Is Self Service Reporting the First Step To the AI Transformation In Finance?

By: Ashok Manthena

Predictive analytics has been revolutionized by the advances in artificial intelligence/machine learning (AI). Using AI in various forecasting and predicting processes within an organization has become less expensive and more effective. But finance has been slow to incorporate AI into its procedures. Comfort in our historical reporting role, lack of access to the right data, among other things, could be factors in this latency.

As part of early steps of digital transformation, Companies have deployed ERPs and standardized methods to capture financial data. This has aided in the development of a financial source of truth which simplified the finance data operations. However, it has resulted in many data silos, with finance, marketing, and inventory data all being in separate ERPs. Finance teams typically have access to financial data but struggle to obtain additional information on marketing, inventory, and human resources.

Due to rapid shifts in the business environment, finance personnel have been fielding an increasing number of questions about the company’s performance from management. Traditionally, finance teams have relied on their Information Technology teams to query data and generate reports. These reports cover financial performance, forecasting trends, expenses, margins and other key financial metrics. While this technique of working with IT ensured data quality and report consistency, it limited finance users’ ability to acquire information when they wanted it. It also limited the scope of exploration and experimentation with the data.

What is Self-service reporting

Self-service reporting refers to the ability of a user to access data and generate reports without the need for technical support.

By implementing self-service reporting capabilities:

  • Finance teams will reduce the amount of time it takes to create new reports and develop new insights.
  • Financial data comprehension will improve throughout all levels of the firm, including leadership.
  • Data discrepancies will be identified quickly
  • The company will be better aligned for a single source of truth.

Data silos will be broken down by democratizing data access.

  • Take a another step toward developing a data driven culture with evidence based decision making.

With all these benefits to reap, how do we implement self-serve reporting in finance teams?

Self serve reporting necessitates a transformation in how we capture the data, how we control the access and how we query and build the reports. Contrary to popular belief, it is more about process and data governance than it is about the technology platform.

What is required to enable finance with self-service reporting?

  1. Simplified and streamlined data access controls.
  2. Organizing the data
  3. Training in report creation.
  4. Technology that enables easy report building.

1. Simplified and streamlined data access controls

For financial data , access control is a crucial process for SOX purposes. Controls, approvals and audit are the important factors that need to be included in the access control process. After the control mechanism is in place, it’s crucial to understand how to get the correct access to the teams that need it. The analyst teams should have access to both financial and non-financial data, such as HR and marketing.

The strict need-to-know principle for data security limits the ability of a user to explore and experiment with various data points. For example, if an analyst wants to predict costs associated with procurement, then they need access to supplier level data along with the invoice data to help better model the costs associated with it. So, a right balance needs to be achieved between data security and the open access to the data.

2. Organizing the data

In order to achieve efficient reporting capabilities, financial data structures and platforms need to be designed to handle various kinds of data like transactions, balances, budgets, forecasts, adjustments etc. If data is scattered across multiple sources, this will help bring all data to one location for easier access, control, and report generation. Many corporations are creating data lakes that contain all of their financial and other data in one place.

3. Training in report creation

Finance departments require training on self-reporting tools such as Excel Plugins, Power BI, and Tableau, etc. Also users need to know query languages like SQL for data mining and Python to access and visualize data quickly and generate insights from raw data.

4. Technology that enables easy report building.

There are numerous technology platforms (PowerBI, Tableau etc) available on the market that can help with report creation and data storage. However, keep in mind that these technologies do not account for data quality or availability. These platforms need to be vetted on the basis of the user learning curve and the cost of operation.

How Self-reporting will transform finance:

Once self-service reporting becomes a norm in an organization, it fundamentally changes how data is consumed. Finance departments have been displaying data in a tabular, raw style. These reports are difficult to comprehend, and the analyst must expend much effort in explaining the findings. Interactive reports and dashboards will make it easier to display the insights and trends found in raw data in a more meaningful manner. Analysts can spend less time on data mining and report generation and more time on discussion and course correction.

Another fantastic feature is drill through, which allows users to delve into data at various segment levels without the need for many reports. With access to all key data points, finance users can start using trends and various other statistical methods to model and predict outcomes. This could be a good starting point for data driven driver-based predictions. Also, real time reporting could be generated once the data operations are streamlined, boosting the frequency of insight generation and decision making.

Conclusion

Finance departments are becoming more analytical, and they require access to the right data at the right time to assist C-suite executives in their decision making. For AI to be effective and produce relevant results, we need to provide the correct historical data, business driver information, and future assumptions. In both these cases, it’s imperative to invest in the right enterprise data management strategies and platforms to embrace future opportunities. Self-service reporting will encourage finance teams to create a data driven culture , which will aid in eventual AI transformation.


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