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.