The government department had to manually extract and aggregate data from more than 1,000 transactions every day. This prevented the team from having an in-depth view of the medication wholesaler’s purchasing patterns and performing accurate cost analysis to utilize the monthly budget better. Similarly, PBMs sent monthly invoices comprising of prescription and billing information in a spreadsheet. However, these invoices had the same compatibility issue and couldn’t be called in the SQL Server database, making it challenging to aggregate usage data for drugs.

Ciena Corporation, a leading networking products and services provider, was facing challenges with the time-consuming process of manually extracting data from PDF purchase orders and feeding it to their in-house custom application. This manual data entry was laborious and prone to human errors, leading to delays in the sales transactions. The company receives customer requests for its products and services in the form of a Purchase Order via multiple methods, including emails. Order and account managers use these POs to create sales orders and initiate related business processes to complete the customer request. These POs are generally in PDF format and comprise of key information, such as customer name, price, currency, total value, parts, addresses, and more. Since every customer uses a different structure and layout for the PO, Order Managers have to read these files and enter this data manually into their ERP system to initiate a sales order transaction.

Vanderbilt University Office of Investments (OOI) manages the university's $3.2 billion endowment. The funds are typically invested through multiple external money managers who have little or no communication among each other. In the uncertain economy, the OOI wanted to determine consolidated risk exposures of the endowment to better understand the impact of different macroeconomic and market shocks. This was a difficult task that often required manual entry because the investment data was scattered across multiple systems. The risk management team needed an application that could integrate and map data from multiple internal and external sources in a timely and repeatable manner and give accurate reports of the endowment's factor risk exposures.

GaP Solutions, a retail solutions provider, was facing challenges with its invoice processing system. The company received around 3,000 invoice variations from vendors in various formats, all containing unstructured data that needed to be transformed into a structured format for further processing. The company had to process 6,000-10,000 files monthly, with employees manually inputting invoice data into their store systems for payment processing, reporting, analysis, etc. The existing invoice processing approach was inefficient and slow, leading to higher processing order time. GaP Solutions tried multiple data extraction solutions but experienced several issues pertinent to data transformation and quality. The company couldn't afford sub-optimal data extraction performance as it would lead to inaccurate payment processing and reporting.

Alshaya Group, a leading name in international brand franchising, was looking to overhaul its existing legacy data architecture as part of its digital transformation journey. The company aimed to implement modern retail and merchandising solutions for better order, inventory, and warehouse management across all their locations. The primary focus was to deploy a standardized data migration framework that could intelligently handle the high volume and velocity of historical data belonging to each brand category. The company opted for a hybrid cloud approach to achieve this objective, which required moving historical data from its legacy ERP systems into an enterprise data warehouse and making trusted data available for real-time analytics through cloud applications. However, Alshaya Group faced challenges in all three key foundational areas of digital transformation: people, process, and technology. The company didn't have the technical resources, dedicated processes, and the right technology stack to build and sustain a standardized framework for moving historical data to its enterprise data warehouse.

RBFCU had previously implemented an internally-developed core banking system to store and maintain member account information. As the credit union’s membership and product offerings expanded, the back end soon grew to hundreds of tables storing hundreds of gigabytes of data. Because of the legacy design of the system, RBFCU’s ability to develop modern, member-centric applications was hindered and fitting new products and services into the system often took an excessive amount of time. Additionally, RBFCU’s team was tasked with completing many regulatory changes to keep the core system compliant, which further hindered them from focusing on developing productive software applications. Ultimately, a decision was made to convert to a third-party core banking platform from Fiserv. The decision was a logical one; however, credit union leadership soon realized that converting to the new platform would be a daunting task. The sheer enormity of the data to be converted would require dedicated resources to get the job done. This spurred the creation of a core conversion team, which was charged with converting legacy data into the new core banking system, developing conversion logic/business rules, identifying opportunities for data cleanup, and integrating newly selected ancillary software with the core system.

VinSolutions, a subsidiary of AutoTrader Group, was tasked with the challenge of integrating data from hundreds of dealership management systems into their core system. This included critical data such as the list price, along with various other pieces of metadata that determine how the vehicle should be listed. With listings on more than a million cars requiring updates every day, the scale of the challenge for VinSolutions approached big-data territory. The company also had to integrate its software into the dealership management system (DMS), the core software applications that automate car dealerships. This was a significant challenge as each dealership used their DMS in different ways, and VinSolutions had to work with a variety of DMSs, including custom-built ones.

Children’s Community Health Plan (CCHP) is a non-profit Health Maintenance Organization (HMO) affiliated with Children’s Wisconsin. CCHP coordinates health care services for over 140,000 adults and children across 28 counties through the state of Wisconsin’s Department of Health Services BadgerCare Plus (Medicaid) program and Together with CCHP (commercial) plans. It also administers Wisconsin’s Foster Care Medical Home Program (Care4Kids), a Medicaid program that coordinates the health care for youth in Out of Home Care in 6 southeastern Wisconsin counties. The state requires HMOs to have a minimum 95% State Medicaid EDI data submission acceptance and approval rate to avoid financial penalties. For this, CCHP has to send accurate EDI 837 files regarding members’ healthcare utilization comprising of records from TPAs and vendors. By ensuring a high EDI approval rate, CCHP remains Medicaid-certified vendor and enjoys better claim reimbursement and capitation rates. For many years, CCHP struggled to meet the 95% encounter data submission and approval rate for State Medicaid reimbursements because of errors and inconsistencies in the encounters from vendors and TPAs. Some common errors included missing the National Provider Identifier (NPI) on referring providers, invalid state code, and missing NPIs to the service location segment.

Aclaimant serves as a bridge among employees, employers, and insurance carriers to speed up the process of settling claims related to workplace incidents. The company receives claim forms from both businesses and insurance carriers in PDF format. These files are manually transcribed to extract pieces of information that are compiled into a standardized report in CSV and Excel formats. The final report goes into the Aclaimant’s proprietary system for updating records on all fronts, giving all parties a centralized view of the claim’s status, details, and progress. The main challenge for Aclaimant was the manual data entry of facts and figures in claim forms. Since the forms came in PDF format, it took hours to transcribe them and create Excel and CSV reports for loading data into the Aclaimant system and data warehouse for further processing. In addition, the changing claim values and other dimensions required updating the system, which meant manually transcribing and entering the revised information.

The IT contractor company was tasked with managing critical personnel data of government employees and processing it for reporting and analyses. The company had to manually download lengthy PDFs containing personnel information, sift through the entire document to extract key data fields, and pass it through SQL queries before transferring it to the personnel CRM system. This process was time-consuming and prone to errors, leading to bad data quality. It took an IT officer more than three hours to process a single personnel document, leading to delays and risk of bad data for analytics.

Cherry Health, a large Federally Qualified Health Center in Grand Rapids, Michigan, was facing substantial costs in managing data for its comprehensive electronic recordkeeping process. The institution used SQL Server Integration Services (SSIS) for data transfers, but the code-based environment required a specific skill set for performing data management tasks. The process of handling patient-related information was time-intensive and required manual effort. The forms were printed, shipped to a central scanning location, scanned, stored electronically in a PDF format, and then shredded and disposed of. Cherry Health sought a solution that could save its users from performing these steps and result in significant cost savings.

CRC Industries, a global provider of over 1,300 chemical specialty products, was facing challenges in moving its internal and external data assets from legacy systems to the cloud. The company lacked the resources and technical infrastructure to handle large amounts of data and speed up the entire process without compromising on data security and integrity. The company's IT team had performed data migration for companies CRC has acquired in the past, but it was always a complex, time-consuming process. The global manufacturer exchanged data between its different business units via Excel or text files, used a legacy system to analyze them, and performed data quality checks via SQL statements. However, the only way to load data in D3FO was through its built-in templates, which mainly supported the Excel format. This made it difficult to handle, import, and export data to and from D3FO.

The Union League Club (ULCC) is a private city club that offers a wide range of hospitality services. It uses multiple systems to track data related to membership, finances, guestroom availability, event planning, inventory, and other aspects. The ULCC has three main systems that handle membership data, and various facilities and services offered to the members. All the three systems work in unison to provide a complete picture of membership and club activities data. This enables to the ability to extract actionable insights to effectively manage resources, finances, and membership services, for better member experience. To achieve this, the Club must use a data integration solution to move and orchestrate data between MySQL and Oracle databases. The solution also helps in mapping specific tables from each system to keep them up-to-date and consistent for use in further analyses.

The Michigan Education Association (MEA) was facing a major challenge with its manual data extraction and integration process. The association received invoice data from a third-party vendor via an SFTP server. This data, contained in a zip folder, had to be downloaded, unzipped, and loaded into an MS SQL transactional system. The zip folder contained different types of files, including CSV and flat files, often with thousands or millions of records. Without an automation tool, the team at MEA had to download the individual files, set up flows, and execute the integration process manually. This was a time-consuming process. Additionally, MEA only wanted to insert records that had been changed or updated into the MS SQL transactional system, which was a challenge without a tool to facilitate the process.

The Finland-based engineering and service company, a global leader in the manufacturing of escalators, elevators, automatic doors, and moving walkways, faced a significant challenge in processing claims for maintenance and servicing on broken or faulty parts. The company's after-sales team had to manually sift through entire agreements to find out whether a customer is eligible for its under-warranty services. The biggest challenge was extracting coverage and exclusion information from 40,000+ contracts manually. Each contract contained heavy legal verbiage that spanned up to 250 pages. It took anywhere between several hours to days for the paralegals to review each contract and draw out a synopsis for claim eligibility. Due to the sheer volume of contracts and lack of resources, reviewing every document with precision and verifying customers' claims would've taken the team thousands of man-days.