Five habits of successful analysts are keeping a high bar on project delivery by walking that extra mile and delivering your best, the segment you can; triangulate numbers and think what do they mean for business, testing out your hypothesis even if you think they make complete business sense and learning something about analytics every day. Now let us see elaborately, what these five habits are.
Quality thinking differentiates a high performing analyst from a low performer. Successful analysts provide enough quality “brain time”, which is nothing but distraction-free time devoted to analytical problem solving to any project they deliver. This is the time when you strive for going beyond what is expected.
This is also the time when you, as an analyst ask some of these questions to yourself. Is there a better way to structure this problem? Will that make the solution better or more intuitive for business, is there a better way to present/summarize the findings of the project? How can you visualize the outcomes in best possible manner, instead of simply highlighting the insights from a project? Can you use these insights and chalk out business actionable? Can you size the impact from these actionable and tell them to business upfront? Is there any aspect of analysis you might have ignored? Is there any implicit assumption you have made which is impacting the result?
Value of Analytics
The value of analytics is recognized through the value it generates for the business and the amount of time spent asking these questions will have a direct impact on the business value created. There have been times when you might have changed the presentation flow, performed additional analysis, verified and re-verified all the numbers/insights till the night before project presentation. All of that is done to make sure the project creates the impact it deserves and nothing beats the feeling you go through after creating that impact. Therefore, it would be prudent not to leave any stone un-turned. Make sure there are no gaps in your thinking on every project you work on.
Successful analysts never work on averages. Every time they see an average, they think if there is an underlying segmentation at work, which could explain things better? By not segmenting the average, there is value left on the table. Successful analysts never do that.
As an analyst, you deal with numbers day in and day out. You need to pick out that one cell in which the formula is wrong from a file containing thousands (if not millions) of formulas. The only way you can do it is by triangulating numbers and by making sense of what they mean for business. While this might sound obvious, you will be surprised to see the number of times this is overlooked. There again arises a slew of questions: Ask yourself, can you reach this number through a different framework / calculation, do the numbers tie up or they are different by a magnitude, are there process dependencies which can give you a sense of numbers, can you issue 2000 credit cards every month, if you only get ~1800 applications every month, what do these number mean for business and do they tie in with the infrastructure and resources business has. Triangulation is like any other skill, it will look difficult to start with. But the more you practice, the better you become at it.
There are times when you tend to overlook the need for testing. Just adding live chat functionality to your website, sounds like a good thing to do with no down side. Test it out and you will know. The customers might not like it.
For instance, one of the leading travel portals in India saw this in their data: 90%+ flights booked have departing location same as the city from which tickets are being booked (determined by the I.P. address). They thought of making this location pre-populated (obviously with an option to change). This sounds like a nice idea which would help provide a better customer experience. Thankfully, they tested it out. Booking conversion dropped by double digit percentage within weeks of making this change. A possible reason, the study revealed that the customers were used to filling too and from location. Removing one of them added to the confusion. So, next time if you are implementing results from any analysis or hypothesis, test it out.
Analytics is a dynamic and evolving field. A new tool/technology/update arrive almost every 2 – 3 months. Being up to date with latest updates in industry helps not only stay on top of it but creates a huge gap from analysts who don’t stay updated.
Some of the questions you might as well want to understand are: What are the latest developments in Big data? How to analyse unstructured data from Social media? How can we make visualising this better, what are the statistical concepts behind the algorithms used by various tools and how can you design and analyze a design of experiments? The list is endless and you would enjoy the fruit of it if you understand and apply the same principles.
Data Science and Big Data Analytics
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