September 6, 2021

Digitisation Guide - The digital transformation of controlling

A guide to the digitalisation of controlling. Discover what is coming in the future and how you can successfully manage this transformation

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This is part eight of our series "Digitalisation 2021". In the previous articles, we looked at digitalisation in sales, IT security, marketing, logistics, finance, procurement and manufacturing. This article is about digitalisation in controlling. Hint: Our article "Digitalisation guide - finance and the digital transformation" contains several overlaps with areas of controlling and additional information.


Expertise in MS Excel and passive reporting are a thing of the past. The controllers of tomorrow are the architects of automation and are masters of business intelligence tools and holistic data management (data governance) in companies. Artificial intelligence and machine learning are removing the old burdens in controlling, freeing up resources that are urgently needed elsewhere: advanced data collection and data analysis, management information design, such as the design of dashboards for fast information capture, and, last but not least, faster business decisions based on real-time information raise the bar by more than one notch. Welcome to the digital transformation of controlling.

The evolution of controlling

First, let's look at the evolution of controllers. Their role changed in the late 1960s. Until then, the main task had been the calculation of costs in production plants - i.e. how much money was spent. In the 1970s, profit and profitability analyses and participation in decision-making processes joined the ranks of the new tasks.

Innovations in the 1980s and 1990s led to further changes. International business increased. Globalisation brought about a change and controlling expanded with new areas such as value creation processes, stakeholder analyses or benchmarking. This also changed the way of thinking from "operational" to "strategic", and from then on external data and non-financial information were incorporated into controlling in order to make strategic decisions. 

In summary, controlling evolved from pure data collection to internal consulting, with a focus on change management.


Fig. 1: Evolution of Controlling – Digital Transformation & the Changing Role of Controlling in Business Enterprises | Tolga Tuzcuoglu, Robert Bosch Turkey


To support senior executives and management in their decision-making processes, controllers need the appropriate soft skills and interpersonal skills. This includes the ability to influence and convince people, negotiation skills and, of course, excellent communication with all stakeholders. Fig. 1 shows this change in controllers who now need social skills in addition to technical skills and analytical and conceptual thinking in order to make change possible.


Fig. 2: Utz Schäfer, Jürgen Weber – “Die Digitalisierung wird das Controlling radikal verändern” in Controlling & Management Review


The digitalisation of controlling

Like any digitalisation, the digital transformation of controlling also involves several points (Fig. 2). Utz Schäfer and Jürgen Weber presented eight challenges in December 2016.

  1. Data Management
  2. Self Controlling
  3. Agile business management
  4. Efficiency in controlling
  5. Analytics
  6. Business Partnering
  7. New skills
  8. Controlling Mindset

In summary, one can say that the effects in controlling particularly affect the processes as well as the organisation itself.


Christian Langmann summarises the central areas of transformation under four headings in his book "Digitalisation in Controlling", published in January 2019:

  1. Big Data Analysis
  2. Business Analytics
  3. Robotic Process Automation (RPA)
  4. Machine Learning

These four areas represent the current technological heart of the digitalisation of controlling. At the same time, these areas also determine the future of controlling.



Big Data and Big Data Analysis

As we already described in our "Industry 4.0 / Internet of Things" article, the global amount of data is growing rapidly. Between 2018 and 2025, the amount of data will increase fivefold (Fig. 3).

(Fig 3: IDC Data Age 2025)


More than ever, companies must be able to collect this mass of internal and external data and prepare it for analysis. This comes under the first point "data management" highlighted by Schäfer and Weber (2016) and also describes data quality, which is a prerequisite for data analysis.

The challenge is the collection and processing of external data, as this data can often represent huge amounts of data (big data) and is also usually more difficult to control.


When properly analysed, external data can be used for financial forecasting as well as for discovering market trends. Furthermore, an accelerated and optimised decision-making process is hoped for, as well as better and more precise (management) reports and analyses.  In the best case, new services or business areas can be discovered. This requires controllers to have the skills of data scientists and to be trained in statistics, regression analysis, and other analyses of data evaluation.

While internal data from the ERP tool (Enterprise Resource Planning) used to be evaluated, the digital transformation is expanding controlling to include external information.


Business Analytics

Business analytics uses various statistical methods such as regression analyses, decision trees or neural networks. These are also intended to support and optimise decision-making processes in organizations. Furthermore, predictive analytics are used, which are also used in the digitalisation of sales, among other things. By means of statistical techniques, forecasts are made about future trends, values or conditions, which influence decisions in companies accordingly.

Data visualisation also belongs into the area of business analytics. Visualisation is usually done by so-called dashboards, which help to understand and comprehend data. For instance, to detect trends or to understand behaviour. It is particularly useful in the area of Big Data - traditional data illustration such as tables, diagrams or simple illustrations have their natural limits in visualisation and are not suitable for complex issues or huge amounts of data. This is where new methods such as heat maps, stream graphs or tree maps come into play.


Robotic Process Automation (RPA)

RPA can be considered a "classic" use case in controlling. It helps to carry out repetitive, administrative tasks. In doing so, the program imitates humans in the execution of a task. In the area of controlling, this includes in particular reporting as well as closing processes at the end of a month or other time intervals. It is also used in processes from invoice to payment receipt, where invoices are checked, booked and generally managed or payments are released.


Maschinelles Lernen (Machine Learning, ML)

Machine learning (ML) is a sub-field of artificial intelligence. Machine learning is the ability of a machine to continuously improve its own performance without the need for human intervention to explain to it how to perform its tasks.

Among other things, ML is used for predictive analytics, and with it, forecasts of EBIT plans and P&Ls (profit and loss statements) are calculated and also assist in budgeting. ML is also frequently used for cash flow planning. In this case, the payment behaviour of customers is processed and calculated on the basis of various information, such as payment history or data from social networks, etc. Over time and with each run, the ML algorithm improves and thus the predictive power.



The future of controlling

Controllers are often compared to air traffic controllers. Their job is to monitor, to guide, to give orientation and also to warn. Digitalisation is changing both the playing field and the circumstances of this profession.

Employees in controlling are now developing into "co-pilots" and business partners. Their role will become more proactive in the future. They will no longer act in the background, but develop into active agents of change. Their rights and duties are very close to those of business partners, as appropriate business insights are necessary to actively support critical business decisions.

Moreover, in the age of data, the skills of data scientists are also becoming increasingly important. In addition to evaluating data correctly (see Big Data and Big Data Analysis), this also includes data governance. Data governance refers to the holistic management of data. Guidelines, processes and methods are set up to ensure the quality, integrity and security of the data. It also includes the fulfillment of legal requirements (data compliance). Data governance is of central importance in the digital transformation. Depending on the size and complexity of the company, data scientists are employed and work closely with the controlling department.

With many traditional core tasks being automated, such as reporting through Robotic Process Automation, controllers are now increasingly acting as digital service providers, helping management with access and supply of services and data. This includes automatic forecasts or self-service offers, where management can obtain information at any time and on the move. This information should be easily accessible, secure and, of course, relevant and up-to-date.



Conclusion

So how do you implement the digitalisation of controlling?

As always, digitalisation is a holistic process. First find out how far along your company is in the digital transformation ("digital maturity"). Then define your vision and your goals. The goals must be tangible, the overall vision comprehensive and the path to the goal detailed. The details often determine success, as is so often the case. If departments and processes dovetail in this process, the full strength and power of digitalisation unfolds. Inform yourself about available solutions and find out what works for other companies and can also be relevant for your transformation. Identify internal and external stakeholders and build a close relationship and collaboration with them. Keep in mind these are win-win relationships, because insights are shared and together you grow as digitalisation progresses.

Start small and scale up at the edges. Add complementary tasks to controlling that lead into neighbouring areas and make sure these initiatives are aligned and stay connected. Focus on single use cases and prove that it works. To prove that it works, use the agile approach of "fail fast" to create a Minimum Viable Product (MVP). This enables rapid implementation and iterative optimisation in the process. Successes build trust with stakeholders and digitalisation scales from here.


Lastly, it remains to say: Get started!

Or to put it in the words of Deloitte: Think Big. Start Small. Act Fast.


And if you need further support and expertise for your digital transformation, we will find your experts.