Sunday, May 28, 2017

When Is It Time To Quit Your Data Analytics Job?

Often a bad job teaches you good lessons. That is something I have experienced recently while working as Senior Performance Analytics Coordinator at a university in Australia. My experiences at this job made me decide to quit despite a healthy six-figure salary.

I was responsible to lead the university’s analytics team and help the decision makers with valuable insights gathered from data. Unfortunately it was far from ideal and instead, it was a constant battle around the following 10 points. To me the followings are the red signs for a data analytics job and one may consider quitting the job showing all these signs. Having said that one must know that no organisation is perfect and as long as an organisation is not showing most of these signs at once, one should know how to adapt to the unique situation.

1. Using the wrong tools

In order for a productive outcome both for your employer and for your personal growth, it is important that you are exposed to current technologies in a data analytics job. Both of you should ideally be aware of the advantages and disadvantages of all the current tools. It is important because, for example, data analytics moved well beyond crunching numbers in an excel sheet. However, if that is the best and only tangible outcome is what you were asked to produce in your current job, it is certainly time for you to quit that job. If your organisation is not willing to accept the limitation of excel and merely expresses interest in data analytics, you will not grow much in that job whereas the rest of the world will move onto something new and advanced.

2. Using the tools wrong

On the other hand, even if you have access to the right technologies, you will still have to use it correctly. When I joined the above mentioned job, I found SPSS was available as analytics software. But to my surprise it was only used to copy-paste files between two locations. If your organisation is using a tool wrong it will not only force you to work longer hours for an insignificant gain but will also hamper your creativity as you will spend most of your time doing trivial things.

3. Lack of proper Data Repository 

“Where are the necessary data?” If this is the question you are asking to your colleagues, every time there is a new request for a data centric corporate report, it is a clear sign that your organization does not have proper data repository. In such cases, everyone in your organization, including you, should be fully committed to have a solid data storage as source of truth prior to undertake any analytical projects. Because, extracting insights out of wrong dataset could be catastrophic for the decision makers in your organization. Despite your strong suggestions for proper data repository, if you still do not see a solution in sight, it is certainly time to leave.

4. Lack of Organisational Data Dictionary 

An organisation aimed to make data centric decisions must have a strong Data Dictionary describing their basic organisational elements clearly. In my case, you would expect “Student Count” should have been one of the elements that had a clear definition including all the possible variations. Unfortunately, that was not the case. You would get different answers depending on whom you were asking. It was not productive for thought-provoking data analysis. It indicates that the organisation has lot to catch up and requires to go back to the drawing board.

5. No Data Strategy

In today’s data centric world, a solid data strategy provides the basis for organisational decision making. A data strategy will help you focus on the right things in order for your data to have organizational impacts. In absence of one, an organization will fail to provide you a data management objective, a plan for project execution, vision to keep up with rapid technological developments and sense of achievements. As a result, your data analysis will never get it right and you will never be satisfied with your performance and neither will be your boss. A sure sign to move on.

6. You are forced to make wild assumptions

As analyst if you are consistently making wild assumptions about your deliverables and data, without any clarification process in place, it should raise an alarm immediately. For example, in the above mentioned job when I found asking questions for clarifications were seen as objectionable, I was pleasantly surprise. It was like shooting in the dark to find insights from data in order for the management to make organisational decisions. This awkward situation is bound to create dissatisfaction for everyone, including you, and surely a recipe for disastrous outcomes.

7. No notion of Data Visualisation

One of the most creative parts of data analysis life cycle is to be able to visualise the data to express various scenarios. If there is no notion of visualisation at your job, instead there are demands only for numbers in various forms, absolutely time to quit the job. Without visualisation, a data analysis job will only offer some trivial tasks without any creative challenges.

8. Disinterest in algorithms and statistics 

If you haven’t heard anyone at your job talking about any advanced algorithms and statistical models in relation to the analytical projects implemented in previous months, you can consider it as a sign of inexperience that prevails at your organisation. This is often one of the first signs that indicates your current job is not going to help you much to progress your data analysis career. It will not be able to provide you a challenge to keep you interested in the long run.

9. No action required attitude 

If you observe that your data analysis were hardly actioned upon, take that as a hint of indifference towards data centric decision making that exists in your current organisation. This situation will never bring you job satisfaction and will eventually turn you into a reactive person instead of being proactive analyst. If this has been taking place consistently with your important deliverables, it is time to browse a job posting site.

10. Poor Management

Poor management bound to introduce unnecessary overhead to your daily workload. For example, if you are expected to report hourly to your manager regarding your data analysis for each day of the year without failing, you are certainly under a wrong leadership team for a data analysis job. This indicates the team leads have no prior experience in delivering great analytical projects and confused between regular corporate reporting and advanced data analysis. Also, if you have a situation where even to make a small decision you are required to go through an extreme hierarchy among your team leads every time, you will be exhausted. It shows the organisation is not ready for a fast-paced data centric world of decision making.

Having made all the points, I must emphasise, one should always first try to bring positive changes themselves at work. As a data analyst you should address all the above issues and have possible solutions to the problems. However, if you are constantly feeling overwhelmed, if you are not growing, if you are not appreciated, if your suggestions are constantly ignored for next time and if nothing you do is ever enough, surely you should leave.

This article is not only to help one to make a sound judgment about their jobs but also to help organisations identify issues to improve on.

As a data analyst, if you are busy satisfying managers’ need instead of having significant organisational impacts through you data analysis, the world of analytics will move on fast leaving you behind. It is always about overcoming your fear of people pleasing and made your voice heard or leave for the better opportunity. After all, management illiteracy and inexperience of data science are the two most common reasons why most data analysis projects fail.

No comments: