Achieving Speed and Flexibility in Business Implementation Cycles

Gabriel Stern, Experian

By streamlining data management processes in HR and finance, there have been roll-on effects throughout the business that have brought significant cost savings and improvements in data quality. We replaced costly interfaces, facilitated data migration and significantly reduced implementation cycles.

Background

With over 30 operations around the world, Experian is a company with global presence. Over the last eight years, Experian experienced the typical growing pains that any company of its size will have. The system implementations have been challenging. Applications were held in multiple servers in different versions of the application, and internal processes were diverse as well. Master data required some cleansing and enrichment effort.

The mass migration of data was done programmatically. Yet, as Experian incorporated new business or expanded into new countries, this approach faced numerous challenges. 

Forces Driving the Change

Originally, implementations followed industry-standard cycles. Specifically, we would look at the existing data, normalize them offline and load them using a specific component built for that purpose.  But we not only had to bring the data into our global instance, we also had to standardize them to support central data processing hubs or shared services centers. We could not afford managing by exception. We needed short process cycles and we had to be cost efficient. Operating in more than 30 countries with numerous country-specific features, ranging from different languages, different character sets, different number formats and different tax requirements, the data conversion posed a significant challenge. 

One such challenge was the creation of new suppliers. Suppliers were created in small batches; the amount of time spent on creating a supplier properly was disproportionate compared to other activities. The specific knowledge it required in any given country to complete all the necessary fields properly created a significant control burden. Not only was the control inefficient, by the time a data entry error was discovered, the implications had snowballed. In addition, supplier-based reviews meant a significant IT effort, and so did mass updates. They were more frequent than anticipated. Tools like Toad could be used to extract the data but could not be used to reload them. Also, they granted a level of access to the data that made the auditors uneasy. Validation was mostly visual by sample, and the process would unequivocally go through an Excel spreadsheet anyway.

Company integrations would take at least six months, with data migration being a significant bottleneck. Figure 1 depicts a data conversion run programmatically.

With this approach, more time was spent on building the conversion component than on the data itself. Besides, the approach needed several iterations in several environments. With the data constantly changing, each environment required a full iteration.

Additionally, we faced other challenges: 

  • Cost: Low volume data (under 10000 records) were too many to load manually but not too many to justify a custom component. 
  • Focus: What matters is the quality of the data. Tests on the custom loaders divert the attention. 
  • Redesign: Custom-built components had to be reshaped to each implementation. 
  • Optimal Solution: Bad master data will undoubtedly hinder user adoption and impact downstream processes. 
  • Speed: Avoid numerous iterations. They slow you down. 
  • Flexibility: combining multiple features in one solution is better than one solution for every need. 
  • Prioritize: Data migration should go unnoticed. You are implementing new processes, not new data. 
  • Fast Training: No need for training is even better! 
  • Black-out Period: If there has to be a black-out period, make it as short as possible. 
  • Keep it simple.

Finding a Solution

On the journey to finding a solution, we knew what needed to be improved. Based on our experience on previous implementations we classified the data in two groups:

Since data is owned by users, why not give the users the tools to deal with them without compromising the data integrity? 

Whilst looking for a solution we found More4apps. 

More4apps worked well for Suppliers, Customers, Receipts, and Open Transactions with a reasonable amount of data (10000 records). It did work with more than 10K but not that efficiently. The processing cycle was simplified significantly.

Lessons Learned

Our implementations with More4apps made the data migration cycle more effective. It was no longer required to cleanse and enrich at the source; the activity was moved into the More4apps spreadsheet. Excel is more flexible and users don’t need special training on how to use it. The More4apps spreadsheet became the de facto master data source for testing and transition activities. This helped in reducing the time and making the process significantly easier for users and management.

Data migration no longer needed staging tables, custom lookups, business rules, translation rules, filtering rules, derivation rules, mapping rules, validation rules, transformation rules, exception reports, custom error messages and automated notifications. 

The blackout period was significantly shortened. 

There was no need for a specific Dress Rehearsal Test Environment.  

Very little training was needed. The tool is simple to use. Most users adopted More4apps immediately and even expanded what they could use it for. The tool became a significant booster for productivity

The number of requests to review or update data increased–it looks like users were taking for granted that updates had to be done manually. All of a sudden, they all wanted access to the tool.

We started with one loader (Supplier), but we quickly installed the AR Invoice Wizard, the Receipt Wizard, the Customer Wizard and the HR Wizard. We also recognized that the increase in productivity facilitated centralizing the effort in hubs.For integrations, we have used the PO Wizard and the AP Invoice Wizard to support Data Migrations.The original purpose of the More4apps Wizard eventually expanded. They are currently used for: 

  • Data Migration (new records). 
  • Update existing records (business as usual). 
  • Provide real test data for other features. 
  • Download data. 
  • Centralize master data management functions

The tool is currently used in all our shared services centers for almost every country where we have operations.  

Benefits

Experian increased efficiency and productivity (KPI). Data Maintenance activities that were already centralized were further consolidated. The workload for one-time activities shifted from IT to end-users, allowing our IT department to focus on the technical job and our users to concentrate on managing the data. Turnaround for mass data updates changed from weeks to days, impacting not only the quality of the process but also the cost involved in the process. The More4apps wizards remain in use for some day-to-day activities. For these users, the time spent in entering data turned to seconds compared to minutes. The number of data entry errors dipped considerably.

Because More4apps offers download features as well, the common approach to determine what fields are needed is to reverse-engineer the template: The user creates one record manually and downloads it into More4apps. All other records are then created following the pattern set by the original record.

The Vendor

In terms of enhancements and support, More4apps has been a strategic partner for Experian as we operate in several countries, each with its own features. We no longer spend development time in the C in RICE-W component. Support also has been a good experience for us. More4apps acknowledges the incidents within the hour, and the average resolution time tends not to exceed one week. The DBAs install the wizards quickly in minutes. Named users assignments sit with a key user.

Because this is a user tool, most incidents are created because of a lack of knowledge with Oracle. One example is how to recognize a DFF. We did have challenges with large amounts of data. It is important to note that we are not early adopters of More4apps upgrades. If the current version works fine, we tend not to replace it.

More4apps has supported enhancements for Experian when needed. Specifically, they have increased the number of Flex – fields and they have supported specific localizations requests. More4apps has been a significant contributor to reducing costs at Experian. It has had a very positive impact on implementation cycles, on data entry standardization and data processing errors. 

Gabriel Stern started his career in finance more than 25 years ago. He has held various positions at companies like Cargill, Phillip Morris, Oracle and Experian. Either as con – troller or project director, he has been respon – sible for process reengineering, rolling out global applications in more than 20 countries and setting up shared services centers. He is currently the head of the Americas Shared Services Center based in Santiago, Chile.

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