From Hindsight to Insight: How Finance Teams Are Relying on Predictive, Not Historical Data to Deliver Business Value

4 mins read

17 January 2023

Joe DosSantos, Chief Data and Analytics Officer at Qlik

Finance teams have spearheaded enterprise analytics’ evolution since its inception, initially focusing on reactive and descriptive analytics relating to financial performance, inventory management, and treasury holdings. Today, we are looking at more predictive and prescriptive analytics supporting risk, credit, and financial business modelling. Often called Active Intelligence, finance teams are looking to use real-time, up-to-date data to inform decision-making in the business moment.  

Strengthened by the progress in artificial intelligence (AI) and digitisation, data will only continue to grow and evolve. The new working practices it enables are seen across many organisations today and support cross-function collaboration for richer insights. Finance teams, for example, are increasingly working with marketing to understand early-stage buying signals that could reduce the cost of acquisition and grow the lifetime value of customers.   

The role of finance within the business is transforming. Financial leaders are now business partners that are not just informing but instigating the individual moments and micro-decisions that help transform the business in real-time. 

However, this transformation necessitates changes to mindsets and working practices. Chief Financial Officers (CFOs) and financial leaders must quickly adopt and instil an active approach to data within their teams so that they are ready to seize on the growing data opportunity. 

Consolidating Data To Deliver Better Experiences

In today’s evolving landscape, the traditional month-end analysis to identify trends or incidents that could affect the profit and loss statement would be redundant. As incidents occur, organisations need to be confident in making real-time, data-informed decisions.  

With an end-to-end data analytics platform, finance teams can combine multiple, complex data sources to analyse financial performance, develop forecasts, and run flexible economic and financial simulations. Bringing together data from various sources with the continuous integration of data across an organisation provides complete, accurate, and up-to-date datasets.  

Unifying data has been tricky, particularly data from Systems Applications and Products in data processing (SAP) and other Enterprise Resource Planning (ERP) software solutions central to most organisations’ operations. An active data analytics platform solves that problem by unifying siloed data and delivering complete and accurate financial analytics insight. As a result, CFOs can now dig deeper into the data and explore expense, procurement, and contract data to discover the actual cost of doing business and identify new ways to reduce costs and increase profitability.  

CFOs can also compare forecasting with actuals in real-time for ongoing trend analysis and to accelerate closing at period-end. For example, the finance department at Bajaj Auto, a leading multinational automotive manufacturer, has extensively performed cost analyses and assessed how individual products perform with an active data approach. In addition, by being more agile, the finance team can alert lines of business when action needs to be taken rather than reporting on an event after the fact. 

The Power Of Machine Learning For Sales Forecasting 

Machine learning (ML) helps augment many data analytics tools to perform the abovementioned capabilities, thanks to the cloud and its limitless computing capability. McKinsey finds that 20% of C-level executives now use machine learning as a core part of their business.  

ML technology does not require heavy investment in specialist expertise. Simple to use, code-free solutions can integrate ML technology into predictive models by automating model generation and testing business scenarios by efficiently connecting data and identifying key drivers. The models are trained on potentially large data sets and learn from patterns often indiscernible by humans. But its real value lies in its ability to provide detailed insight into key drivers and inform more accurate sales forecasting.  

Set Up Alerts For Efficient Bill Payments

With an end-to-end data analytics pipeline, CFOs can set up alerts that spot outliers and anomalies in data, notifying in real-time to take action. Business users can also create self-service alerts directly, which can then be centrally configured and managed for more widespread distribution across the organisation. These alerts in the digital world can also alter the customer or partner’s behaviour and habits by promoting products based on buyer preferences. 

Alerting can help reduce bill payment delays to make billing and accounts more efficient. CFOs and their teams can set up thresholds and alerts for in-the-moment monitoring of spend to avoid budget derailing and compel action.  

The Time Is Now – Act At The Business Moment

Using the same mindset with unifying data, finance teams can no longer work in silos away from the wider business. As CFOs have a firm place at the boardroom table, they must make good use of the business data to improve the bottom line. Accurate, relevant financial reporting reflects the business at the moment. It demands a far more agile and active relationship with data – delivered by AI-driven analytics.  

Gone are the days of static quarterly and yearly forecasts. Instead, businesses must make decisions based on continuous real-time insights from enterprise analytics platforms that use complete data for far more accurate financial analytics insights.  

Becoming active with data means becoming engaged in today’s fast-paced digital economy. This is where real change comes. So put the finance team at the heart of success now and in the future.