In this article, you will discover the potential benefits of finance analytics and the qualities of best-in-class analytics — and what you should look for in an AI-driven solution.
Financial directors spend 75% of their time on data analysis and comprehension. That’s not only the vast majority of their day; it’s the vast majority of their career. This serves as proof that analytics solutions aren’t a luxury. They’re a core need of today’s finance teams.
The CFOs I hear from on the daily tell tales of their CPM needs moving from system governance, experience, and process-specific features to data integration, capacity, and advanced analytics. And while many of these CFOs know artificial intelligence (AI) is part of the answer, they’re ot exactly clear on what that AI can do.
In this article, I’ll level-set on the potential benefits of finance analytics and the qualities of best-in-class analytics — and what you should look for in an AI-driven solution. Let’s dive in.
What you’ll learn:
- The benefits of finance analytics
- How financial analytics work
- What to look for in a finance analytics solution —and how AI takes financial analytics to the next level
- Self-service AI-based analytics with CCH Tagetik Intelligent Analytics
The benefits of finance analytics
In the information age, gut feelings must always be substantiated by hard data. The trouble is that accessing the appropriate insight can mean parsing through a million-celled Excel spreadsheets or searching for a pinprick of information in a haystack of software-generated reports.
This is where financial analytics solutions come in.
Analytics is the promised land of finance professionals. By honing analytics, companies can tap into performance trends that can improve the financial health of their business, the impact of their strategies, and the success of their projects.
How financial analytics work
Here’s a high-level overview of how financial analytics work:
- Companies collect a lot of data about their finances. Transactional data, customer data, purchasing data, supply chain data, marketing data, sales data, tax data, ESG data, and operational data all fall under this umbrella.
- Analytics helps finance teams connect data details from different datasets to enrich or clarify performance analysis. Cross-functional data from around the organization — like operations, sales, ESG initiatives, marketing, supply chain, tax, and HR — can illuminate correlations and causation, while providing much needed context to performance results.
- A holistic understanding of performance gives companies a clearer, multi-dimensional picture of its health. Armed with financial analytics, businesses can make better choices around expenditures, cash flow, and revenue opportunities.
What to look for in a finance analytics solution —and how AI takes financial analytics to the next level
As analytics expert, Gary Cokins once stated, “Today, the need for analytics may be the only sustainable long-term competitive advantage.” Corporate performance analytics must strive to provide reliable, standardized performance data to improve and automate financial analysis.
Here are five financial analytics capabilities intrinsic to achieving this goal — and how AI can augment and improve the impact of each finance analytics task:
1. Data availability
Instantaneous data availability — the ability to pull any performance metric from anywhere across the organization — is critical for fostering agility in the finance department. But as data volumes reach epic proportions, traditional analysis methods and aging software buckle under the hierarchies, diverse sources, data types, and dimensions required to mane data and extract insights.
Data availability is a critical capability for organizations to successfully make sense of the oceans of heterogeneous data they’re dealing with. The financial analytics software you depend on should be able to surface various levels of performance data and speed.
How AI improves data availability in finance analytics
2. Data quality
Trusted, validated data is everything to the teams completing the last mile of finance. Accurate reports and analytics can only be achieved if the first mile of finance — close, consolidation, and planning — has strong data governance and control.
Finance analytics software should provide data quality safeguards via validation processes like anomaly detection.
How AI improves data quality in finance analytics
3. Data presentation
Data alone isn’t enough. Presentation shapes perception. Clear visuals drive data comprehension, insights, and alignment. When performance is illustrated, it’s not only illuminating; it’s persuasive as well.
CPM software is becoming more sophisticated in terms of its visualization and storytelling capabilities. Visualizations allow organizations to present performance data clearly and concisely. Charts, graphs, dashboards, and tables add much-needed visual context to the numbers' story, providing stakeholders with an at-a-glance understanding of performance.
How AI improves data presentation in finance analytics
Generative AI can instantly visualize performance data, trends, and KPIs.
4. Data accessibility
IT or systems specialists have long been the go-to resources for finance teams in need of complex performance analysis requests. They are the ones with the technical skills to navigate the multi-cube architectures needed to fulfill data requests.
Finance analytics software liberates finance teams by giving them the self-service tools to query databases, create reports, and build out dashboards — without data science knowledge, programming skills, or complicated syntax queries.
How AI improves data accessibility in finance analytics
Generative AI alleviates IT from the burden of coding reports by automatically generating reports, surfacing insights, and creating forecasts.
5. Data intelligence
Today, you’ll seldom find a discussion in tech that doesn’t involve AI innovation. Analytics are no exception. In fact, analytics are the prime candidate for AI-enhancement. AI’s primary strength is identifying, replicating, and using patterns in datasets to produce new information. This gives it an incredible ability to make connections within deep data reservoirs, automate repetitive processes, and generate predictions.
What’s more, generative AI is increasingly advancing to improve the way we can query datasets. It uses language to understand and pattern replication to produce everything from equations to text to visualizations to audio.
The day has come when analytics software is antiquated and subpar unless it has AI embedded in its functionality.
