Insights on Process Analytics

How to build a data-driven culture at your mill?

Often, when speaking with a prospective customer, we learn that they are struggling with the chicken or egg dilemma: whether to invest in data tools or build a data-driven culture first. The customers’ line of thinking often is that, without a data-driven culture, any investment into tools and capabilities goes to waste. Intuitively, the argument sounds right, but does it stand up to scrutiny?

We think that culture and tools are equally important, and they can and should progress simultaneously. Gartner study “10 Ways CDOs Can Succeed in Forging a Data-Driven Organization” takes a very similar view. It lists ‘data-driven culture’ and ‘advanced analytics capability’ as the two most critical elements when building data and analytics team success. Advanced analytics capability can be further split into employees’ capabilities and supporting tools.

So, we claim that a data-driven culture is a sum of three things:

  1. Data-driven decision-making
  2. Employees with data literacy
  3. Solutions that enable easy use of data

Data-driven decision making starts from the top

Data-driven decision making is easily led by example: when you make decisions, favor data-backed arguments. This message will quickly echo through the organization and lead to a situation where people have data to support their ideas and requests. The ultimate goal is naturally that people instinctively start to utilize data when doing small optimization decisions on a daily basis.

When evaluating data-backed arguments, it is worth remembering that anything can be proved by facts. The more data you have, the easier it becomes to misuse the data to serve the conclusions you want to draw. This highlights the importance of an open mind, transparency, and reproducibility when conducting analyses. 

Another word of warning: as a leader, do not demand unrealistically detailed data-based evidence of everything. If you want to turn down an idea, do it, but don’t use ever-continuing data-based evidence asking as an excuse to suffocate ideas that you are not enthusiastic about. 

It is crucial that employees learn to “speak data”

Data literacy is the ability to read, write, and communicate data in context. This includes an understanding of data sources, analytical methods and techniques applied, as well as the ability to describe the use case, the application, and the resulting value.

How to foster data literacy, then? Training and easy-to-use tools help. Sometimes one needs to recruit new skills. Still, curiosity is the single most pivotal success factor, as curious and creative people tend to dive into data, play with it, fail, fail again, and finally succeed in turning the data into information and actions. People without the right mindset will never become data champions, regardless of how literate they are.

Data solutions must fit your needs

Don’t get fooled if somebody argues it can be done in Excel. You may have one wizard in your team who can, but it will never scale and become a culture.

The top 3 things that kill motivation for data usage are: 

  1. Data is not easily available, it is siloed and/or of low quality 
  2. Data processing is slow and cumbersome
  3. There is no easy way to get meaningful results and findings from the data.

If you are considering investing in a new data utilization tool, stop for a second and think about what type of data you need to study: Is it event or time-series data? How is the raw data quality? Does the data contain delays? Would you like to study profile data as well? Is your process continuous, a set of batches, or a hybrid of the two? Do you want to keep the data in your own servers, or is a cloud-based solution ok?

Once you know what you want to analyze, list available tools. When reviewing the tools, make sure that the new tool provides a smooth and flexible workflow, intuitive and visual user interface, and analysis capabilities ranging all the way from basic analytics to advanced problem-solving. And last, make sure that your team gets good training and support during the implementation.


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