It was just mid-March when GPT4 blew us away with its applications and advances. Bard is moving quickly on its heels, and DeepMind is not far behind. The obvious outgrowth is that artificial intelligence (AI) is changing the way companies operate in many sectors, but AI needs a solid data backbone for AI to perform its magic.

However, for AI to perform its magic for finance teams, we need to provide it with finance and operational data, including internal and external data points. Data is the backbone of any AI application, and it is critical that the data is clean, structured and relevant.

Over the last 15 years, companies have implemented Enterprise Resource Planning (ERP) systems to standardize how finance data is recorded, stored and retrieved as part of their digital transformation journeys. But this process is optimized for reporting and compliance purposes only and not for AI. We need to revisit these data assumptions to accommodate AI in finance processes.

Here are seven tips to avoid data pitfalls when implementing AI for finance processes:

1. Record Actual and Forecast Data in the Cleanest Way

To enable AI to extract valuable business insights from financial data, it is crucial to ensure that the data is accurately captured using proper accounting practices. This involves maintaining consistency in data entry, including adhering to standardized rules for recording date formats, currency and account names.

To avoid errors and inaccuracies, accounting teams should take care to properly reconcile accounts and classify transactions, while avoiding last-minute adjustments to records. Regular reconciliation of accounting records helps to ensure that all transactions are properly recorded and accounts are up to date. The use of automation can help streamline data entry processes, resulting in improved accuracy and consistency.

2. Revisit Your Chart of Accounts (COA) Structure

The Chart of Accounts (COA) and the hierarchies define how we structure finance data.  It is essential for accurate financial reporting, as it provides a standardized framework for recording financial transactions. However, as companies grow and change, their financial reporting needs also change, and their COA structure may become outdated or inadequate.

A well-structured COA can provide more granular financial information, allowing decision-makers to better understand the financial impact of their decisions. The granularity also helps AI systems to capture more data to find patterns. For example, if a company is considering launching a new product, AI can separate expenses by product line and help determine the profitability of the new product, if enough granularity exists in the finance data.

3. Save All Historical Data

To prepare for the increasing importance of data in an AI-driven world, finance and IT teams should establish a process for preserving all historical data. Although some may believe that data from a decade ago may not be relevant, it is better to save it now as a precautionary measure.

Preserving historical data is crucial for several reasons. First, it serves as a valuable reference point for future analysis, trend identification and predictive modeling. Additionally, historical data is an essential component for training machine learning algorithms, which are crucial for AI applications in finance.

By storing all historical data, companies can gain valuable insights into trends, patterns and anomalies in the data, which can lead to better decision-making. Historical data can also be used to track changes over time, evaluate performance, and identify potential risks and opportunities. Along with actual data, all versions of forecast data should also be saved for measuring accuracy.

Moreover, as technology advances and new AI applications emerge, companies may discover new uses for historical data. Therefore, saving all historical data can ensure that businesses have access to the necessary information to capitalize on emerging technologies.

It is advisable to store the data at a granular level so it can be mapped to newer hierarchies as needed in the future.

Getting your data right is a critical part of the digital transformation process. Learn how to do it in the 2022 AFP FP&A Guide: Get Your Data Right.

4. Implement a Metadata Management Tool

Financial metadata is the chart of accounts used to track the financial health of a company. Typical metadata includes a list of accounts, entities, cost centers, products, etc.

To make work AI on financial data, it is essential to adopt a streamlined approach to metadata management. A centralized metadata tool is crucial for this purpose, as it can ensure that the underlying data structure remains consistent throughout time. By leveraging AI algorithms, which are trained on financial data, patterns and variations in trends can be identified more accurately.

Additionally, the use of a metadata tool can reduce errors caused by manual data entry, resulting in improved accuracy of financial data. The tool can also help improve compliance with regulatory requirements and maintain data quality.

5. Break Data Silos Through Self-Service Reporting

Self-service reporting is a data approach that allows users to access data and create reports on their own, without needing assistance from the IT department or data analysts. This approach breaks down data silos by giving finance users access to operational and transactional data, which they can use to gain insights into the business and make informed decisions.

By breaking down data silos, self-service reporting enables better collaboration across functions and helps to drive business results. In addition, self-service reporting can also help to improve data quality and accuracy. When users have direct access to the data, they can quickly identify and correct errors or inconsistencies, which can help to ensure that the data is accurate and up to date.

6. Use AI-Enabled Software to Automate Data Curation and Cleanup

Data needs to be curated and cleaned before it can be fed to AI. While this process is effort-intensive, there are AI tools available that can automate data curation and cleanup, saving time and minimizing the risk of errors. These tools can streamline the process of handling missing data, outliers and negative numbers, resulting in data that is clean, relevant and structured for AI algorithms. Companies can take advantage of these tools to improve the quality of their data and enhance the accuracy and effectiveness of their AI applications.

7. Don't Prematurely Invest in Data Lakes for the Sake of AI

Data lakes are not a prerequisite for AI implementation! Prematurely investing in data lakes for the sake of AI can be a costly mistake for a company. While data lakes can be useful for storing and managing large volumes of data, they require significant capital investment and ongoing maintenance costs. This can be especially challenging for small and medium-sized enterprises whose budgets may not justify the expenditure. Companies can leverage existing data extraction tools and databases to obtain the requisite data for AI implementation.

Furthermore, AI in finance is still in its early stages, and there is often a lot of trial and error involved in the process. Companies may find that they do not need all the data points that are stored in a data lake for their specific AI needs. In such cases, the money invested in building and maintaining a data lake would have been wasted.

In conclusion, implementing AI in finance can bring numerous benefits such as improved accuracy, faster processing and enhanced decision-making capabilities. However, it is essential to avoid the data pitfalls to make the most out of this technology. By prioritizing a strong data strategy and governance, finance teams can become the champions of AI adoption in their organizations, leading to better financial performance and overall business success.

About the Writer

Ashok Manthena is an author and practitioner of A.I for finance. His insights and tips provide valuable guidance for companies to harness the power of A.I. for their finance processes.

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