Data as a driver: five impulses for the industry

Whether in mechanical engineering, the semiconductor industry, electronics & high-tech or chemicals, pharmaceuticals and medical technology, data-driven intelligence in particular can create the conditions for successful growth in industry. So far, so good. The crucial question now is: Where does the greatest data potential currently lie that companies from the industrial sector should be exploiting for themselves? We have taken a look at the market and filtered out the five most important drivers at the moment:

Impulse 1: Use virtual assistants for optimized self-services

Particularly in industrial companies with increasing international competition, it is very important to increase as many efficiencies as possible. Virtual assistants and self-service approaches can provide excellent support here. Virtual assistants in particular provide support for complex tasks and ensure significant increases in efficiency in a wide range of work processes - from internal risk management (e.g. assistance with hacker attacks) to supply chain management (e.g. optimization of ordering processes and inventories) and legal support (e.g. support in preparing quotations). In B2C, generative AI is also increasingly providing support in the area of predictive maintenance.  For example, smart chatbots with automated round-the-clock support ensure greater individuality and speed in customer service. However, it is also clear that only the right data quality and data governance ensure reliable and secure data that can be used to implement trustworthy and maximally successful AI solutions.

This is how you implement this impulse:

• First check and strengthen your basic data, IT infrastructures and systems to be prepared for the use of AI. Technical blockages and data silos must be removed so that the assistant can work successfully.

• Measure the process maturity level in the affected areas or the entire organization to identify inefficiencies (preferably combined with industry benchmarks and orientation towards best practice approaches).

• Building on this, pilots for virtual assistants and self-service approaches can be identified and implemented. It is particularly important to find suitable efficiency-enhancing solutions for manual and/or cost-intensive activities.

• Ultimately, all relevant factors in the teams and departments should be considered for the final implementation - from the individual skills of the employees to the data basis and data architecture. By the way, we have the entire development story for our AI chatbot Dicobot here.

Impulse 2: Exploit B2B potential with data-driven product development and customization

Rising customer expectations and growing “Amazonization” or an ever stronger customer focus are also changing customer purchasing behaviour in the B2B context. Value creation for customers is therefore becoming increasingly complex and individualized. Against this backdrop, the development or improvement of products based on data is rapidly gaining in importance. For industrial companies, it is important to collect customer data that is generated when individual features are used. This can be used to derive potential improvements that address various objectives (e.g. customer value, cost reduction or (development) speed). In customer service, too, the best possible personalization of offers and recommendations determines whether potential customers can be activated.  The trend in B2B is moving towards self-service across all industries, i.e. direct ordering options without the need for sales staff to intervene. Overall, it is clear that companies with data-based decision-making generate significantly better sales.

This is how you implement this impulse:

• The first step is to identify which data is required, in what quality and whether it is already available in the company. Any gaps need to be closed with targeted measures.

• Challenge your own data-driven culture, i.e. specifically question the extent to which data is already being used as a decision-making aid and who is using this data and where - keyword data-driven decision marketing.

• Data silos and data pools must be eliminated and dashboards designed for the respective user groups in order to make the data easily available to everyone.

Impulse 3: Consolidate fragmented platform landscapes for further efficiency gains

Historically grown, often fragmented platform landscapes cost industrial companies a lot of money to operate and lead to difficulties in accessing and processing data. By consolidating the platform landscape, efficiency gains and thus an increase in competitiveness can be achieved. Data silos are a major hurdle for AI products in particular, as they benefit most from different data. The more consolidated the data is, the more insights can be gained that can be converted into monetary value using data-driven methodologies (e.g. more sales, fewer employees). In practical implementation, there is always a business and a purely technological perspective. For growing business, it is primarily a matter of interconnecting different business models in order to create important added value and lock-in effects from a central point of view. From a purely technical perspective, the focus is on linking central capabilities and core functions for several business models on one platform. Optimizing the cost structure is another key factor, as fragmented landscapes result in higher licensing costs and the need to develop costly interfaces between systems.

This is how you implement this impulse:

• Always start the optimization of the platform strategy with an analysis of the current platform landscape and a survey of the existing legacy systems.

• This primarily involves identifying redundancies, fragmentation and data silos.

• On this basis, the next step is to create a data architecture that is performant within the framework of the technological solutions and covers the respective needs of the users (e.g. through the integration of AI).

Impulse 4: Pave the way for data-driven sustainability

New EU legislation makes transparent reporting on energy consumption and emissions mandatory. The collection of sustainability data is therefore becoming extremely relevant for ESG reporting. And customers are also increasingly expecting companies to address sustainability issues. Data can be used to make transparent what is happening in the context of sustainability and where companies can make adjustments. In the end, this also pays off for the business - for example, in view of the high energy prices, data-based transparency can lead to savings in this area and clear cost reductions.

This is how you implement this impulse:

• The first step is to take stock of and reflect on all current sustainability initiatives that need to be optimized with data.

• Harmonized sustainability KPIs and their data-driven measurement give companies a clear picture of where they should start in optimizing their sustainability efforts.

• The focus here is not least on the introduction of suitable organizational structures, in which the management level must play a central role in order to break down existing silos and establish sustainable transformation as an integral part of the business strategy.

Impulse 5: Pay attention to ethical AI and responsible data management

AI systems can provide companies with data-based support in making the right decisions and driving economic growth. However, in order to exploit the full potential of AI, they must also be able to trust these decisions. This is especially true if AI is used in a business-critical area such as finance, logistics or research and development, or in a safety-critical area such as medical technology or driver assistance. Industrial companies should definitely take ethical principles and their corporate responsibility into account when developing and using AI technologies. What is needed is responsible and transparent handling of data that focuses on the protection of privacy, sustainable innovation and social trust in order to ensure fair and ethical AI systems.

This is how you implement this impulse:

• For responsible data management throughout the company, it is worth developing a separate ethics and data policy to increase acceptance and transparency for this topic. Change management involves an open feedback culture in which everyone participates.

• Training and further education are essential for employees to properly understand and implement the content of the policy.

• From a technical perspective, suitable measures should be implemented that enable encryption, data anonymization and special access management, for example.

• In terms of data quality, it is also important to regularly clean the data records in order to prevent or minimize distortions and inaccuracies - this also allows AI solutions to be used efficiently.

You can get more tips and information on the value-adding use of data in your company on October 16, 2024 at THE SESS10N, our free event in Stuttgart - with exciting speakers, co-creations and more. Discover how software, data, and AI impact global competitiveness, and learn strategies to maximize ROI on digital initiatives and drive digital value creation.