How can Digital Intelligence add value in Finance?
Management of day-to-day financial operations, preparation of monthly operating statements, monthly financial performance analysis, cash collection, treasury management, budget, forecast and global reporting processes… those are just a few of the activities carried-out by controllers or management controllers.
Among the responsibilities mentioned above, the monthly reporting activities and forecasting are essential as they should enable to analyze and understand figures so decision-makers are free to take action. Analysis comes from the understanding of financial, non-financial, quantitative and qualitative data. This being said, most controlling teams either:
- Solely have access to financial data, and the complete data needed to enable relevant analysis is missing,
- Have access to different standalone data sources containing different data granularity levels and repositories.
In both cases stated above, analyzing figures and KPI is a daunting task. Even if data is available, the time needed to reprocess and standardize it surpasses the reporting time constraints. Furthermore, most IS Architectures that are currently in place have not been revisited for the past ten years and are no longer aligned with top management needs: transversal KPIs, – leading indicators preferred to lagging - more data (internal or external), shorter reporting periods, and last but not least enhanced visual reports for top management executives.
“Information” is a strategic element at the heart of any business, however business and finance leaders are now facing a major problem: how is it possible to collect, exploit and analyze data faster to make the right business decisions in a limited time?
Bringing Digital Intelligence to Finance at last
Advanced Analytics, Artificial Intelligence (AI) and other technologies such as Robotic Process Automation (RPA) have applied to many back-office functions, however Finance has been one of the most recent business functions to benefit from them.
For instance, financial services, in particular – i.e. banking, risk management and trading - have already adopted Big Data and Analytics to generate better investment decisions along with consistent returns. In order to maximize portfolio returns complex mathematical models are built and generate vast historical data needed to find new areas to innovate or to simply cut costs.
However, the Financial Services sector is only the tip of the iceberg, and these technologies can easily be applied to Finance on a larger scale, and more specifically to controlling which is a position that needs to have access to widespread data to fulfill its mission. Through advanced technology, controllers are able to go beyond the usual reporting needs and begin to start collecting and analyzing data from other business departments - such as sales, HR and marketing – to enhance the existing data and improve analysis and troubleshooting.
As simple as this observation might seem, organizations are more inclined on improving reporting capabilities than creating the necessary transversal data and associated tools needed for quick decision-making. Why?
Traditional Architectures cannot fully answer the Current Needs
Stovepipe data systems are still the norm in a majority of companies. The past IS Architectures that many companies are struggling with today were built through a compilation of business needs per back-office or front-office function. Each system invested in usually answered a sole requirement for a specific business, however the big picture was ignored. Those architectures were only aligned with the second-level operational view and not the main strategic business view coming from top management. Companies who needed to know who their customers were invested in a CRM, without even possibly brushing the idea that information contained in that CRM could be beneficial to other departments or that data from other systems could also benefit the CRM.
Today, controlling teams need to have access to different types of information for an analysis to be relevant, information which will potentially come from a CRM, a procurement tool, operational applications, a cash collection application, an ERP, etc.
As an illustration, below are a few questions that could require answers from a controlling team in a Facility Management company along with the data elements and sources needed to initiate analysis:
- What urgent measures than can be taken on client sites?
Data analysis : Gross Margin (Finance) and Payment Terms (Operations)
- How can we detect the viability and the profitability of an upcoming contract?
Data analysis : Sales Margin (Sales) and Payment Terms (Operations)
- How to improve customer service and customer experience?
Data analysis: Credit Note follow-up (Finance), penalties on-site (Operations) and Satisfaction Surveys (Marketing)
- How to improve the sales offer and raise awareness amongst the sales teams in regards to on-site operations?
Data analysis: Sales Margin (Sales), versus Gross Margin (Finance)
- How is it possible to improve budgeting on seasonal activities (i.e.: snow removal)?
Data analysis: Financial data – actual/forecast (Finance), versus external data (past weather data)
In order to be able to answer the questions above, and beyond the fact that data needs to be exposed, it also needs to be cleaned and harmonized. For example, in regards to question #4 the data mentioned comes from two different sources: CRM (sales margin) and ERP (gross margin). In order to cross both KPIs, the data must be understood the same way by both departments sharing the information. Data catalogs can help structure this view by offering searchable business glossaries containing data sources and common data definitions.
The same goes for question #5, in businesses with seasonal activities that are difficult to forecast, adding external data to the mix is a way to gain accuracy on budgets. For example in regards to “snow removal” activities in countries where this is a main seasonal factor, it is complex to forecast costs and turnover. Costs range from salt, special machinery and employees for that specific time of year; the variable being the snow density and the length of the winter period. Understanding and incorporating external weather patterns into predictive analytics during budgeting and forecasting will improve operations on site and the financial analysis.
However, as simple as the task may seem, adapting an old architecture to new needs takes time, investment and a whole new organization tailored to be data-driven and trained for data ownership.
Focus on Business Intelligence: a First Shot at Data Analysis
At the far end of each system rests the business decision-making tools – first generation BI - that are generally handled by a majority of controlling teams; and let’s face it, widely used as a reporting generator.
In Finance, data warehouses coupled with Business intelligence (BI) tools were a must have over ten years ago. Since BI has appeared, it has become a real strategic element at the core of any company, and traditional BI has revolutionized the way companies treated, analyzed and viewed information.
However the first generation BI tools were missing the following points:
- These solutions generally remained at the hands of IT teams and were not as easily approachable for finance as they required specific technical skills,
- Manipulating or building ad-hoc reports was merely impossible due to the fact that the data model was not built for a financial audience.
In recent years, BI has gradually given way to “self-service” BI and to Data Visualization tools. “Self-service” BI takes into account all of the points mentioned above – easy access for end users and a real understanding of the data model. Data visualization enables to see data through a storytelling approach, making figures easier to access and to comprehend by various audiences.
Business Intelligence and Data Visualization tools are however at the far end of every Information System. Where BI used to rely on data warehouses to store data and create and diffuse effective business reports, changes in the Finance role are leading users to need to have access to other types of data - non-financial, qualitative and / or unstructured. The two latter types of data require advanced technology and calls for data lakes as the deep analysis provided can directly support business growth and improve effectiveness.
Management Control: Becoming a True Business Partner through Technology
Controllers seem to have moved from organizational facilitators to only executing new needs arousing from management requirements such as new or improved performance indicators and daily optimizations. Indeed, the management control function is very regularly in "reaction" to events rather than in "anticipation" to them. This means that beyond the current financial reporting needs which are dear to management, the position must evolve to take into account the changing environment such as new competitors, increased client demands
Businesses are waiting for tomorrow's decision-makers to be able to cope with the advantages offered by Big Data, as data is gold mine for any company as it encloses an economic value for any company willing to go further. This value could be: creating innovative services, optimizing operational models and finding new positions on the value chain. The use of data can also bring benefits to a more operational level in the organization based on the daily work and daily decisions. For example, in a Facility Management company, an analysis of credit note types through a clustering model could generate groups containing similar data based on specific criteria. The clustering model would lead to a better understanding of quality issues on site and helping finance or operations take the necessary corrective or predictive measures.
In order to become a full business partner, controllers need to dispose of:
- A full understanding of the company’s business and strategy,
- A relationship with all business functions to be able to work hand-in-hand,
- Leading indicators versus lagging indicators,
- Automated processes for increased efficiency,
- Transversal data; data which is up to date, relevant and reliable,
- The right data processing and the right tools to access and visualize data.
It is clear today that without new technology derived from digital technology the role of management control will remain limited to "data crunching" and analysis will be narrowed down to the release of new performance indicators coming from non-stable financial data. Furthermore, that strategic business partner position desired by all management control actors, is only possible if the company is sufficiently mature and armed to design its strategy and align its information system accordingly.
The arrival of new technology in finance brings solutions to process unstructured data, and makes it possible to analyze them with much greater ease, which effectively reduces the information processing time, allowing to retrieve information produced in real-time. It is also made possible to remove data silos existing in the information system to allow the various services to interconnect and to strive towards a common goal for the organization; namely a common business vision. Whether it’s cost cutting, developing new products or gaining more insight on customers, the benefits are invaluable.
Moreover, the value directly brought to controlling by the addition of qualitative data is unequivocal: business as a whole can finally be understood. Controllers are therefore free to influence and predict tomorrow’s strategy. However, it is up to every team to qualify the needs for such a project, nevertheless the organization must be mature enough to initiate it.
Beyond any system, human skills still remain at the foundation of a proper understanding of business and its technicalities. The tools available such as Data Visualization or Artificial Intelligence, will remain today the facilitators of the organization’s, but are key to reaching the next step in Data Analysis for Finance.