you defined back in Step 1, combine this with the analysis of the problems you uncovered in Step 3, and make adjustments to your data governance processes. Here are some things to look for:

  • Legacy system data fields or data entry forms often provide “wide open” entries instead of forcing users to choose from a list of options. Lock these fields down and only let users pick the proper entries
  • Too many users have access to enter or modify data. Restrict your user base to only the folks that understand the data and therefore are less prone to mistakes
  • There is a lack of processes. Look to set up offline forms, a simple workflow tool or ticketing system, and create a new process. Make the management of your critical data a priority. The process you create here will apply to your new ERP system.

By following these four steps you’re going to give yourself a huge leg up in your ERP journey. Start readying your data for large-scale software implementation as soon as possible to avoid costly delays or complete program failure. If your organization is about to begin an ERP transformation and you have questions about data readiness, reach out to Liberty. We can drive data readiness and governance within your organization. We can also help to assess your company’s data governance maturity and target areas for improvement and optimization to allow you to get the most out of your data, shifting it from a burden to the competitive advantage that it should be.

To learn how Liberty can support your ERP and data readiness efforts, contact Dave Leopold, Principal at Liberty Advisor Group ( ).

About Liberty Advisor Group

Liberty Advisor Group is a goal-oriented, client-focused, and results-driven consulting firm. We are a lean, hand-picked team of strategists, technologists, and entrepreneurs – battle-tested experts with a steadfast, start-up attitude. A team with an average experience of 15+ years, that has delivered over $1 billion in operating income improvement and over 300 M&A deals for our clients. Liberty has a proven track record in Business and Technology Strategy, Transformation and Assurance, Data Analytics, Business Threat Intelligence, and Mergers and Acquisitions. We collaborate, integrate, and ideate in real-time with our clients to deliver situation-specific solutions that work.


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