Finding the right data environment for you (part 2: industry)

How do you find the right data environment for your career, does the industry sector matter? Amongst the data leaders whom I’ve interviewed on the Customer Insight Leader podcast, there are examples of both answers to that question. Some, like Dan Kellet, have stayed in one sector or even one company. Others, like Aubrey HB, have experience of working in many different industries. Which is right for you?

I’m sharing longer posts from guest blogger Tristan Mobbs in this short series. He is the data & analytics manager for Kite Packaging, but Tristan is also active online generously sharing his learning experiences. He describes himself as a Data Translator and has shared with us before on important topics like data literacy, analytics communities and data storytelling.

In this series, Tristan shares his experience and advice on finding a good match. How can you judge if the organisation and role will suit you and aid your development as a data professional? How can you avoid taking a job that ends with you being frustrated or regretting your decision because you’re no longer growing? In this second post, Tristan focuses on choosing between different industry sectors. What should you consider when weighing up data opportunities in different industries?

After size, why do you need to consider the industry sector?

You have decided you want a role in data. You have probably thought about whether you want to be a data analyst, data engineer, or data scientist. You have now considered the size of the company you want to work for. But have you considered which industry you want to work in?

One of the advantages of a career in data is that your skills are transferable. Almost all industries use data. There can be significant differences between industries and certain skills that matter more in some industries than others.

Some industries are heavily regulated, others less so. Some have very specific rules to follow while in others you can make your own path. In some industries you own your data, in others, you have to rely on external companies to collect it or find public data you can use. The world of data has huge differences. Here are my experiences of transferring between 4 industries and 5 companies.

Four different industry sectors compared

A) The Energy Industry.

A complex industry with a significant number of moving parts. Equally, heavily regimented with processes to follow. If you can learn and understand those processes, there is a lot you can achieve.

B) The Construction Industry.

Depending on the contract, a complex industry with lots of different companies involved. Requires cross-company working and agreements, particularly with the use and ownership of data.

C) The Pharmaceutical Industry.

An industry where you don’t own your data. In the UK the NHS has all the sales data. This means you have to buy your data back from aggregation companies. One industry where you can get more information on your competitors’ sales too.

D) The Distribution Industry

The simplest industry I have worked in. Buying goods in and selling them back out. You own your own data, there are no explicit processes to follow. A focus on sales, marketing and efficiency is key.

What to consider when choosing based on data access

One of the biggest questions when choosing is, what are the sources of your data? Does your industry have ownership of the data and collect it through tools such as a CRM? Do you have to go and collect lots of data and have colleagues input that data? Do you have to use external providers to get your data? Is it a combination of all of the above?

Construction industry data access

One of the challenges of the construction industry was collecting data. This relied on many colleagues doing the hard yards out in the field, inspecting structures and measuring things such as the amount of silt in a drain. This environment had challenges in getting consistent, well-validated data to be able to report on and analyse. In these environments being able to develop processes while also educating your colleagues on the outcomes of their data collection is vital for having robust data that can be analysed.

Energy industry data access

The energy industry posed another challenge. Each energy supplier has a CRM system, but the central record of who is supplied by each particular supplier is held by Elexon or Xoserve. These central records are the source of truth and are updated by text files called ‘flows’. When everything works well the CRM, and the central record will match. If one small failure occurs, then things can start to get out of alignment. Being able to consolidate two sources is an interesting and important challenge in that industry.

Pharmaceutical industry data access

In the pharmaceutical industry, the challenge was different again, having to buy data from NHS aggregation services to understand your sales was something I never considered when moving into that industry. The unique nature of it however also allowed you to see competitor performance at a granular level. This allows for offensive or defensive strategies that are relatively unique to the industry.

Some questions to ask yourself to help you choose

• How much do you like working with external parties to get your data? Or do you prefer your own source of truth and ownership of your data?
• Do you also want to work somewhere with more manual data collection methods or in an environment where sensors or automated processes may capture things?

Different stages of data maturity in different industries

Some industries will be streets ahead in terms of their technical capability. If you want to work in a cutting-edge environment there are lots of tech start-ups or large tech companies that will be developing data science teams.

There are also many industries and companies where doing the basics and bringing in some data expertise will make a huge difference. You are also likely to encounter both ends of the spectrum at the same company.

Here are some general patterns of data maturity in three industries:

• In the energy industry, we had many spreadsheets and manual tasks, but we also developed neural networks and complex forecasting models.
• The construction industry had a 3D model of the road and was looking at using computer vision to detect problems with the road, yet also used spreadsheets to plan out projects.
• Within the pharmaceutical industry, we used machine learning to develop complex models fed by CSV files manually extracted from other systems.

Often the perceived data maturity at organisations can be different from reality. Companies shout about their cutting-edge tech; they don’t tell you about automating a simple process.

Which industry should you choose?

If you have a passion for a certain product or a strong interest in a sector, then go for that. By reading that sentence you will know if that is you. You are more likely to develop your skills and spot trends in something you are interested in. It may even be a sporting environment; the world of sports is becoming more data-driven and it is great to see analysts at most sports teams now. My favourite sport of cricket has lots of analysts who even have the opportunity to work around the world at different franchises.

If something doesn’t stick out immediately then consider what data you would like to work with. Are you more process driven and like having rules to follow? Then a regulated industry like energy, pharmaceuticals or banking may suit you.

Do you like creating your own processes and seeing what is possible? Then maybe retail, e-commerce or a sales business might be a good choice. Do you like helping others collect data from a variety of sources? Then, construction, environmental organisations or a service-based industry are options to consider.

5 Questions to ask yourself when choosing an industry?

1) Where do I want to get my data from?
2) Do I like working within a set of rules and will specific structures?
3) Do I want to work with customer data?
4) Do I want to focus on data collection, validation, and processing?
5) Do I want to work on cutting-edge tech or help a company grow in its data maturity?

We hope that helped you and would love to hear your choices

Thanks again to Tristan for again generously sharing his experience and reflections. We both hope that helped readers, particularly any who are wrestling with such a choice right now. It would also be great to hear what has worked for you. How did you choose the industry in which you now work? How has that selection served your growing data career or do you regret your choice? Please share your wisdom too, either using the comment boxes below or when this post is shared on our social media channels.