What makes a good Data Analyst? – 8 Pointers a good analyst should strive to develop
So what makes a good data analyst? Do you think you are a “good” Analyst? Are there certain traits, attributes and qualities that can make you outstanding in your chosen field? If so, how can we transform these personal skills into practise that can help generate success in what we do?
These are habits that can be mastered to achieve greater benefits. This article is written directly to analyst(s) who strive to become a champion in his or her role. However, if you currently or in the future will manage a team of analyst, this article can also help managers to look out for the characteristics they may wish to develop within their respective team.
A lot of organisations have invested money in tools. Having the right tools is required but not sufficient. Investing in the people who use the tools is much more important! The challenge is that good analysts can be hard to come.
Here are the 8 pointers every analyst should strive to develop:
1. Be able to tell a story, but keep it Simple
“If you can’t explain it simply, you don’t understand it enough” – Albert Einstein.
Why do I need to be a good story teller? Because people respond well, with information, within a story. They might not be able to remember all the story. Data needs someone to clarify and simplify it in a way that the line of business, project managers and the project team will understand. A good analyst must be able to communicate or present ideas clearly and confidently such that a non-technical audience can grasp the subject matter easily and also, this can help sway decision makers toward the right decisions. Spend Less Time on Data Preparation
2. Pay attention to Detail
As the saying goes, “The Devil is in the details”.
A good analyst should pay attention to details, this can help him or her to question or manage suspicious events during any data analysis project to avoid making a costly mistake down the line.
3. Be Commercially Savvy
A good data analyst must have a firm understanding of the business operations. In any organisation, the analyst must be commercially aware of the customer, people within his or her team, different departments and the line of business. The analyst must recognise and differentiate the true impact of their analysis and how it can affect the organisations decisions commercially. There’s a difference between “insight” and “actionable insight.” Insight is an accurate and deep understanding while actionable insight is information that can be acted upon, with the further implication that actions should be taken. Actionable insights lead to tangible improved performance that will result in increased revenue and/or efficiencies. A good analyst is a change champion.
4. Be Creative with Data
“It is all about finding the calm in the chaos” – Belinda Davison.
Ability to demonstrate the data proficiency, flair and mastery on data manipulation to solve or answer organisations questions should be a habit of a good analyst. This quality would help the analyst develop the ability to spot when things or data issue are very wrong. I have seen an analyst produced an analysis and make recommendations based on dirty data. The analyst must be comfortable with large volumes of data from disparate data sources, and be able to spot relevant patterns and trends.
It is important to be able to look at disjoint thought or action, see pattern that the ordinary people will ignore and also translate those less obvious pattern into business meanings. Rarely do we see people born with this skill, but this can be developed.
5. Be a People Person
“Communication and collaboration skills are vital for business analysts to be successful,” says Scott Ambler, the practice leader of agile development for the IBM Methods Group and author of several books on software project management and agile development. “Therefore they must be people persons.”
A good analyst must be comfortable in networking and liaising with a whole range of people across different line of business. A good analyst should be friendly since he or she will be the middle man/woman between people and the line of business. This will enhance his or her engagement with people so as to easily understand business requirements and to deliver an outstanding data analytics project.
6. Keep Learning new Tools and Skills
According to Ed Parker, “The intelligent man is one who has successfully fulfilled many accomplishments and is yet willing to learn more”.
A good analyst must never rest on his laurels; he or she must strive to become better either in data, tools, presentation and communication styles etc. The whole world of data analytics is very dynamic and it changes a lot. Hence to be distinctive amongst others, you have to continue to develop yourself and build capacity in term of technical skills.
A good analyst must never be content to do the same things the same way every time. They must aim to select the right tool for the job instead of relying on their go-to tools and making it work for every situation.
7. Don’t be Afraid to make Mistakes, Learn from Them
A good analyst should never be afraid to make mistakes. Once you make mistakes, admit it and learn from it. Experience is what you get when you don’t get it right the first time. If you are not afraid to make mistakes, it will help you to want to try a new way or new challenge which will then be part of your experience. Remember, this habit can be developed for an analyst that wants to become better in his field or for a business manager that is looking to have a set of good analysts on his or her team.
8. Know when to Stop
Finally, a good analyst should be able to differentiate when good is good enough when delivering a data analytics project. Example, if during a project, the solution you delivered is already 80% satisfactory. If trying to add the remaining 20% will cost you extra 50% of your time or energy and resources, a good data analyst must be able to make a decision on just delivery the 80% as it is good enough, hence re-invest any additional or time savings on other project priorities.
having read this article, as an analyst or if you manage a team of analysts, how many of these pointers apply to you or your analysts? Can you identify areas to develop? And finally, are you a good Analyst?
Version 1’s experienced consultants are on hand to help you understand your SPSS needs – from consultancy and training to finding the best software and license type for your analytical and usage requirements. Contact us to discuss your requirement and identify the best SPSS solution for you.
Take a look through our SPSS Articles covering a broad range of SPSS product and data analytics topics.
What’s New in IBM SPSS Statistics v29?
Looking to quickly learn what’s new in IBM SPSS Statistics 29? Rather than reading a list of changes and new procedures, there is a quick way to tour what’s new and access help. Read our post for more info.
What’s New in SPSS Statistics v27?
SPSS Statistics v27 was launched in June 2020 with the biggest change compared to v26 being the inclusion of Data Preparation and Bootstrapping as standard functionality, as part of the SPSS Base module. This post will take a high-level look at these changes plus cover off the key enhancements in v27.