The Depreciating Value of Data
Posted by Sjoerd-Jaap Westra
CFOs are increasingly being called upon to inform, support and contribute to upper-level strategic decision-making, and in many businesses, financial analysis has replaced financial reporting as the main priority of the CFO.
This change must be reflected in the methods and tools CFOs use. While financial reporting looks back, stating facts about what has happened, financial analysis for strategic decision-making must always look forward, using current data to consider what might happen in the future.
This shift brings with it a change in attitude. Financial reporting was about meeting key deadlines, whether annual, quarterly or monthly. But using financial analysis to aid real-time decision-making requires data that is up to date all the time, not just at certain key moments. It is not enough for data to be just-in-time, it now needs to be real-time.
Why Is Real-Time Data Important?
A piece of data that is truly "real-time" should accurately reflect the world that it models. To make an effective decision in real-time, you need accurate, real-time data.
A piece of data that is correct for today may no longer be relevant, useful or correct the next day. In some industries, the effect may be even more extreme: A piece of data that is correct at 9 a.m. might be considered out of date by 10 a.m.
It does not matter how good your decision-making is if that decision relies on out-of-date data. Businesses that do this are relying on the assumption that what was correct when that data was measured is still correct today - this can be expensive.
The Problem With Delayed Data
Millions of people rely on the real-time navigational information that sat navs provide to get from A to B. But what if, instead of providing real-time information, the directions the sat nav gave were five minutes behind? Instead of showing a driver where he is now, the sat nav would show where he was five minutes ago.
Every time that driver reached a junction, he'd have to make an educated guess about the direction to take based on out-of-date information, and then five minutes later he'd discover if he was correct or not. Sometimes he'd get the decision right, but often he'd get it wrong, and he would suffer consequences for this poor decision -- in this case, a delay to his journey.
Decision-makers aren't truck drivers, but they can be misled by poor information in the same way. The difference is, instead of a wrong turn costing 10 minutes of time, it costs a business millions of dollars.
Achieving Real-Time Data
Collecting and using data requires three steps:
- Data is collected and reported.
- Data is manipulated and analyzed to provide intelligence.
- Intelligence is delivered to decision-makers.
In the past, each of these steps involved manual work, significantly slowing the process. Data was collected manually, analyzed manually in Excel, and then delivered through a report or PowerPoint presentation. The more staff a business had working on its data, the faster the process, but it would never be real-time.
Today, business data does not need to languish in the past. The latest financial systems can speed up the process dramatically, allowing businesses to access real-time data. Additionally, new advances in artificial intelligence are delivering significant boosts to data analysis.
Modern Financial Systems Enable Real-Time Data
The latest financial systems, such as Unit4's Office of the CFO suite, enable an organization to access a definitive, real-time view of its finances.
The ability to integrate seamlessly with other key systems means that data is automatically delivered quicker than could ever be achieved manually. This data can be easily manipulated to deliver custom reports to different decision-makers, according to their needs.
Centralized financial data on one user-friendly system allows CFOs and other users to visualize key information, improve cash flow planning, and create complete, accurate reports based on real-time data.
The Growing Role of Artificial Intelligence in Financial Decision-Making
Over the next few years, businesses will increasingly look to AI to transform the speed, accuracy and intelligence of their data analytics and manipulation. Artificial intelligence can be used to analyze and validate large volumes of data and then automatically use that data to create insightful commentaries and reports using advanced analytics and predictive modelling.
All of this can be achieved in a fraction of the time and cost it would take a team of humans to achieve the same result. These advances will free up the CFO and other key finance employees to focus on using the data, rather than just creating it.
Real-Time Data is Essential in Competitive Markets
The advent of big data has shifted the battleground. Businesses previously sought to gain a competitive advantage by tracking more data -- but the amount of information available is now so vast that the true advantage comes from how quickly and accurately you can use it.
Real-time decisions based on real-time data are now a possibility, and the addition of AI means even the most complicated calculations and analytics can be made available faster than ever. Increasingly advanced financial systems create the potential for a huge data-led competitive advantage over competitors with inferior tools and techniques.
The conclusion is clear: Businesses relying on manual or semi-manual solutions that produce out-of-date data must upgrade their systems or risk being out-competed by rivals with better data-driven decision-making.