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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Work has always changed. We have had work revolutions from hunter-gatherer societies to agriculture, industrialization, and the information age. During the past few decades, information work has increased dramatically, and it continues to grow. Automation and algorithms* have already replaced some of the information work humans do, and this development will continue. And that is just great!

But what does this trend mean for engineers? Will engineers soon be on display in glass cases in museums, as the mammoths of tomorrow?

Automation is an engineer’s best friend

There aren’t too many of us who absolutely love laborious and time-consuming routine tasks like typing, copying, and sorting numbers on spreadsheets. This type of work should be automated whenever possible. We find it almost insulting that engineers’ brains are used to do work that automation could do. In fact, in many cases, routine tasks can be performed better, faster, and with higher quality when automated.

So, it is a great relief for engineers that they can get rid of “dummy” work and utilize advanced technologies to help and support their creative work. 

Creativity and curiosity make a great engineer

Engineers at their best are curious and creative. They don’t need detailed rules, definitions, or instructions to get a job done, like a computer does. Actually, micromanaging kills creative problem-solving and innovation.

Allowing creativity to bloom brings way better results and makes it possible to create something new. 

Skillful engineers excel when something is happening for the first time, and when everything is not going as expected or planned. When real brainwork is needed.

Creative problem-solvers are always needed

The type of brain work that a great engineer does will never disappear; on the contrary, the demand will increase. 

Problems are renewable resources when aiming for better performance. When automation and algorithms are used and technology becomes increasingly complex, more complicated problems arise. This allows engineers to focus on what they do best: creative problem solving.

So, hooray for automation, algorithms, and engineers – we need all of you! Now, and for ever after. 

 

*In this article, we view automation and algorithms as a broad concept: any technology that performs a job or task without human thinking, intelligence, or physical intervention. It can be a simple formula in Excel or a set of rules in software, neural networks, artificial intelligence, machine learning, quantum computing, intelligent robotics, etc.

 

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You might have encountered situations where the best way to work, based on your long experience and know-how, is not aligned with the systems in use. Systems that are supposed to help you work better. You are not the only one facing this issue.

Let’s take data analysis as an example. In theory, the process consists of the following steps: Defining the task, gathering data, cleaning the data, analyzing the data, drawing conclusions, and finally implementing the conclusions. But how often is the workflow that straightforward in real life? And, what if your system is built to follow the process strictly without any exceptions?

Which one is leading the way: Workflow or System?

Workflow is what people actually do: a sequence of tasks from start to finish. The ideal workflow is often described in guidelines, playbooks, and other process descriptions. In a work environment there are usually also systems, like ERP, CRM, a ticketing system, and a process analytics system, to help you follow the chosen process. In a perfect world, these are of course aligned, which saves time, resources, and your nerves.

But sometimes reality strikes back. 

Process descriptions can be made without understanding workflows, and a workflow can be forced into a system that is not suitable for it. 

When these are in conflict, workflow tends to win. People might pretend to follow the guidelines, and they might even do additional work to create the illusion that processes are followed to fulfill the system’s needs.

Systems should suit people’s needs, not the other way around

If systems don’t support actual work, people will find alternative ways to get the work done; and they can do it even if it’s strictly forbidden. This will cause extra work when people are pretending to follow the process and simulating things according to systems requirements.

When systems genuinely support the workflow, they make work easier, faster, and more efficient. Great systems excel in reducing routines and minimizing mistakes. They make people’s lives easier and happier, and employees and companies more productive. 

But only when they serve the workflow. So, choose them wisely. 

 

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