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How to focus on the problems and not on the tools

Why it is not always important to focus on the tools in technical software roles.
Richie Wong

Data Analytics Engineer, checkout.com

Technology is constantly changing

In the last decade, the way organisations are applying the use of data and technology more seriously because of the potential benefits it can bring. Technology is constantly changing and at a much rapid pace, organisations are adapting to competition which means traditional roles are redefining as well as new roles emerging. It is a correlation of how technology listed stocks has increased over the last decade compared to other industries.

Either way I think it is an exciting age to be working in to learn and apply these tools.

However, is it a concern that we are too focused on learning the exciting and trendy tools and not actually focused on the underlying problems?

Here I'll be explaining how the tools within AI and Machine learning applications have advanced and explaining why the business problems should be the main focus.

The headlines

We hear today's headlines are new and exciting ways of applications in AI and Machine Learning to name a few are in medical health and self driving cars and climate change. These are tools and technology were science fiction until recently are being started be applied into reality.

Photo by NeONBRAND on Unsplash

Why technology in the work force has change now? - The power of open source

In the technology community, it is becoming much more common to share codes and practices. Complicated problems can be tackled together from not only a workforce of ~ 100,000 but an open source community of 10,000,000. It is a much more powerful way to solve problems, where the best ideas wins. This has led to the increased acceleration of sophisticated machine learning algorithms and technologies. In addition with more advanced modern computers and data centres to compute store and use big data, data science has accelerated.

Let's be realistic

With the open source of technology the majority of people who have internet and a computer can learn the tools and take advantage of the many data science / machine learning packages. Our energy should not be focused to build a better search engine or facial detection just because the technology is exciting, as it is highly unlikely we are going to build it better than these big tech companies.

Instead we should focus on local problems by applying the technology that is out there. Thinking about how to make your organisation more efficient, how can we utilise our data, how can we reduce the churn rate of our customers etc.

The takeaway

It is often far too easy to fall into the trap of focusing on the exciting tools and not the essential problem. This can waste of a lot the business time and money to invest in unnecessary technology, where the most important matter is actually solving the route of the business problem.

Other examples are visualisation or charts that look cool but they don't really say much or is actually meaningful to the stakeholder for insight or action because the analyst / producer was too excited to show off all the tools that they have learnt to their stakeholders.

It is an easy path to follow just because you heard of something new and you learnt from a conference, it may not be best or appropriate for the organisation at the time.

Key ways to avoid

  1. Keep the end user in mind and think about how your work will impact them
  2. You can be mindful of the technology (that it exisit and what it does) - no need to spend a huge amount of time
  3. Start small by a process to trial and test the technology, then scale if it works
  4. Explore in your L&D time

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