The burdens of manual processes
Regulators know the reality of working with data in banks. They know it includes manual work and slow, cobbled spreadsheets, which are prone to errors.

Historically, the link between data models and operations has been manual, unreliable and tedious. It required a cumbersome translation of models into executable runtime by developers, with each requirement taking weeks or months to implement.

All of this work, and there's still no guarantee that the model is accurate within the pipeline.

Considering the increasing demand for accurate and timely financial insights to strategic decision-making, your financial organization is ready for automation when:

Your teams are involved with repetitive, routine and non-productive tasks.
You spend too much time on significant manual reconciliations and the updating of multiple Excel spreadsheets for closing and reporting.
Your current accounting system involves manual workarounds and data re-input.
There's a frequently high error rate detected in reports.
You have poor visualization and flat reports without flexible drill-down options.
Many organizations continue to invest significant time every day in calculating, manipulating and validating critical financial reporting data using spreadsheets.

The consequences of manual and outdated processes include:

Thousands of sources, targets and integrations are controlled without standardization to the process.
If people have no other option and do their own thing, you're in danger of creating siloed data. This causes a semantics of process and quality that's not helpful in reporting and analysis.
If data sets aren't correlated, individual functions of your organization will be disparate and possibly duplicated as each department builds its solutions to its problems.
Investing in new tools that allow you to better build out your processes and data architecture means that you can shake off the status quo and focus on structure and innovation at the sam