Case Studies » Leading Financial Institution

Enabling data analysis

Standardizing data for control automation and operational efficiency

If you want to successfully implement process optimization, you need to capture critical data first.

A leading financial services institution embarked on an enterprise-wide mission to streamline its business processes to align with industry benchmarks on efficiency. Part of the goal was to automate 100 process controls. These process controls may be an entire process or an element of a process that can be automated to remove the human time and effort it takes to perform the task and move on to the next step.

Process optimization through control automation can not only save time and money, but it can remove human error, improve operational efficiency and empower teams to focus more tightly on goals. The biggest challenge is knowing which processes are most advantageous to automate.

The organization knew which kinds of controls they wanted to automate, but they lacked the single, standardized view of the process they needed to perform data analysis and select the ideal 100 controls to automate. In fact, the data they needed to assess the situation was spread across more than 25 different spreadsheets, each of which had variations in formatting. They needed help. So they called in Celerity to sort through the data and find and automate the best candidates for control optimization.

Services

  • Data analysis
  • Operational excellence
  • Process optimization
  • Robotic process automation

Services

  • Data analysis
  • Operational excellence
  • Process optimization
  • Robotic process automation

If you want to successfully implement process optimization, you need to capture critical data first.

A leading financial services institution embarked on an enterprise-wide mission to streamline its business processes to align with industry benchmarks on efficiency. Part of the goal was to automate 100 process controls. These process controls may be an entire process or an element of a process that can be automated to remove the human time and effort it takes to perform the task and move on to the next step.

Process optimization through control automation can not only save time and money, but it can remove human error, improve operational efficiency and empower teams to focus more tightly on goals. The biggest challenge is knowing which processes are most advantageous to automate.

The organization knew which kinds of controls they wanted to automate, but they lacked the single, standardized view of the process they needed to perform data analysis and select the ideal 100 controls to automate. In fact, the data they needed to assess the situation was spread across more than 25 different spreadsheets, each of which had variations in formatting. They needed help. So they called in Celerity to sort through the data and find and automate the best candidates for control optimization.

Standardizing data from disparate sources for data analysis

The first step was to combine the data from the 25+ sources and standardize it.
To combine both controls and criteria data from each of the sources, Celerity first created a primary dataset with the controls data. This was done by taking each source dataset and ensuring it was in proper tabular form, standardizing the key identifying variables, tagging the dataset with identifying information, and appending it to the other source datasets. Celerity then merged the criteria data onto the primary controls dataset.

Once they were done merging and standardizing data, they could then perform data analysis to determine which 100 controls would be the most advantageous for process optimization.

Prioritizing controls for process optimization

Using their established criteria—such as whether the controls were already automated, what business process they were part of, and whether they already had planned improvements—Celerity filtered the unified dataset down from over 600 controls to a prioritized list of over 120 controls, in line with the 100-control goal. They then summarized the results using graphics and tables to help the organization’s process improvement team learn more about the control automation process and help them communicate plans to the affected business areas prior to preparing for automation.

Automating and documenting the processes

The organization wanted not only a list of the 100 controls, they wanted the list created in an automated way. Microsoft Power Query was already implemented in the organization’s system, so Celerity used that to perform the analysis. Along the way, Celerity also created documentation to summarize the key steps taken. Between the documentation, the established familiarity with Power Query and the newly streamlined data, the organization’s process improvement team had everything they needed to rapidly develop automated processes in the future.

Establishing a new level of operational efficiency

Celerity helped position the organization to achieve its goal of automating controls and left them with a roadmap for performing control automation on their own in the future. The streamlining of data and data analysis gave them visibility into processes across the enterprise. And the automation documentation will enable them to replicate the automation process with any control in the future. With this information, the organization can now quickly meet industry standards for automated controls, compete in a cost-effective manner in the marketplace and achieve new levels of operational efficiency.

Before Celerity

25+ separate control datasets

Nonuniform formatting of key variables

No repeatable process for building combined dataset

No prioritized list of controls to target for automation

With Celerity

All data streamlined into a single dataset

Waterfall Method IconUniform formatting to enable streamlined data

Repeatable, documented process for building combined dataset

Prioritized and summarized list of controls targeted for automation