The 4 Small but Powerful Ways to Improve Your Data Skills This Year | by Hanzala Qureshi | Jan, 2023

Level up Your Data Game by Mastering These 4 Skills

A colleague recently asked how they can level up their data skills this year.

Data skills can come in many forms. From Data Engineering, Analysis to Data Science & Visualisation. However, they all have a common goal: to drive business value. This means the answer to the skills-related question is more value-driven than technical.

So, let’s explore four ways to improve data skills this year.

1. Finding the Root Cause of Data Problems

There are always Data Problems.

Whether that is quality, infrastructure or performance. Your organisation will face many Data Problems.

It is imperative to have analytical skills to pinpoint issues in the data lifecycle. Recently, the reporting of the executive metrics had been delayed in an organisation. The IT team took the major brunt for delayed data delivery, where the delay was due to a core logic change in how the metric was developed. Things are only sometimes as they seem. Hence, having analytical skills to ask the right questions is essential.

Finding the root cause of Data Problems quickly and efficiently is an important skill. Analysing your organisation’s top ten latest data problems can help you level up this skill. Once the problem is resolved, people generally move on; if you spend the time to understand it, you can be effective on the next one.

Invest time learning to analyse issues, find root causes and offer remediation steps.

2. Explaining Data Concepts in Simple-Speak

If you can’t explain something in simple terms, you don’t understand it well enough — Richard Feynman.

Data is a complicated topic with brilliant people working on challenging problems. However, only a few people can explain concepts jargon-free to the broader business.

Being a good storyteller is an underrated skill in the Data industry. In my early days, I remember always struggling to be succinct when providing updates on my work. In reality, not many people care about the details; they want the TL;DR version. Learn to boil down topics, use analogies and avoid jargon. If the message doesn’t land, tweak it and try again.

Good storytelling helps you land the message, keeps the listener engaged and builds credibility. Distil your work into simple concepts/analogies and rehearse the delivery. This is how you level up your storytelling skills.

Explain it simply enough, and even a five-year-old will understand it.

3. Understanding Business Objectives Behind the Data

If the data doesn’t solve a business problem, it is useless.

As Data knowledge workers, we spend a lot of time on moving priorities. However, the bigger picture about how the activity moves the needle often needs to be noticed.

Linking your day-to-day work to an end business objective is an under-appreciated skill. We had a team of analysts who spent four weeks finding the root cause of a Data quality issue, only to find that the resolution had zero business impact. It was great to resolve the issue for some inane technical challenge. But it did not improve customer experience or mitigate any business risks. If it doesn’t help a business objective, why is it important?

It is imperative to keep the business objective in mind to avoid the above scenario. Always ask “why” until you are satisfied that this is a core problem. If you are not satisfied, ask “why” again.

Connect the dots between the Data & Business; only a few people are doing it.

4. Think Strategically

We all spend too much time firefighting.

Firefighting makes you a great tactical thinker. However, you lose the strategic objectives and future-proofing thought process.

A resolution to a Data Problem could be quick but tactical or long but strategic. Nine times out of ten, the tactical will be chosen to stop the bleeding; I have done this myself. But months later, the strategic resolution that was not put in place will come back to bite. Think of the long-term impact of your proposed outcomes. Otherwise, you are simply putting out the fire to light a bigger one in the future.

Regardless of your seniority in an organisation, thinking strategically gives you an edge. Question whether what I am about to implement, present, remediate, write etc., will have a longer-term impact on the business and your credibility. If it is, look for alternatives.

Become a strategic thinker; it’s a rare skill.

Conclusion

Yes, you need to know SQL and Python and the Visualisation tool of choice and.. and.. but these are a means to an end or just an enabler. The technology landscape keeps evolving, but these soft skills will be timeless. Prioritise them to level up your skills.

If you like this kind of content, check out some of my other posts:

If you are not subscribed to Medium, consider subscribing using my referral link. It’s cheaper than Netflix and objectively a much better use of your time. If you use my link, I earn a small commission, and you get access to unlimited stories on Medium, win-win.

Level up Your Data Game by Mastering These 4 Skills

A colleague recently asked how they can level up their data skills this year.

Data skills can come in many forms. From Data Engineering, Analysis to Data Science & Visualisation. However, they all have a common goal: to drive business value. This means the answer to the skills-related question is more value-driven than technical.

So, let’s explore four ways to improve data skills this year.

1. Finding the Root Cause of Data Problems

There are always Data Problems.

Whether that is quality, infrastructure or performance. Your organisation will face many Data Problems.

It is imperative to have analytical skills to pinpoint issues in the data lifecycle. Recently, the reporting of the executive metrics had been delayed in an organisation. The IT team took the major brunt for delayed data delivery, where the delay was due to a core logic change in how the metric was developed. Things are only sometimes as they seem. Hence, having analytical skills to ask the right questions is essential.

Finding the root cause of Data Problems quickly and efficiently is an important skill. Analysing your organisation’s top ten latest data problems can help you level up this skill. Once the problem is resolved, people generally move on; if you spend the time to understand it, you can be effective on the next one.

Invest time learning to analyse issues, find root causes and offer remediation steps.

2. Explaining Data Concepts in Simple-Speak

If you can’t explain something in simple terms, you don’t understand it well enough — Richard Feynman.

Data is a complicated topic with brilliant people working on challenging problems. However, only a few people can explain concepts jargon-free to the broader business.

Being a good storyteller is an underrated skill in the Data industry. In my early days, I remember always struggling to be succinct when providing updates on my work. In reality, not many people care about the details; they want the TL;DR version. Learn to boil down topics, use analogies and avoid jargon. If the message doesn’t land, tweak it and try again.

Good storytelling helps you land the message, keeps the listener engaged and builds credibility. Distil your work into simple concepts/analogies and rehearse the delivery. This is how you level up your storytelling skills.

Explain it simply enough, and even a five-year-old will understand it.

3. Understanding Business Objectives Behind the Data

If the data doesn’t solve a business problem, it is useless.

As Data knowledge workers, we spend a lot of time on moving priorities. However, the bigger picture about how the activity moves the needle often needs to be noticed.

Linking your day-to-day work to an end business objective is an under-appreciated skill. We had a team of analysts who spent four weeks finding the root cause of a Data quality issue, only to find that the resolution had zero business impact. It was great to resolve the issue for some inane technical challenge. But it did not improve customer experience or mitigate any business risks. If it doesn’t help a business objective, why is it important?

It is imperative to keep the business objective in mind to avoid the above scenario. Always ask “why” until you are satisfied that this is a core problem. If you are not satisfied, ask “why” again.

Connect the dots between the Data & Business; only a few people are doing it.

4. Think Strategically

We all spend too much time firefighting.

Firefighting makes you a great tactical thinker. However, you lose the strategic objectives and future-proofing thought process.

A resolution to a Data Problem could be quick but tactical or long but strategic. Nine times out of ten, the tactical will be chosen to stop the bleeding; I have done this myself. But months later, the strategic resolution that was not put in place will come back to bite. Think of the long-term impact of your proposed outcomes. Otherwise, you are simply putting out the fire to light a bigger one in the future.

Regardless of your seniority in an organisation, thinking strategically gives you an edge. Question whether what I am about to implement, present, remediate, write etc., will have a longer-term impact on the business and your credibility. If it is, look for alternatives.

Become a strategic thinker; it’s a rare skill.

Conclusion

Yes, you need to know SQL and Python and the Visualisation tool of choice and.. and.. but these are a means to an end or just an enabler. The technology landscape keeps evolving, but these soft skills will be timeless. Prioritise them to level up your skills.

If you like this kind of content, check out some of my other posts:

If you are not subscribed to Medium, consider subscribing using my referral link. It’s cheaper than Netflix and objectively a much better use of your time. If you use my link, I earn a small commission, and you get access to unlimited stories on Medium, win-win.