Why is Data Maturity in a Finance Organization important? 

In 2020, Aston Martin’s balance sheet eventually ended up with discrepancy totalling to $20mn in reconciliation. The accounting error came to understating car sales in dealerships. Now, this issue came to highlight – quarters later , in financial reconciliation stage. Any seasoned finance professional will advise that when Enterprises reconcile data, the data changes happen across multiple levels – where organizational hierarchy or partner hierarchy eventually create multiple layers of financial data. Most financial back-office or mid-office teams work really hard to ingest different datasets and build a single financial view for making decisions.

To build single financial view, enterprises incorporate data sources and bring in these datasets. Organizations have to cross multiple stages of maturity to achieve best-in-class practices. So what are different maturity stages of enterprises ?

Stage 1 – Baseline/ Ad-Hoc.

Most organizations have very basic processes for data management or analytics. These organizations have data silos and CFO team focuses on manual processes and run through spreadsheets for creating financial reporting and analysis.


Stage 2 – Developing/ Managed

Once organizations start valuing value of data, organization starts using basic analytical tools for financial planning. Eventually, organizations build a sense of standardized processes for data management and benefit via financial literacy across business functions.


Stage 3 – Defined Analytics

In this stage, CFO teams start making data-driven decision making. The data management processes have become well-defined and data related documentation for understanding financial risks. Mid-size companies using historical data start making forecasting and manage risks via automated decision making. 


Stage 4 – Integrated Data / Analytics function

Now, organizations start embedding data into financial processes. Business teams have defined metrics and understand context of incoming data.  Teams leverage advanced analytic tools that helps to take decisions in real-time. With gradual confidence seeping in, business leaders start aligning business strategy with data strategy to making decisions.


Stage 5 – Cutting Edge Data Management

With major hurdles of data quality solved, financial data is sorted from historical perspective.

Organizations can now start leaping into advanced layers of data forecasting or modeling around business use cases. To achieve best-in-class, Enterprises uses ML recommendations for building innovation in financial Operations. Now, organizations can build data models and generate benchmarks for financial data and analytics.

Closing Thoughts

In conclusion, managing data analytics for CFO function is complex and prone to errors in data reconciliation. To streamline data and analytics function, organizations needs to have full-proof data management solution. By using cutting edge data analytics solution such as astRai, CFO function can maximize value from data residing across spreadsheets, ERPs and Data lakes within Enterprises