Case Studies > Modell’s Ups its Game with Self-service Data Prep

Modell’s Ups its Game with Self-service Data Prep

 Modell’s Ups its Game with Self-service Data Prep - IoT ONE Case Study
Technology Category
  • Application Infrastructure & Middleware - Data Visualization
Applicable Industries
  • Retail
The Customer
About The Customer
Modell’s - Modell’s is the oldest sporting goods retailer in the U.S. with more than 150 stores in the Northeast—from New Hampshire to Virginia
The Challenge

Retail is a fast-paced business. To stay competitive, organizations like Modell’s must react quickly to market place conditions, industry trends, and economic upticks and downturns, sometimes overnight. 20 years ago, critical sales, inventory and transactional data lived in a mainframe system that was only accessible through Lotus 1-2-3. Joe Paltenstein, now AVP, Assistant Controller at Modell’s Sporting Goods said, “It was challenging to get the data we needed out of the mainframe system and into a workable format that we could use. And with so many different types of data it was hard to extract any useful insights without a painfully slow reformatting process. We had to print out reports on green bar paper and then entered the data into spreadsheets.”

The Solution

At the time, Datawatch had just come out with its Monarch product for data preparation, so Modell’s decided to give it a try. Now, two decades later Modell’s is still using Monarch and depends on its efficient and effective data preparation capabilities for its day-to-day business needs. “The product is so intuitive,” said Paltenstein, “I learned how to use Monarch my first day on the job, and Datawatch continues to enhance it.” Today, Modell’s has a new ERP system and users in finance, accounts payable, inventory control, credit card reconciliation, sales auditing, and real estate all rely on the Monarch system to pull financial reports, accounts payable check registers, and various merchandise inventory reports. On average, Paltenstein produces 200+ reports a month. “We use Monarch to extract different types of data and summarize it into reports, which can be thousands of pages long. The system also helps us quickly spot anomalies that can provide important insights into our business,” he added. “It’s so easy. I just create a batch file, extract the data, and run the report. I use Monarch to find problems. If the data doesn’t make sense, it helps you find that needle in the haystack.” Today, more than 20 Modell’s employees are dependent upon the data models that Paltenstein has built using Datawatch for their day-to-day jobs. The finance department even uses it to bridge the gap to other departments that don’t use Monarch. In addition, individual store profit and loss reports (P&Ls) are created monthly using Monarch to retrieve and format the data so upper management and operations can analyze it. Monarch gives Modell executives a big picture view of all their stores —letting them see which are performing better than others and what inventory is moving or isn’t. They can also drill down and see how much they’re spending on utilities, supplies, credit card costs, etc.—to help them make smart decisions about their business.

Data Collected
Data Security, Inventory Levels, Process Procedure, Sales, Time Series and Transactional
Operational Impact
  • [Efficiency Improvement - Labor]
    Streamlined process makes the work more straightforward, enabling employees to work more efficiently
  • [Data Management - Data Security]
    Ensures that important data is impossible to breach and access
  • [Data Management - Data Analysis]
    Data storage and analytics allows to actionable results

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