As we increase our level of pricing maturity at ARDEX Americas, we are also conducting advanced pricing optimization and analytics experiments. As a champion of this unique experimental activity, I realized that preparing and extracting data for analysis was a much more difficult process than I imagined. This process required thinking, coordination and organizational commitment. From a practitioner’s perspective, then, there are critical parameters of data preparation and extraction that have to be conceptualized to ensure successful data analytics. Some of the potential barriers to data preparation and extraction are presented in the figure below:

From a practitioner’s perspective, barriers to pricing optimization start very early in the data preparation and extraction steps. While it seems obvious for pricing software and consulting companies, the process creates both technical and behavioral challenges for practitioners.

Barriers related to data quality and systems complexity are to be expected. For example, daily manipulation at order entry, manual modifications in master data files and manual accounting entries to address incorrect transactions will create issues with the overall reliability of data. The data may be incomplete, unstructured and inconsistent. Issues with systems are equally problematic. The use of multiple ERP systems, large-scale upgrade projects and the dependence on old outdated systems to extract historical data, can lead to breakdowns in data integration and consolidation.  These are to be expected in any pricing analytics and optimization project.

Other barriers to data preparation and extraction are more organizational and behavioral in nature. First, it is critical to obtain commitment from the internal department involved in the project to generate commitment and support for the overall project. That requires detailed explanation of the project scope, and the presentation of why this is done and within what context. Explaining the data optimization program within the context of the overall corporate vision is a critical step toward project buy-in. The project scope definition is also important to make the right type and amount of data are extracted. The goal is to define the data scope right the first time to make sure multiple extractions are not needed.

Another important barrier to this process is the potential capabilities issues related to data cleaning, preparation and extraction. Many organizations haven’t conducted large-scale data preparation projects out of their ERP systems. Training and demonstrations might be required to improve the comfort level with the process.

From a practical perspective, we have found that the timing of the data extraction project plays an important role. Asking the accounting or finance team to spend one full day on this process in the middle of the budgeting season or at month’s end might not be the best idea. It appears obvious, but it’s one of these smart things to consider.

Finally, issues of confidentiality and data security must be considered. At ARDEX Americas, we have had many discussions on the topic of secured data transfer, data encryption and data management post-project completion. Consultants and software companies have to take these considerations more seriously by proactively providing upfront all the proper reassurance on both dimensions. Information related to sales, pricing and costs represents the livelihood of an organization. Simply asking to extract and transfer the data without consideration for security and confidentiality won’t work.

Based on our experience, we recommend the following simple actions or steps:

1. Create a multi-functional team for data preparation and extraction. Conduct a kick- off meeting and explain the vision, the purpose and the clear scope of analysis. Create a common vision for the project, and reassure the team from the very beginning that data will be secured and treated with high level of confidentiality.
2. Conduct a data audit to evaluate potential technical barriers and issues related to data quality and systems.  Map out where data might come from, along with possible interface issues. Link the project purpose to the project outcome, and create a road map on how to get the best and cleanest data.
3. Select the proper analytics experts to address and treat all possible discrepancies through advanced statistical analysis. Do not improvise on the manipulation and treatment of data as this might extend the project schedule, and, in the long run, create more problems.
4. Involve the team by creating a taskforce to support the project. Create transparencies on issues and solutions without finger pointing or breaking the “data kingdom.” Remember that information is power. Having everyone on board with a top executive champion might be the most powerful combination to ensure project support.
5. Get it right the first time. Garbage in garbage out! Avoid multiple iterations of extraction and data file versions that might confuse everyone. The point is to keep the project simple, but highly structured throughout its lifetime. Make proper use of consultants for this step in the overall pricing analytics process. Spending more time preparing and extracting the right data file will make the back end of the process faster and more robust.

For most of you, the thoughts in this paper might seem obvious and too simplistic. From a practitioner’s perspective, it is not as easy as you think. Organizational complexity with people and systems might slow down or derail the project. We have experienced it, and we have learned a great deal from it. Working with The Pricing Cloud team helped us get on the right path.

Stephan Liozu (www.stephanliozu.com)  is President & CEO of Ardex Americas, an innovative and high-performance building-materials mid-sized company located in Pittsburgh, PA. He is also a PhD candidate in Management at Case Western Reserve University and can be reached at sliozu@case.edu.

Vernon Lennon is CEO & Founder of Pricing Cloud (www.pricingcloud.com,  a consulting boutique dedicated to “pricing made actionable” for mid-sized companies. He can be reached at vernon.lennon@pricingcloud.com.