Clean Data Plays a Role in Strategic Pricing

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Summary: Dealing with “rogue data” or “dirty data” is a reality that is universal across all industries and verticals, but particularly prevalent in distribution. Start building habits today that will result in cleaner and more valuable data in the future.

Do the countless number of inconsistently-organized customer and product data in your ERP system seem overwhelming? Some refer to this as “dirty data,” and many distributors feel they can’t move forward with a new pricing initiative until the data is fixed. Let’s talk about it.

The reality is that every company has a collection of outdated or inaccurate data that needs to be categorized, properly segmented and then managed from then onward. So, if this rings true for your company, you’re not alone and might find some relief in knowing that other businesses are in the same boat. Nobody’s perfect in this particular area.

The good news is you don’t need perfect data to improve your pricing system and find measurable success. Of course, cleaner data provides a cleaner path to success, but many companies have made major strides in pricing even with imperfect data. The goal is to make improvements and progress along the way because both managing data and strategic pricing are ongoing journeys.

In this article, we’ll discuss how to start correcting your existing data and provide tips for maintaining cleaner product and customer data moving forward that can pave a clearer path to pricing success.

Has My Data Gone Rogue?

Let’s begin by clarifying what it is and what it looks like.  “Dirty data” and “rogue data” are any records in a database that are incorrect, outdated, duplicated, incomplete, and have inaccuracies and typos. The data includes the item master, product info, customer info, product hierarchy, how you’re setting up various products, how you categorize them, and your special pricing agreements.

“Rogue” or “dirty data” happens gradually. It’s easy for a company to accumulate disorganized, erroneous information over time because managing product information is a separate task that understandably slips through the cracks for the sake of daily operations. When there’s no process in place and there’s no one responsible for data management, “dirty data” grows and grows as new products, customers and the information associated with each populates into a company’s system.

How Can We Avoid Rogue Data in the Future?

There are a few things companies can do to get a handle on existing data and manage the new data acquired regularly:

  1. Put processes in place to clean up dirty data.
  2. Assign a point person or team to manage the data and prioritize maintaining it.
  3. Develop a centralized way of setting up records and inputting customer information.

A lot of companies do this work themselves internally. However, if necessary, work with experts for guidance and to utilize the latest best practices and tips for data management, and eventual pricing strategy.

Put Processes in Place to Clean Up Your Data

It’s important to start correcting inconsistent data so that it doesn’t continue to grow out of hand in the future. Cleaning up your data is an ongoing journey so starting now is a great time to dive in, but where do you start? Let’s form processes to bring some structure to cleaning things up and inform other team members how to maintain it.

Tip #1: Work vendor by vendor or customer by customer and don’t attempt to fix your entire customer or product master at once.

Attempting to clean all your data at once can be cumbersome. Instead, cleaning your data in small chunks forms a more reasonable and doable management process by starting with one product or one customer at a time. One tactic may be to tackle your biggest product type or customer. Should this strategy work, you can rank your different product lines by revenue and then pick the one at the top with the highest dollar amount and start cleaning that file. Regardless of how you choose to start cleaning your data, it’s much better do so a little at a time. Take it in small bites and don’t look at your entire master as a whole.

Tip #2: Match your system to how your vendors organize data.

Typically, manufacturers have already created a product data file especially for pricing. They’ll have SKUs, item information, UPC, weight, and descriptions provided. If it’s a purchasing file, there’s costing that’s included as well. Vendors must supply those records, so you can use this information as a basis because the work has been done for you and this data is clean and will be easy to match. Hopefully, it’s a file that you can import into your system.

Tip #3: Know what to look for and make corrections.

Piggybacking off using vendor-provided information to organize and clean your data, look for any mismatches among your SKUs. For example, where are any weird spaces and dashes? Sort your items by A-Z and filter to group them together. You can download the manufacturer price list or catalog and load the entire product file in a temporary place in your system. Build cross references and note whether the vendor‘s product file has spaces and no dashes or vice versa. Update your data to match so that when typed, it will pull up the referenced item next time.

Have a Standard Method for Setting Up Data

Part of developing a process for data management includes centralization. One way to accomplish this is to set up a template with required fields to complete before anyone adds a new customer or product file. For example, required fields can be sell group, price line or buy line. Other considerations for setting up records involve categorizing them: parts and accessories versus finished goods or commodity versus non-commodity items. Standard operating procedures or templates help automate data management and decrease errors.

It would also be helpful to have a centralized group of individuals or team to set up products and customers in your system. If this isn’t possible for your organization, it’s a good idea to train everyone in how to set up data records correctly.

Assign a Person or Team to Manage Your Data

Having a dedicated data integrity person on staff is valuable. Sometimes, this responsibility aligns with the purchasing team or the accounts receivable team, regarding customer master. Once a process starts forming, a key person or team would manage data regularly. New items will continue to roll in as your organization starts cleaning up your existing data, so having a dedicated person to manage records ensures things are getting done. Assigning a point person to lead the task brings accountability to the job.

Implement a review system to assess data accuracy for new items as they come in. Reviewing records on a weekly basis may allow your assigned person or team an opportunity to catch inconsistencies early on, preventing an intimidating list to inspect later. To augment the review process, build an automated report that runs on a schedule of all new items that are set up. The more often you do it the more manageable it is.

Glean Best Practices from Experts

It’s important to prioritize data management because it affects your pricing strategy and, therefore, pricing success. Because every organization has dealt with data management, it’s useful to learn what’s worked for others, and what hasn’t, and then to adopt best practices. Working with experts can help your organization leverage healthy practices and develop steps to take on your journey of pricing data improvement.

Even if your pricing data isn’t in the best place, experts can make recommendations to help. They can take a product group, look at the margins, and supply suggestions, identifying what groups to split up and clean. Using your data, experts help your team understand where to get started by revealing where things are broken. Consider the industry knowledge, tips, and best practices that experts will bring to your journey.

Every Business Deals with Data Management

Dealing with “rogue data” or “dirty data” is a reality that is universal across all industries and verticals, but particularly prevalent in distribution. Everybody’s dealing with it, and it can understandably be discouraging to clean data as more items come in. But it’s possible to do so with a process, and your pricing strategy will be much better afterward.

We recommend tips to help implement this task because it’s a two-part process of addressing rogue data already in your system and preventing future similar data. Have a review process, assign someone responsible for executing data maintenance, and then develop a centralized way for inputting new records properly. Consider working with experts to ensure you’re using best practices for pricing data management.

We’ve explored some ways you can start managing your data more effectively and, as a result, come out on top with your pricing strategy. Of course, you can be successful even if your current data isn’t in the best shape. We’ve helped numerous companies make significant improvements in their pricing even with flawed data, but eventual data cleanup helped them realize even better results.

Regardless of what state your data is in, it can be a gold mine. It holds insights and key information that informs your pricing and other commercial strategies. Start building habits today that will result in cleaner and more valuable data in the future.

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