7 essential business tips for Data Analysts and Data Scientists

After spending the last 5 years of my career across 6 different companies working in various data analyst roles, I can tell you right now that there is a common set of themes, practices and behaviours I have picked up across every company I have worked at, including my time at Google. So many Data professionals today focus on gaining Data skills, but often lack the business skills which are much harder to acquire.

So here are some top business tips for anyone working as a Data Analyst or Data Professional in the corporate world today along with some personal stories in no particular order:

1. Always proto-type in Excel/Google Sheets first

As a data analyst, you are often perceived as a very competent person. Many times I had business stakeholders that would ask for fancy dashboards or reports, they require data from multiple sources so they could track various KPIs (Key Performance Indicators). Sometimes you work hard and put together something amazing, but only to realise that the stakeholder didn’t ask for certain features or that you missed specific points.

Instead of wasting time in Tableau, PowerBI, SQL or any other tool of choice, consider building a “proof of concept” or “mockup” in Excel first using some sample data. Create some pivot tables, with slicers, pivot charts and layer on conditional formatting or utilise COUNTIF and SUMIF formulas to aggregate data. This is a great way to make sure that the data points your stakeholders want to see are presented in the way they expected. You can then do a more advanced build in your tool of choice later saving you valuable time and effort.

2. Don't send emails that take more than 3 minutes to read/explain

This is something that many Analysts forget and something I’ve learnt the hard way. A long time ago I had written to a group of business managers about a complex data problem and how I built a solution to fix it, the email I wrote was 7 minutes long and even then, you would have to re-read it to fully understand it. Silly mistake. Not only did the business managers fail to make a decision from my recommendation but it took me 20 minutes to write it in the first place and several emails back and forth with further questions before they understood it.

So next time you have something complex to explain or talk about, avoid the email “tennis” and arrange or book a call/meeting instead.

3. Don't say “Yes” to everything

When I first started in Data, I remember desperately wanting to impress my manager and my colleagues with my Data Skills, I pretty much said yes to everything they asked, 6 months later, I realised I agreed to some horrible pieces of work which involved a lot of manual effort simply because my company didn't want to invest in some proper tools, so I ended up doing a lot of manual data transformation in Excel, I ended up disliking my job and wished I could do more “proper” Data Science work and learn something new. So be careful about what you say Yes to when someone asks you to look at a data request, manage a task, or take responsibility for a key report.

4. Always manage people’s “expectations”

Every man and woman working in a business has expectations, they expect to receive something by a particular date, or be told something in a particular meeting. In business often people’s expectations fall short — make sure these “failed” expectations aren’t coming from you. If your boss or senior stakeholder expects a piece of work by Friday, make sure you are on track, if you aren’t make sure they know about the delay and why there is delay. Make sure you communicate to key people and manage their expectations, good or bad, as long as you manage their expectations, YOU are in control.

I once struggled to deliver a piece of detailed analysis as my eldest child was sick at home with a virus, I spent all week caring for my family and was about to miss my deadline at work, so I postponed my meeting with business stakeholders and pushed it back 1 week to give myself time to catch up. It was fine, no one had a problem with it as I flagged it early. What's important is that you communicate this with people, which brings me to my next critical point.

5. Communication, communication, communication

It is said that in any presentation, half the battle is in the content you deliver, and the other half is in your delivery, how you say it, is what matters. If you can’t communicate a message or piece of work clearly, people will fail to understand it, so even if your content is very good, you can still be let down by poor delivery.

Communication is critical in business, not just in terms of managing expectations but also in dealing with people. Be sure to learn how to communicate properly with the right people at the right time. Go on YouTube and watch some TedEx talks and how people pitch, learn to structure any written presentations with an opening/executive summary and any additional info can go into an appendix. Learn how to write short snappy emails, with clear actions and call-outs. Avoid Waffle. Data is complicated to explain to non-technical users, so naturally, it's always going to be a challenge to communicate in a way which makes it simpler for people to understand.

6. Show an interest in the Business — learn how it makes money

Something I realised early on in my career was that Data professionals who mastered the business model of the company they worked for always had an advantage and gained an advantage with promotions and salary increases. It is, therefore, a very good idea you learn how your company “makes money”.

When I joined the Banking industry in 2019 I knew it would be a beast to understand. So much of Banking is centred around Managing Risk, Capital Efficiency, Regulation, Financial Modelling and Calculations. It took me a full 6 months to learn about Balance Sheet optimisation and Profit & Loss within the bank, Margins (there are several types), Costs calculations and ultimately how banks measured success. 6 months later I had senior stakeholders engaged in conversations around Net Interest Margins and Risk Weighted Assets and I held regular meetings for 30 minutes entirely by myself. That was when my manager knew, I had finally learnt how the Bank made “money”.

7. Manage your manager

It is said that we spend more time at work with our colleagues than we do with our loved ones and family — we spend almost 8–10 hours every day, 5 days a week, only to go home for a few hours and enjoy 2 days each week to ourselves. Your manager is therefore the closest person you will often have in your life in any busy Data job.

Your manager will also very likely be someone who has a lot of responsibility, they may also be managing Data projects, team members, critical issues and new product launches at the same time. So getting time with your manager is very important for your development. When I first started in Data, I often lost out on the chance to learn new skills (SQL, Python) because I didn’t take the chance to regularly speak with my manager, I ended up with work I didn't like and realised I should have developed a closer relationship with my manager. So take the time to get regular 1–2–1 catch-ups in your diary and communicate what you want to achieve out of your Data role, it's wise to talk about things that matter to you as well, salary, career progression working hours etc.

Last few worthy mentions for future reference:

8. Find a mentor — seek out other Data professionals who can coach you within your workplace.

9. Be wary of office politics — don’t engage in office chit-chat or favouritism, be objective, impartial and neutral.

10. Leave any emotions at home — as a Data professional you need to be logical and rational. Emotions sadly don’t do much to help Data Analysts.

Closing Remarks

I hope you find this article useful, there are more I wanted to share but I wanted to keep this one short. So much of your success in Data is dependent on you navigating a Business, its people and its culture. This is a side which is rarely talked about within Data Science or Data Analytics but is something you pick up with experience. Don’t just learn from mistakes but learn from other people’s mistakes too, I hope a few of the learnings from my mistakes serve you well.