Clean data for better decision making

by | May 8, 2017

A few weeks ago, we wrote a post about how the most strategic, profitable decisions are made with real data and analytics, not anecdotes, assumptions or gut feelings. Now we’d like to take a bit of time to discuss the quality and cleanliness of the data you use to make strategic decisions. After all, the choices you make can only be as good as the input used as the base of your conclusions.

Clean, consistent and connected trading partner information is important for effective management of business relationships. That could be the relationship between a retailer and a vendor, a retailer and a 3PL, a vendor and a manufacturer, and a variety of other relationships across the retail supply chain. Whatever link you are in the supply chain, access to the right data is vital, as accurate data provides the foundation for businesses decisions – from investments to marketing strategies, pricing agreements to cost reduction activities and more.

How we get unreliable, dirty data

In many large organizations, information can be scattered across a variety of departments, processes and applications. More often than not, this results in incomplete, incorrect and inconsistent information. When the data isn’t reconciled due to siloed systems and disconnected departments, errors in inventory, payments, shipments and more run rampant. What’s worse is that updating the information in one system doesn’t always mean it gets updated in other systems across the organization. Multiply these problems by the number of relationships in your trading partner network – it could be dozens, hundreds, thousands or more. That’s a lot of potentially bad, unreliable data.

It’s impossible to get a holistic view of a trading partner’s performance without complete, accurate, clean data. Without it, you’re blind to the overall relationship. How has the partnership been performing for you? Has the vendor been compliant with the contract? Has inventory lapsed due to shipments arriving late? Are the 3PLs properly fulfilling drop ship orders? Is this retailer underperforming compared to other retailers in the same region?

If you’re not even sure if the information you have is accurate, it’s difficult to make the best decisions. The bad data could result in bad decisions every day in sourcing, purchasing, inventory levels, efficiency, etc., leading to financial losses, not to mention on top of that the extra administration costs of dirty, disconnected data. Additionally, without the very basics of clean data, it’s impossible to take a deeper dive into analytics to uncover missed opportunities to be more profitable and better deliver on consumer expectations. Those missed opportunities can be the costliest mistakes of dirty data.

How accurate, clean data is achieved

There are a variety of approaches to achieving clean data, but here are some tips that should be helpful for determining your strategy for cleaning up your data.

  1. Identify all the sources of data. Where is all this information being stored and who is using it? There could be multiple departments that use some part of the information for various duties, from accounting to the loading dock. Additionally, each department may be using different applications of software that may house information that needs to get cleaned up.
  2. Apply data quality improvements. Clean up the data at the source before trying to connect and combine it with other data. This method avoids bringing along dead SKUs and other incorrect data into your new, clean data system.
  3. Don’t reinvent the wheel. It takes specialized technology that’s optimized for collecting, reconciling, managing and connecting varied data sets to achieve complete information management. Find a data management and data integration platform that can overlay your existing systems with as little disruption to your business as possible.
  4. Good data in. Cleaning up existing data is one thing, but you have to keep it clean. Creating data standards, engineering processes to collect all the correct data for the standards and enforcing the standards across the business organization and your trading partner network are vital to keeping the data clean. Regular audits can also be helpful.
  5. Good data out. Use the clean data to make good decisions within your organization and without. When appropriate share clean data with your trading partners to help with opportunity discovery and improved collaboration. Deploy your clean data to the places it can be turned into insights and those insights can be turned into money.

These guidelines may seem pretty obvious at first, but failing to apply these standards across organizations are the main reasons for bad data and the poor decisions to which bad data leads. These basic practices can help fuel operational and analytical applications, as well as predictive and strategic planning. Clean, consistent and connected data encourages a more accurate view of trading partner performance, compliance and risk. As a result, your teams can be empowered to make better decisions, improve negotiations, streamline processes and encourage better profitability in the future.

Clean, accurate data across all departments and trade partners is possible. To learn more about how SPS Commerce retail supply chain solutions can help, please visit the SPS Commerce website and request to speak with a retail and supply chain expert.

Rob Davis

Rob Davis

Senior Director of Product Management at SPS Commerce
As senior director of product management for SPS Commerce, Rob is responsible for managing the company’s cloud based Analytics product line including product requirements, roadmap, marketing and GTM activities. Before joining SPS Commerce in 2014, Rob was the director of business intelligence product management at Oracle. He has also held positions with Siebel Systems, AT&T Worldnet and internet startup Quixi. Rob has a bachelor of science degree from the University of Rhode Island.
Rob Davis

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