In my previous blog, “4 steps to becoming a data-driven manufacturer,” I described a 4-step approach to becoming a data-driven manufacturing enterprise:
- Building a robust and end-to-end data strategy
- Taking a holistic enterprise-wide data management approach
- Leveraging enterprise intelligence to take data to the next level
- Ensuring organizational readiness to manage the human side of change
These four steps are the pillars of a data-driven enterprise, and I will delve deeper into each one, starting with a closer look at data strategy.
In today's fast-paced and highly competitive manufacturing landscape, data has become an essential resource for running a business. Making the most of what’s fast becoming almost unlimited data reserves requires a well-defined data strategy. It can help manufacturers achieve their goals, whether it is to better understand customers, improve operational efficiency and resilience, or drive profitable growth. However, determining the right data strategy for your manufacturing organization can seem daunting.
Below, I share some key considerations for defining and implementing a data strategy that’s right for your manufacturing organization.
#1: Identify your business goals
The first step in defining a data strategy is to really pinpoint your business goals. These goals should align with your overall business strategy and value drivers while being specific, measurable and achievable. For example, if your goal is to improve operational efficiency, you may want to focus on reducing asset downtime or increasing production output. Identifying your business goals ensures that your data strategy is aligned with the overall direction of your organization.
#2: Assess your current data landscape
Once you have identified your business goals, the next step is to evaluate your current data landscape. This includes identifying the types of data that your organization currently collects and how it is stored, as well as identifying any gaps or challenges that need to be addressed. This will help you to understand what data is critical to operations and what areas need improvement. However, it is not just the data that needs an assessment. Understanding your organization’s maturity regarding people, processes and technology will also influence and guide the right way forward for your unique needs.
#3: Develop a data governance framework
Data that is of low quality is as good as having no data. A data governance framework is essential to ensure your data is accurate, accessible and protected. This includes policies and procedures for data management, security and quality. By developing a data governance framework, you can ensure that your data is protected from unauthorized access and that it is of high quality.
#4: Identify and prioritize use cases
Once you have assessed your current data landscape and developed a data governance framework, the next step is identifying and prioritizing use cases. Use cases are specific applications of data that can help you achieve your business goals. For example, if your goal is to improve operational efficiency or resilience, you may want to focus on use cases such as predictive analytics or supply chain optimization. By identifying and prioritizing use cases, you can ensure that your data strategy is aligned with business value.
#5: Build and implement your roadmap and continuously monitor for success
With the objectives, use cases and governance in place, a suitable roadmap can then be developed to guide your organization in the implementation of the strategy. Ongoing monitoring is crucial to gauge its effectiveness. Some of the key aspects of a comprehensive roadmap include the review and selection of the right technologies, such as data visualization or analytics tools, as well as employee training on how to use them. It is important to regularly monitor and review your data strategy to identify areas of improvement and make adjustments to ensure your organization is getting the most out of your data.
Benefits of the right data strategy
A robust and well-defined data strategy aligned directly with your business and IT strategy will successfully deliver usable, accessible, quality data that supports insights-led decisions. When put into action, the right data strategy:
- Accelerates outcomes by strategically aligning initiatives to business value
- Allows data and analytics to adapt as business and customer priorities change
- Identifies the maximum value with the minimum scope to deliver initiatives that result in an ROI
- Factors in the role of critical information and data for strategic and operational decision-making
- Considers the role of people and culture across the organization
Are you currently on your journey to becoming a data-driven manufacturing organization and ready to take the next step?
Get in touch with me to discuss how we can support your data journey.
4 steps to become a data-driven manufacturer.
Data management in manufacturing: the difference between being data-driven and data-burdened.
Enterprise intelligence: Going from “data rich” to “insights rich” in manufacturing.
Why taking a people-first approach is critical to becoming a data-driven manufacturer.