To build successful data solutions you must know what you are trying to achieve. And bosses shouldn’t try to monopolize knowledge. A talk with Iain Crosely, Director at XpertRule.

Talk to almost any experienced analyst working with data on a business project, and they will tell you that you must start with the end in mind if you want to get a good result. But what do we mean by this and what are the practical steps that service leaders and managers can take to ensure successful outcomes? For example, in a recent Service Leaders Network “Collaboration Project” a group of service managers defined a simple -yet effective- framework for success which identified 4 areas to get right: 

  • Know your Purpose
  • Understand your data architecture
  • Integrate into your processes and IT tools
  • Don’t underestimate the impact of people

We described this framework recently in another article.

And in this context, we recently spoke with Iain Crosley, Director Intelligent Manufacturing Solutions at XpertRule, an automation solution developer, which incorporates multiple AI capabilities and data analytics to streamline and enhance the decision-making process, including for manufacturing and service. Iain was for many years a Managing Director at Hosokawa-Micron, an industrial equipment manufacturer, where he was instrumental in creating an advanced remote service solution based on connectivity and data analytics. Here are some excerpts from the discussion:

Si2: What are the biggest challenges that industrial service businesses face when integrating advanced analytics into their everyday processes?

IC: From my own experiences and now talking with many companies, including small & medium-sized businesses (SMEs), I’ve realized that not many have a good handle on what data analytics is really going to do for their business. They hear a lot of the buzz words, but struggle to visualize how and what these data solutions really look like. In fact, I would go so far as to say that many managers really struggle to analyze their own business and so develop a balanced way forward. Too often they are biased by their own experiences and so find it difficult to take an “Outside-In” in perspective. I genuinely believe most managers new to advanced data solutions, do need help to clearly articulate the purpose and benefits of the data solutions they are considering.

The second biggest challenge I have experienced is around people. They have a massive influence on whether a project will be successful or not. And the more strategic a data project is, the more important it is to get alignment on purpose from top to bottom and bottom to top.

So how do you go about addressing these two challenges?

Let’s start with the “Top-down – Bottom-up” management perspective. Even now, in a business environment where we have had quite sophisticated data systems for the past 30 years, many senior managers (and business owners) still come from a perception that “Knowledge is Power”. Breaking this perception and building trust that knowledge transparency will help improve their businesses is probably the single most important challenge to overcome. Most data solutions only can have an impact if data-driven insights are free to be used by people throughout the business. And indeed, the more advanced “self-learning” data solutions rely on people and machines continuously learning from each other. So, breaking the “knowledge-is-power” paradigm is important for most. What these leaders need to understand is that leveraging the transformative nature of data starts with a fundamental change in their own thinking.

Only with this in place, can you release investment in training to bring all members of the team to be data-savvy. This is not just educating older employees about how they can use the technology available to digitize their processes and move to paperless systems. It is also about sharing with the younger digitally native generation the experiences of what data is particularly relevant to boosting the performance of a process, business, or finding new opportunities. It is very much a two-way street. 

For these reasons I always encourage the companies I work with to see any data projects as part of a larger and longer change journey.

What other pointers do you look for when working with your clients?

One factor I always look for is how well the business has documented its data project. It is one thing to come out with visionary statements, but something completely different to articulate specifics on paper. It shows me that the business has understood the problem they are trying to solve, they have developed a data hypothesis that bridges the gap between the business need and the data solution, and that they mapped out their pathway to implementing their Proof of Concept (POC) solution. If I see evidence of these activities, then I can be pretty sure the management team has a good handle on what they are trying to achieve. 

However, I can tell you one area I am NOT too concerned with is around processes & tools.  These are generally the easy bits to deal with because usually there are people in the business who understand how to develop processes. The tools are generally well defined and there is a wealth of knowledge accessible. 

What always worries me more is whether the business understands how its data systems and processes fit together and are used. For example, how accurate is the data? Where is it located?  How it is being managed?  Is there one “Source of Truth”?  We are not just talking about the Master data, but also the architecture of the systems that hold it.  

In a greenfield operation, it is relatively easy to build infrastructure that is relevant to your needs and can support your data vision. What is much harder, is building a robust data architecture around legacy systems, which is the context for most businesses. In these situations, I always recommend a “health check” before rushing in and executing a project. It is critical to understand the compatibility and quality of data as well as where the one source of truth for data is located. The most common challenge in implementing CRM or Field Service systems is the quality of this data, and how well people add to and maintain the database.   

Is there a top tip you would leave people with?

With so much data being created in the world around us, there is a very strong temptation to collect everything, put it into a central location, because “one day it could be useful”. Well, for most companies this approach can lead to significant risks.  The danger is that a company can land up with a very large Data Lake and people drown in lakes! For example, I was recently working with a contract manufacturer in the aerospace industry that spent two years diligently working through their data and systems so they would only have one source of truth. Most companies just do not have this amount of time. So, the trick is to shrink the lake to a pond by making sure you just focus on the relevant and significant data. Throwing more sensors, more connectivity, and more computing power at the problem is no longer morally acceptable as the world strives to reduce CO2 emissions.