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Generating inter-company and inter-business synergy with data utilisation
New trends in data and technology utilisation
In this highly unpredictable age of VUCA (volatility, uncertainty, complexity and ambiguity), it is becoming more important and valuable than ever to be able to create inter-company and inter-business synergy by using new technologies for data utilisation. The introduction of data utilisation technologies differs greatly from that of conventional IT systems because the emphasis is on the value of the data itself. This difference is a key point in digital transformation (DX) strategy, and provides a means of transforming to new business models.
The inter-company and inter-business synergy that a company generates through data utilisation enhances the speed and quality of management decisions, bringing the company an overwhelming competitive advantage. Therefore, this also represents a major threat to companies that do not utilise data. Moreover, because data utilisation can also contribute significantly to higher business value, we can expect to see more and more examples of effective data utilisation not only among companies seeking new growth drivers and management sophistication but also by companies looking to engage in business revitalisation, M&A and business restructuring.
Factors in a data-utilisation model for decision-making
The generation of inter-company and inter-business synergy through data utilisation differs from conventional management strategy and decision-making in that it is centred on data- and algorithm-based analysis rather than analysis that depends solely on past experience and intuition.
Companies looking to make their management more sophisticated and complex or to expand the scale of their business must engage in highly precise decision-making. However, as they do, their executives and business managers encounter many issues that they cannot fully resolve by relying on their on-site and management experience and intuition alone. This situation can lead to slow progress in their efforts to optimise management resources and to declining competitiveness.
Anticipated benefits
Building a data utilisation environment makes it possible to automate the collection of financial and non-financial data during business operations and eliminates the need for the conventional process of inputting data manually. It also makes analytical operations, such as preparing forecasts, more efficient. Reducing the amount of time and labour hours needed for this series of tasks can help speed up decision-making on important management matters such as the consideration of policies and measures, thereby creating more opportunities to examine the available options. This helps improve the accuracy of data and demand forecasts used for considerations, and can lead to advanced insights for better management decisions.
Examples of improving management and operations through data utilisation
- In business management and planning
Data utilisation enables highly precise forecasts of corporate finances (such as in quarterly results and investment returns for individual businesses) by enabling a dynamic understanding of not only conventional management indicators but also the leading indicators that drive them. Such forecasts accelerate management decision-making pertaining to matters such as the reallocation of management resources to individual businesses. - In marketing
Data utilisation promotes greater sophistication and precision in business management by enhancing demand forecasts through the use of third-party data in the unified management and analysis of customer data. Specifically, it enhances the benefits that customers bring to the company (i.e. their ‘lifetime value’) and lowers contract cancellation rates. In manufacturing, data utilisation can help to improve reorganisation and hiring criteria, and can make it possible to examine new services and business development by utilising data for the formulation and re-examination of manufacturing department production plans. - In supply chain management (SCM)
Data utilisation can improve cost management through more advanced supply-chain analysis based on the centralised management of manufacturing divisions’ IoT data and of sales- and customer-related data. For example, you can improve the accuracy of demand forecasts by using AI analysis in combination with external information, such as weather information.
Structure of the data utilisation management model
The data utilisation management model used to generate synergy can be divided into four processes, from data collection to management decision-making. This model creates an environment that enables the use financial information and non-financial information* whenever it is needed by collecting data from each department in a timely manner, accumulating it in a data lake, and managing it centrally. Using statistical means, this model translates the realities of the accumulated data into visual form and then uses AI algorithms to generate models of the causal relationships and correlations that exist within the data. Finally, it conducts objective analyses of the data, attaches meaning to it, and then outputs it in a form useable for more advanced management decisions.
*Non-financial information: Information that is not included in financial statements, such as information on accounts held by marketing departments, lifetime value, size of operations staff (full-time equivalents) etc.
Issues related to generating synergy through data utilisation
To generate inter-company and inter-business synergy through data utilisation, you will need to build the necessary environment for data-based decision-making and organisational restructuring.
Setting up a data-utilisation environment and organisational structure
To execute data-driven decision-making and management decisions, generate inter-company and inter-business synergy to increase profits, and achieve continuous growth, it is essential for management to encourage cross-organisational data utilisation. This requires an environment that includes data infrastructure capable of collecting, accumulating, analysing, and visualising data as well as an organisational structure that can launch and manage this environment. It also requires the ability to arrange and fully apply this environment and organizational structure at the group, company and department level.
Two approaches for transformation into a data-utilization environment and organization
The process of transforming your environment and organisation into one capable of generating inter-company and inter-business synergy through data utilisation begins with a study on whether to adopt a company-wide approach or a department-led approach. This study should take into account such factors as corporate culture, organisational structure, and the status of associated projects. With a company-wide approach, an environment and organisation for cross-company and cross-business data management is established under company-wide control, and data analysis and modelling capabilities are reinforced based on this environment and organisation. This is the ideal approach, as cross-sectional data utilisation can be expected to deliver greater synergy, and it should be applied when management’s intent is clear. On the other hand, with a department-led approach, a limited number of departments take the lead in data utilisation, and data utilisation is then expanded to the entire company if the prospects for value creation are good. The fact that work begins at the department level makes it possible to start at a relatively low cost. This approach is suitable when the concept of data utilisation already exists within an actual project or when management’s policy is to ‘start small and grow large’.
The difficulties of transforming your environment and organization
Although neither of the two approaches to transforming your environment and organisation into one capable of generating inter-company and inter-business synergy through data utilisation differs greatly from conventional system infrastructure conceptualisation projects, the path to such transformation is not an easy one. If the organisational system required for data utilisation is not sufficiently established, your efforts may meet with many obstacles.
Introducing such an environment and organisation requires the establishment of a system for preventing or mitigating the issues and risks that can be anticipated with such a project, or for implementing countermeasures against them. When establishing such a system, you should pay attention to the following points.
The project approach
The following project approach is typically used for transformation to a data utilisation environment and organisation for generating inter-company and inter-business synergy. Although the key tasks do not differ greatly from those of general operational support and IT introduction projects, the development of management models for making data-based management decisions requires repeated modelling and hypothesis testing at the design stage.
How PwC can help
Introducing new data and technologies into your business model requires reliable emergency response measures based on the thorough management of prerequisites and the presentation of a strong vision for regrowth.
The PwC Japan Group can provide you with end-to-end support covering everything from reviewing your existing business plans to helping you formulate and execute regrowth strategies. To do so, we aggregate the collective strength and the vast store of knowledge of our firms and the firms of the PwC global network. This strength includes know-how relating to business feasibility assessments, business planning, and new business concepts that we have cultivated through M&A-related decision-making support provided to our clients. It also extends to our ability to gather information quickly by fully leveraging our global network, our relationships with various stakeholders in the public and private sectors, and the knowledge of our member firms in such areas as accounting, taxation, legal affairs, risk management and technology.
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