Case Study: How Improving Data Structure Saves Money

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Data is a powerful tool, which businesses can and should use to drive both daily and long-term decisions. When organized well, data can do much to optimize performance. When structured poorly, however, data can create unnecessary management and storage costs that affect an organization’s bottom line. In this article, we take a closer look at the basics of data structure, and we examine how understanding and optimizing data structure can save money in the long-run. 

What is Data Structure?

Data structure encompasses a company’s approach to managing, processing, storing, and retrieving data. While there are many different ways to organize data, a company’s data structure ultimately needs to serve its core business interests. Ideally, the data structure should provide easy access to stakeholders so they can expediently work with the information they need, and it should create a streamlined storage footprint that contains no duplicate/corrupt data files. 

Why Does Data Structure Matter?

If your company’s data is not properly organized and stored, unnecessary costs are almost certain to build up over time. Further, when data is disorganized, missing, duplicated, or corrupted, it becomes exceedingly difficult, if not impossible, to meaningfully analyze trends, evaluate metrics, and optimize performance. As such, understanding and improving your company’s data structure can and will lead to lower costs and better performance.

Case Study: Data Aggregation and Analytics (Greenville Ventures)

A current client, Greenville Ventures, was facing difficulties managing data for a large-scale marketing campaign. Due to some data management issues, the team ended up facing 6,000 unexpected backorders, which resulted in a major impact to both the length and success of the promotion. These errors largely stemmed from marketers working across three systems simultaneously: the backend of the ad platform, the backend of the warehouse management system (WMS), and the backend of the web interface (Shopify). With daily ad budgets often in excess of $100,000 and more than 150 unique SKUs, the Greenville team was overburdened with numbers, manual calculations, and a whole lot of moving parts.

Qnectus stepped in to help Greenville create a more scalable, workable data structure that would allow their team to aggregate and analyze data efficiently and accurately. They worked with Greenville to:

  • Identify critical data across the ad platform, the WMS, and Shopify.
  • Design, develop, and integrate a data visualization platform with the relevant data streams.
  • Present both raw and computed data cleanly to assist decision-making.

As a result:

  • The marketing team was able to spend more time on the data visualization platform instead of working piecemeal across systems.
  • Data requiring computation(s) were automatically presented in their final form – allowing Greenville to more rapidly and accurately make decisions.
  • Additional third-party data streams from common carriers (both domestic and international) were integrated to provide fresh insight regarding lead times.

At the time of the next major promotional event, Greenville realized higher levels of volume over a longer stretch of time with hardly any inventory-related errors. Qnectus’ solution has become a cornerstone of Greenville’s daily marketing activities which generate between 4000-8000 e-commerce orders per day and climbing.

Conclusion

At Qnectus, we are proud data structuring experts who help business owners develop a rich understanding of their company’s data structure and provide solutions wherever possible. Our team’s expertise relating to data corruption and redundancy is expansive, and we are ready to help you optimize your business today. To ensure that your data is stored in the most efficient manner and is readily retrievable, contact us today.

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