6 steps to a successful data strategy – proven strategies from over 100 Industrial IoT projects


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In a modern factory, data is not only crucial, but also the driving factor for efficiency, productivity, product quality and innovation. A clear and effective data strategy plays a central role here. With the following 6 steps, successful companies develop and implement effective data strategies that give them a lasting strategic advantage.


1. Define goals & create clarity

Defining and communicating clear objectives in the area of data management is the fundamental and decisive first step. Identify and understand the key challenges and requirements of your business units (e.g. realize sales growth with existing production systems). Derive targets for data management at company, business unit and operational level (e.g. standardized analysis of equipment effectiveness OEE). Define priorities and set measurable targets (e.g. global increase in OEE by 15%).


2. Analyze the status quo: Identify data landscape and key stakeholders

Start an analysis of your current data environment to uncover data sources, formats and types as well as potential integration challenges. First, limit yourself to OT and IT systems that are crucial for achieving your goals from point 1. This can include machines, sensors, SCADA, historians, databases, MES and ERP systems. Identify the stakeholders in your current data landscape (e.g. data consumers and producers) and actively involve them in the development of the strategy.


3. Shaping the future: Defining data architecture and infrastructure

Derive the requirements for your data architecture, security and infrastructure based on your objectives defined in point 1. While data architecture defines the basic organization and flow of data, data security refers to measures to ensure the confidentiality and integrity of data and to protect it from unauthorized access or loss. The data infrastructure physically implements the data architecture and security by providing hardware and software. Together they form the basis for your successful data strategy.

One example: If scalability is an important requirement, the implementation of information and data models (data architecture) for the standardization of machine data is crucial. In order to make the standardized data secure and globally available, transmission is encrypted and via a multi-level access control (data security). The fulfillment of this requirement is ensured by the integration of software and hardware components such as cloud services, servers and middleware (data infrastructure).


4. Forming a data team – Industrial IoT is a team sport

Form an interdisciplinary team that represents the most important stakeholders from point 2. Make sure that your team covers both the key specialist areas (e.g. OT and IT) and subject matter experts (e.g. for data architecture and security). The expertise of team members from different areas ensures that your data strategy can be implemented effectively and meet the diverse needs of your organization.


5. Invest in data infrastructure & gain a strategic advantage

A robust data infrastructure forms the backbone of a successful data strategy. Therefore, invest in state-of-the-art data infrastructure and make sure that your infrastructure is tailored to your specific requirements. By making targeted investments in your data infrastructure, you can meet your requirements and ambitions and provide your team with the optimal “tool” to make your company competitive in the age of data analytics and AI.


6. Pioneering work, convincing the organization

Prioritize potential use cases based on their cost/benefit ratio. Identify a pilot use case and monitor your pilot with regard to the defined goals from point 1, differentiating between improvement KPIs (e.g. OEE) and stabilization KPIs (e.g. number of faulty data points). This allows you to differentiate business success from teething troubles in your pilot and make targeted adjustments. Do pioneering work and use lighthouse projects to get your organization moving. In an iterative process, the data team from point 4 ensures that your data strategy is constantly evolving in line with business objectives and technological progress.



By setting precise objectives, analyzing the status quo in detail and developing a forward-looking data architecture, security and infrastructure, you lay the foundation for your successful data strategy. The formation of an interdisciplinary data team, supported by targeted investments in modern data infrastructure, maximizes your chances of success in implementation. Test and refine your strategy with a pilot project, do pioneering work and use a successful lighthouse to convince and inspire your employees.


You can also read our article on the most common risks when implementing a data strategy.

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