Unsurprisingly, given that the digital revolution knows no bounds, it has inevitably impacted knowledge management. But what does knowledge management 4.0 – aka “digital knowledge management” or “knowledge management in the digitalisation era” – look like?
1. Digital technologies are changing knowledge management for tomorrow’s corporate tax departments
Major changes are always accompanied by uncertainty and a degree of risk. This article sheds some light on the subject. We have divided our article into three parts. We begin with an introduction to classic knowledge management. We then look at how knowledge management has influenced the tax sector and changed the operating environment. Finally, we present relevant new technologies and venture a look at what the new, digital knowledge management world will look like in corporate tax departments.
2. A brief introduction to (tax-specific) knowledge management
Knowledge management (KM) encompasses a range of disciplines including economics, management theory, IT and communications technology. Almost all industries also have knowledge managers or “champions”, who deal with knowledge processes either for the whole company or specific departments. As we see it, knowledge management provides relevant information and helps to improve workflows and processes within a company. It encourages innovation, identifies critical knowledge and gaps in knowledge,[1] and boosts productivity. Knowledge management can be used to define processes with a view to achieving long-term corporate objectives efficiently and cost-effectively. It also provides a basis for decision-making at management level. In addition, knowledge management provides a framework for evaluating and incorporating new information and expertise.[2] The framework ensures that existing knowledge is preserved and remains accessible even when employees retire or leave the company.
Nonaka and Takeuchi explain how the formation of organisational knowledge can be seen as a spiral process.[3] This process begins at the level of the individual and continues through expanding communities of interaction, gradually transcending cross-sectional, departmental and organisational boundaries.[4] In other words, contextual knowledge is initially created through conversations and personal design processes, then spreads within the company.
Knowledge management is often compared to the ideal database or corporate intranet, but it offers far more. A well-designed knowledge management system should create an environment in which it is easy for employees to save, locate and apply information that is relevant to their tasks and activities. The system should also ensure that information is exchanged across all levels – so, long-serving employees can share acquired knowledge with new arrivals. To this end, suitable platforms must be available. Good knowledge management also identifies and flags up cross-cutting issues and overlap. Specialist technical solutions can be presented to all stakeholders, thereby preventing mistakes.
There are obvious benefits for companies. New staff can integrate faster and will make fewer mistakes. Tasks, ideas and deliverables can be completed and implemented sooner. Sometimes, difficult issues can be solved more easily and efficiently through "lessons learned". The result is lower costs and a greater investment scope for the company.
Haddow and Greenfield argue that “Tax knowledge is critical to any tax practice, no matter how large or how small. Knowledge management is now a recognised science but in essence, managing tax knowledge in this context is about recognising it, gathering it and organising it…”.[5]
For example, taxation and tax law can have very tangible consequences for companies. The following questions arise whenever the legislation changes:
- How complex is the new legislation?
- What are the implications for our company?
- How should we interpret changes to the law and administrative procedure?
- Is further clarification needed in relation to the new duties and changes to be made?
- How much will it cost to comply with the new legislation?
Only when the insights gained are put into context through specialist tax- and case-related expertise, do we obtain knowledge that is actionable..
3. Digital transformation has changed the ground rules
3.1 Digitalisation of tax authorities
Digitalisation is changing the entire tax landscape, including the context in which knowledge management operates. Knowledge management needs to evolve in order to achieve the objectives set out in the first section. But, before we go into this in more detail, let us first take a brief look at the changed framework conditions.
By the second decade of the 21st century at the latest, digital transformation had reached the tax industry. Following the global financial crisis in 2007-2008 and the ensuing deep recession, many states were faced with falling tax revenues. In order to remedy the shortfall, efforts were made to combat tax fraud more effectively and close the tax gap. In the process, many tax authorities discovered the inherent benefits of digital technologies. The technologies offered better, more efficient and extensive control mechanisms. In the meantime, many companies and individuals had already gone digital for most of their business activities: communication, accounting, banking and contracts etc. Using digital technologies, tax authorities can now analyse the large volumes of data effectively and generate far more information than was ever possible in the analogue world. In addition, tax authorities are becoming increasingly less dependent on the information provided by taxpayers. By analysing third-party data from public sources such as patent and trademark databases, professional social media platforms and intermediaries (e.g. DAC6 reports), tax authorities can get an overview of the taxpayer’s activities. Advanced analytics algorithms can go much further in terms of risk analysis and identifying irregularities. Investigations can be expanded, moving from sampling to reviewing a full dataset, heralding a new era for transparency in the relationship between the taxpayer and state.[6]
However, national tax authorities do not take a harmonised approach to digitalisation, not even within the EU. At the moment, each tax authority defines its own digital standard – for example, stipulating the data format required for submitting information. Each tax authority has different priorities and has opted for different technologies in the process of digitalisation. As a result, companies encounter completely divergent risk scenarios in different countries – but these still need to be managed centrally. Certain countries are also seeking to tax the new business models found in the digital economy, adding further complexity to the legal landscape. Keeping track of this landscape is becoming ever more difficult and time-consuming.
3.2 New challenges for corporate tax departments
These developments represent a challenge for corporate tax departments, which need to manage the growing workload while also minimising novel risks and keeping track of the regulation jungle. What is more, these factors are subject to a permanent and undergone rapid change. It can be difficult to keep tax departments functioning efficiently in this volatile, uncertain and complex environment. At the same time, working from home, remote working and flexible working hours are becoming increasingly common. Tax knowledge management is a crucial factor here. In future, it will be vital not only to have specialist knowledge available in the company that can be accessed quickly and easily, but also centralised processing and analysis of all the information collected. The goal is not only to filter specialist tax knowledge, but all kinds of knowledge that could impact the company’s tax situation. This is the only way to guarantee the required efficiency and the task-related expertise needed to understand the upcoming changes and manage them well.
What does that mean in practice? In the final section of our article, we set out how particular technologies can take knowledge management to the next level, ready to tackle the challenges ahead.
4. Digital knowledge management for digital tax departments
4.1 Digital technologies
What is commonly described by the collective term "digital revolution" turns out to be a whole range of very different "technologies". For example, small-scale automation – also known as simple automation – mainly comprises extract, transform, load (ETL) tools and robotic process automation (RPA). The processes are designed to perform simple, standardised, rule-based tasks faster, continuously and without errors.[7] Intelligent automation includes technologies like machine learning (ML), optical character recognition (OCR) and natural language processing (NLP). This area focuses mainly on analysing large volumes of data to identify rules (patterns) or errors (anomalies), to make predictions based on past performance[8], and to recognise and process human speech (unstructured data).[9] Another big area of activity is producing visual representations of processes and large amounts of data. New visualisation tools enable companies to process data more efficiently and faster. This makes them easier for people to grasp and so provide new insights.
How can these technologies benefit digital knowledge management? We distinguish between three categories:
- Tapping into internal knowledge
- Processing external knowledge
- Providing data & supporting decision-making
4.2 Tapping into internal knowledge
For many reasons, the volumes of data flowing into tax departments have drastically increased in recent years. When combined with the state-of-the-art technologies mentioned previously, the data can support and significantly improve traditional knowledge management activities. Automation can feed data and information into tax decision-making and processes, thereby optimising outputs.
But, a number of conditions need to be met first. “Clean” data management is dependent on two essential factors: standardising processes and creating a common basic dataset. Only when this "foundation" is in place can the truly advanced technologies take effect. The aim is to convert knowledge and processes into structured data.
Standardisation encompasses both technical processes and knowledge acquisition. For example, identifying issues and cases with similar processes then establishing a harmonised workflow. To speed up these workflows, knowledge management can then produce templates or prepopulated forms that are managed centrally. All existing knowledge also needs to be collated systematically in a standard format. One possibility could be to use interactive chatbots, which regularly ask staff about certain situations and automatically process the answers using a particular format. Well-designed chatbots[10] would be more entertaining and stimulating for staff than filling in traditional questionnaires or adding to wikis.
Data management – storing and organising data – is just as important as standardisation. Newly collected data and existing knowledge in various databases, shared spaces or intranets must be merged and standardised in terms of format. We know from experience that this is a huge task and should not be underestimated.
Robotic process automation and ETA tools can be used to process and clean up data in both areas. Automation can simplify and speed up repetitive processes. Optical character recognition tools can be used to convert existing analogue information for digital use.
Once this basis is in place, algorithms can take on the task of supplying the relevant information, creating a whole new working experience for employees. For example, machine learning algorithms can derive rules from existing data, then classify and allocate content. In this way, the algorithms “learn” how to identify task-related information and forward it to the right subject matter expert. Painstakingly trawling through a wealth of different information sources will become a thing of the past.
With the help of Natural Language Processing, which is making great strides, even unstructured data sources with significant knowledge could be automatically evaluated and processed. Potential sources include the regular corporate email circulars, newsletters, tickers and other emails containing information.
From a purely technological point of view and from the singular perspective of knowledge management, it would be theoretically possible to extract relevant knowledge, for example, directly from communication or certain work activities (presentations and legal opinions etc.) of the employees. But because of its proximity to the monitoring of employees, this type of approach will often clash with other objectives, like data protection, and is therefore rightly criticised.
So, the human element must not be overlooked in this area. Motivation and upskilling are critical factors for practical implementation. Only if employees in companies and departments share the tacit and context-based knowledge (work experience, expertise and networks, etc.) that is often available, and are empowered and motivated by new, entertaining (keyword: "gamification"), easy-to-use tools and platforms, will digital technologies be able to create a new work experience. Let’s be honest, no one enjoys spending hours searching for a piece of information they urgently need. In this context, knowledge management can encourage agile learning and the acquisition of new and essential knowledge, perhaps by establishing digital learning management systems or creating incentives for microlearning in the workplace.[11] Change management has an important role to play here too and should be an integral part of a good knowledge management programme.
The human factor makes it increasingly complex to tap into internal knowledge, while in the categories described below, the difficulties tend to be linked to technological barriers.
4.3 Processing external knowledge
Companies have little influence over the format and type of information provided by third parties. Courts, tax authorities, parliaments, specialist publishers, external service providers and news agencies produce information in very different ways. Some permit connections via application programming interfaces (APIs), while others don’t. Everyone is (still) doing things their own way, which creates particular automation challenges in terms of gathering and structuring knowledge.
Knowledge management has some fairly clear objectives here: Ideal tools and platforms have algorithms that track newly enacted laws in different countries and clearly present the resulting tax-relevant changes. The same is true for analysing court rulings and their consequences. Automated reviews of essays, commentaries and similar contributions in the multitude of publications from relevant specialist publishers would also be worthwhile, with information for specific tasks being classified and passed to the relevant subject matter experts.
This kind of unstructured data is mainly processed using computational linguistics: natural language processing and understanding. This is a special application of machine learning where algorithms are used to process human speech. It is particularly difficult to deploy in the tax sector, where the algorithm has to understand the content of the text. While emails are classified as spam[12] based on a series of known patterns and features, for tax matters, an in-depth understanding of the content is required – and the content is often very complex. The semantics of the text have to be analysed, which involves identifying the meaning of sentences, clauses, expressions and wording. Although huge progress has been made recently in this domain, it remains a complex undertaking.
In the next step, predictive analytics is applied to historical information to make forecasts about uncertain future developments. For example, calculating the probability of a favourable or unfavourable outcome in a court case, tax investigation and other legal disputes involving the tax authorities. Resource planning can then be optimised and cases with little prospect of success discounted.
Given the technical complexities associated with this approach, many companies will wonder whether it would be better to use products from a highly specialised external provider, rather than developing an in-house solution. An agile market has emerged in recent years. In the tax sector, pure NLP providers compete against the big names in cognitive solutions, with insight platforms and specialist solutions from large consulting firms and smaller providers.
As explained in the previous section, centralised monitoring of the tax authorities’ digitalisation activities is also important. What progress is being made by individual authorities in which countries? What company data are public authorities able to gather? What non-corporate information sources are available? What capacity do they have for analysing large volumes of data? How do they decide which companies to monitor more closely and which to audit? What type of information do they expect companies to provide and in what format? Having a centralised understanding of the answers to these questions will be crucial as digitalisation continues. Only then can companies minimise tax risk, respond promptly to official requests and implement or dispute tax authority decisions effectively.
4.4 Providing data & supporting decision-making
Knowledge management can make a further valuable contribution to operational units and senior management. The information obtained through successful digital knowledge management can be made available to other departments for further processing.
Tax departments are often faced with the challenge of improving awareness of tax issues among internal decision-makers. Information collected in the context of knowledge management like the respective tax rate or the tax expense incurred in different countries, can be compared with other decision-relevant information like the sales ratio. New visualisation tools can produce easy-to-understand illustrations of tax analyses, simplifying the decision-making process for senior management. It is easier to simulate multiple scenarios using modern dashboards. What would the practical consequences be for the company if draft tax legislation were approved? What would happen in trade disputes if new penalties were introduced?
Incorporating tax information can also create new opportunities at an operational level. For example, understanding the tax issues associated with free trade agreements could feed directly into programmes used to coordinate supply chain management, thereby cutting costs significantly and improving performance. The automated collection and processing of internal and external knowledge can also feed into warnings, alerts and instruction manuals used in operational activities, which considerably reduces the compliance workload. This should make everyone happy.
5. Conclusion
In a digitally accelerated world – including the world of tax – with rapidly expanding data volumes, constant changes in the regulatory environment and ever more technology, knowledge management needs to provide vital support for corporate tax departments in the digital transformation process. Only then will they be able to overcome the upcoming challenges. Digital knowledge management constitutes a separate strategic business unit, the automated connecting element for knowledge from many different departments. Channelling and evaluating all information flows within a company and making them available in a structured form (as structured data) represents a massive challenge for humans and machines. However, it can be done with the assistance of a modern digital knowledge management system and associated programmes and tools, opening up new possibilities and innovative ways of working. There’s no need for concern though: ultimately the human element is still crucial.
“An investment in knowledge always pays the best interest.” Benjamin Franklin (1706–1790, one of the Founding Fathers of the United States)
