Insights Starved Data Curiosity
How traditional Data Analysis tools cannot help anymore – The Life of Every Other Manager
You would think that after reaching a middle or upper management position, a Manager would be doing lots of cool things in their day-to-data activity. However, from our experience in the GCC, most managers spend a lot of time and energy in trying to analyse data.
The fact is, junior employees do not understand the business well enough to analyse data and create meaningful reports and hence the onus of analysing data, creating recommendations & advising seniors forcibly falls to every manager’s hands. So, starts the journey outlined below!
Once upon a time, Excel was good enough for the small datasets that professionals typically used to work with. Today we are working with large datasets that become cumbersome to analyse in Excel. For instance, most managers face some of the following frustrations. Do you face any of them?
So, your company invests in a BI Tool and your IT Managers gets the biggest and shiniest toy in the BI Market for you to do your job. Now you are expected to perform & deliver. But let’s see what really happens (totally based on our experience).
There are many excellent BI tools are available in the market and BI’s are a must for organizations in today’s day and age. However, while a BI is good for real time reporting of selected KPI’s, it is not a good investigation and exploration tool because it is not an investigation and exploration tool. You use exploration tools to explore data and during the exploration process, when you stumble upon a ‘view of the data’ that you like, you can call that view a KPI and have your BI tool monitor that KPI on a real-time basis.
At the end, the end-user, the manager has to come home to Excel to make the reports the upper management need to see.
Just as spreadsheets once revolutionized the work environment and replaced the scientific-calculator, Analytics Programming Languages like R & Python are revolutionizing how we analyse data. Data rich companies like Facebook, Google, Amazon, etc. do not limit themselves to Excel nor do they suffer the costs and limitations of BI tools. They are heavily dependent on open source analytics & statistical programming languages including R, Python and various others in order to determine trends, predict customer moods, etc. This is driving the growth of such Data Analytics languages as Artificial Intelligence, Predictive Modeling, Sentiment & Text Analysis, Social Media Analysis, etc. are becoming must-have capabilities to gain the competitive edge even for SME’s.
Some of the benefits of R for Data Analysis include,
Some would argue that there is a steep learning curve in becoming proficient in R. However, just like one would learn excel, one would go about learning R.
Based on our experience in training business managers & executives from many different industries and background, most of them being from non-IT background, the need of the business and availability of a business-problem makes the journey of learning R easy. You have a dataset and you are stuck with analyzing it with Excel or BI. You start learning R with a problem in mind and the focus shifts from learning a new language to solving that problem. You just happen to learn R along the way.
Looking at this from another point of view, almost all data analysis requirements of non-IT managers are satisfied with a couple of functions & packages in R. The need to learn the exhaustive capabilities and functionality of R is not a key requirement. A 5-day crash-course in R is sufficient to get people started for their day-to-day requirements. From there on, it depends on a case-to-case basis if someone wants to further develop their skills sets. Packages for data analysis like Dyplr, Ggplot, etc. make working with R a breeze and a lot of fun and offer exhaustive range capabilities to the business manager looking for quick data analysis.
Also, there is a strong online community of users who are happy to help in case you are stuck. Rarely will you run into problems or have requirements which an online forum hasn’t already answered.
Another point to consider is that by being an open source language of statics that has been around for some time now, in comparison to BI tools, investing in learning R allows you to take your skills with you from one job to another and you are not dependent on whether your new organization has invested in licences for R.
The question is not whether you need to invest time in learning R. The question is how do you utilize the world of opportunities that raw data analysis platforms like R & Python open up for you!
We find that more than learning these languages, the challenge becomes how to exercise and develop the muscles of data-curiosity correctly. What we have witnessed is that once familiar with the different possibilities of data analysis available through R, managers need to be introduced to a standardized way of framing the data analysis problem, correctly applying statistical methods and making statistically valid inferences. With great power comes great responsibility!
Also, I would like to point that developing a mind-set for satiating data-curiosity is not dependent on learning a data-analysis language like R. With a new wave of students graduating with strong experience in R, Python, etc. and entering the work force, it is important for senior managers to know about the capabilities and risks of advanced data analysis instead of blindly believing newly hired Data-Scientists. They need to know the what and why of the steps employed for the analysis, they need to be able to ask which statistical methods were employed to create a model, etc. in order to evaluate the ‘correctness’ of the inferences and insights presented to them.
We offer a 5-day training program in Data Analytics with R and have successfully trained many senior executives & managers across the GCC. We start from the basics and do not expect participants to have any IT or programming background.
The course is aimed for management using case studies and examples of problems faced by managers on a day-to-day basis.
The training program opens a new world of opportunities that were not previously possible with spreadsheets & BI Tools. With these new capabilities achieved by R, you need to change the way you think about Data and Data Analysis and our training focuses on getting the participants on exercising their data-curiosity and framing Data Analysis problems with the right mind-set.