• Joe DosSantos, Chief Data and Analytics Officer at Qlik


• 05.07.2022 03:15 pm
#data

The finance department has been leading the charge for enterprise analytics for over a decade. What started as reactive, descriptive analytics relating to financial performance, inventory management and treasury holdings has now evolved to more predictive and prescriptive analytics that support risk, credit and financial business modelling. The latter often called Active Intelligence, gives finance teams the ability to look back at data but to inform real-time decision-making in the business moment. 

Powered by two significant trends – artificial intelligence (AI) and digitization – this enhanced relationship with data will only continue to grow and evolve over the next decade. And the new working practices it enables are already taking shape in many organizations and are supporting greater collaboration between departments 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.  

This is transforming the role of finance within the business. From an after-the-fact analyzer that assists with macro-decisions, financial leaders are now active business partners that are not just informing, but instigating the individual moments and micro-decisions that help transform the business in real-time.

But this transformation necessitates changes to mindsets and working practices – something that CFOs and financial leaders must quickly adapt and instil within their teams so that they are ready to seize on the growing data opportunity. There are three clear places where CFOs can start to quickly benefit from the real-time, context-rich insights that an active approach to data provided. 

Unifying data for more complete insights and action

Continually shifting consumer, economic and political demands means that the traditional month-end analysis to identify trends or incidents that could affect the P&L simply won’t cut it anymore. As incidents occur, organizations need to be able to make real-time, data-informed decisions that they feel confident in. 

With an end-to-end data analytics platform, finance teams can combine multiple, complex sources of data that can be used to analyze financial performance, develop forecasts and run highly flexible financial simulations – at the moment. Bringing together data from multiple sources with the continuous integration of data across an organization provides complete, accurate, and up-to-date datasets. 

This hasn’t always been easy, particularly when it comes to data from SAP and other ERP software solutions that are at the core of most organizations’ operations. An active data analytics platform solves that problem by unifying siloed data and delivering more complete and accurate financial analytics insight. CFOs can now dig deeper into the data and explore expense, procurement, and contract data to discover the real cost of doing business, and identify new ways to reduce costs and increase profitability. 

They can also more accurately compare forecasting with actuals in real-time for ongoing trend analysis and to accelerate closing at period-end. For example, the finance team can view the relative IRR of a top product line’s historical performance, and then instantly shift the view based on geography, distribution model, or other factors to gain deeper insight to mitigate future risks. 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.

Accessing machine learning for sales forecasting

Underlying many of these capabilities sits a technology that is increasingly augmenting many data analytics tools thanks to the cloud and its limitless computing capability – machine learning. In fact, McKinsey finds that 20% of C-level executives are now using machine learning as a core part of their business. 

While it may seem like a future technology that is out of reach of anyone except data scientists, it doesn’t in fact require heavy investment in specialist expertise. Simple to use, code-free solutions can integrate machine learning 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 that are often indiscernible by humans. But its real value lies in its ability to provide detailed insight into key drivers and why predictions were made to inform more accurate sales forecasting. 

Reducing bill payment delays by alerting

With an end-to-end data analytics pipeline, CFOs can set up alerts that spot outliers and anomalies in data, letting them know in real-time if an action needs to be taken. Business users can also create self-service alerts directly, which can then be centrally configured and managed for more widespread distribution across the organization. And, increasingly, these alerts in the digital world can alter the customer or partner behaviour, much in the way that Amazon proposes products based on buyer preferences.

Alerting can also 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. 

Driving change in the business moment

Just like the data that they use, finance teams can no longer work in silo away from the wider business. CFOs have a firm place at the boardroom table and it’s crucial they 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. Businesses need to make decisions now, based on continuous real-time insights provided by enterprise analytics platforms that use complete data for far more accurate financial analytics insights. 

This is where real change comes from – not last year’s finance report but context-rich data on where the business is today. Becoming active with data means becoming active in today’s fast-paced digital economy. It puts the finance team at the heart of success now and in the future.