Analytics - Where to begin with?

ANALYTICS is the new buzz word and everyone seems to be running towards it. But most struggle to find the correct point of entry into the space. And by forcing through a wrong entry point, they generally mess up things for both parties.

Analytics space is a combination of 3 completely independent fields of study -
BUSINESS MANAGEMENT, STATISTICS, COMPUTER SCIENCE.

With this post, I want to paint a clear picture of the analytics space, its entry point and the ladder up.

I will consider the space of analytics analogous to the human body. I hope this will help us understand the analytics space much better.
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BUSINESS MANAGEMENT - This has been and will be the brain in any analogy. Business skills are the thinking centers and the control towers of the analytics space. It is the source of all the queries and questions that has fueled the need of analytics and lead to the convergence of these distinct fields of study.
But these are most "TAKEN  FOR GRANTED" skills in the space, however, people must understand that it's a DATA-DRIVEN BUSINESS now. So people must understand and accept the fact that their X-X years of experience is no longer the data that can convince others.
Not to be mistaken that the XX years are no longer important, they are important.
The convergence of statistics and business is still fresh and we still require a way to validate if the statistical tools and methods are guiding us correctly or not.

People with these skill-sets report to their respective (CXOs) like the marketing guy to the Chief Marketing Officer and so on.

STATISTICS - It is the heart of the analytics space. This is probably the most direct, logical and visible set of skills in the analytics space. The industry has forced people to believe that one must be a statistician to be in the analytics space but I personally believe that this the biggest myth.
It's true, the whole data processing algorithms are results of this field of study. Statisticians are the ones who develop methods and algorithms to process data and get those strange numbers out of it. But it's also a fact that in general, it's about using the already developed algorithms efficiently rather than developing new ones. You need the best solider to use the weapons, not the blacksmith who created them.
So every person in an analytics team must have a fare bit of understanding of statistics and statistical methods and need not be a statistician.
People with these skills would generally be under the Chief Analytics Officer (CAO). This team would be responsible for using the data assets of a company and deriving actionable insights and presentable ideas. They would also have the big responsibility of breaking down the statistical results to business language.
This team is the new inclusion in the traditional  organization structure, so in some companies it will have an independent identity and in some, it will be  part of the traditional departments working in silos and in the absence of CAO.
The structure of the analytics team is still a debate where some suggest a central team catering to all and others suggest a department-wise teams. A third hybrid structure is also in discussion nowadays.

COMPUTER SCIENCE - These skills are the blood veins and arteries of the analytics body. I believe technology has been the ultimate enabler for this convergence.
Till now
Statisticians had algorithms. Business had some data.
Statisticians needed more data for holistic view. Business concentrated on day-to-day operations.
Statisticians needed supercomputers to process this data, Business was not capable of funding such operations.
Statisticians needed months to generate results, Business understood nothing more than seconds.

Then came the CLOUD revolution, the NoSQL & HADOOP revolt, the MOBILE uprising and the giant leap of 32-bit to 64-bit, which drastically reduced the cost and improved performance.
People of these skill-sets are needed to transform the traditional system into an analytics ready system.

People with these skills will be reporting to the traditional Chief Technology Officer (CTO) or the Chief Information Officer (CIO) or the Chief Data Officer (CDO) depending on the companies structure and industry domain. This team would be responsible for the storage, security, management of the companies data assets.
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Analytics is an area of convergence, so an analytics team needs all these skill-sets to work together towards a common goal to derive the most out of it. So one needs to exhale in one or a combination (Business\Computer with Statistical Understanding) of these categories to enter the analytics space, which one... I think you alone are the most capable of answering that.

Analytics - Foxy or Frank

After spending a lot of time in this new space, I felt that there is lot of myths flying around in the world. These myths are not only setting wrong expectations but also entangling the existence of the space.

Myth 1

If we hire an analyst and give them access to our database, they will start farming gold.

Noooooooooooooo...
People get out of this. Analyst are not GOD. They are simple human being who studied something other than engineering.
Giving them access to your database doesn't mean anything. If your data collection methods are false, if you are not collecting the right data, even the best of data analyst and data scientist wont be able to do anything.

Myth 2

Data is the new oil.

Yes Data is the new oil, but someone forgot to complete the sentence.
Just like oil cannot run everything in you life, similarly data cannot do wonders for everything. At places the traditional  methods are still superior than even the most advance form of analytics.

Myth 3

Analytics will "help" us take decisions.

Yes very true. But in most cases, people skip the "help" part. They follow the analyst's prediction and prescription blindly. But people you have to understand, that the analytical processes are simple PCs in the backend, they are our same old stupid computers. They do what some intelligent people (with all respect to them) have told them to do. But these programs may not suit your situations. So please stop following blindly..

Myth 4

Analytics means high investment and a technological shift

Noooooooo
Analytics is about making sense out of data, the only requirement is to have a proper data collection method (means capturing the right parameters and data points). Tools and softwares can be freewares like Google Spreadsheet, MySQL or even CSVs. For analytics, 60-70% of the job can be done in MS Excel itself (which is paid but assumed to be available) or 150% of the jobs can be done in R (which is free in all directions)

Myths 5

Big  Data is not Analytics. No Big Data is not Analytics

Yes, I accept the fact that the whole need of having a Big Data eco-system is because it gives more power to analytic teams and helps in bridging the gaps in our traditional analytic reports and get a more real-time analytics done. But these 2 are not the best of buddies.