You may be an employee, a consultant or an CXO of a company. How do you view the $16 bn Analytics industry?
Broadly speaking, when you look at Analytics services, it can be offered by companies in two modes:
Boutique model
Captive model
Boutique (also called as Niche Analytics companies) Model
The companies fall under this category has deep expertise in the field of Analytics. They can offer services in almost all major domains such as Banking, Finance, Insurance, Manufacturing, Government etc. to name a few.
They have the right set of people trained on domains, tools & techniques required for any analytics projects. Major clients approach (or vice-versa) these companies to help them in analyzing the data. It typically works like how IT outsourcing takes place.
Few companies in India that are operating under this model are
boutique-analytics-companies-examples
Captive Model
MNC companies who have big ticket investments would set-up their in-house Anaytics division to cater to their tailored needs. For instance, a credit card company can set-up its own analytics team to prevent and alert the fraud transactions happening over its network in the form of a system.
These companies store the highly confidential data, mostly in BFSI space, hires the consultants in setting-up their division. And then the team grows based on the ROI and the type of projects they take up.
This really got me thinking. Why is Analytics a buzzword these days?
According to many industry experts that I listened to, this is not a brand new process as such. However, industries are adopting this at a high scale.
Why now? Because of overwhelming data that are accumulated thanks to all the Social Media, Search Engine and especially my most favorite User Generated Content (UGC). With billions of searches on Google, millions of photos being uploaded on Facebook, a million ride thanks to Uber, these cutting-edge software companies have now access to store torrents of data, and make informed decisions out of them!
Just visualize the volume of data being generated every minute, variety of data such as text/multimedia/rich content created and shared, and velocity (past and real-time data). These are the 3 Vs of Big Data! Companies want to make sense out of it to reduce costs, improve revenue, profits and customer satisfaction.
The transition in major companies is evident. From Business Reporting using KPI Metrics to Business Intelligence and Data Visualization + Dashboards to Descriptive Analytics to Predictive Analytics to Prescriptive Analytics.
I’ll write my understanding on these on separate articles.
I’m a computer programmer. Can I transition my career or line of work towards Analytics?
That’s what I asked me a question 2-3 years. I didn’t have the answer back then. You too may be curious to know what it takes to be in the Analytics profession?
What’s the business problem you’re intending to solve? The problem could emerge from your corporate, government or any for that matter. Without understanding the context & background, it’ll be very difficult to structure the problem! To go through this, domain expertise is essential. Having this skill enables you to visualize the big picture from Problem to Probable Solutions.
Next step is the “How” part. How are you going to solve the problem? You may gather the data, do some analysis using any applied mathematics or statistical concepts, understand the relationship hidden in the data and come up with recommendations. Tools and Programming skills are essential at this stage to perform the steps.
In a nutshell, this is my understanding when it comes to the skill sets required for Analytics.
skill-sets-for-analytics-by-coffeewithshiva-com
Well, hold on, don’t jump into making your conclusion! The tools, techniques, programmings evolve and one might replace the other during the course of time. The way to approach in this typical field of Analytics is to become good in one skill and try to learn the other skill sets at the earliest.
You can choose to become a go-to person when it comes to business acumen and you may also learn the A,B,Cs of frequently used Statistical models, tools and programmings. In my case, I have relatively good experience over programming and hence my focus would be on learning statistics, gaining business knowledge.
Each skill set mentioned above is very vast in itself. For instance, there are many statistical models available. There are many programming tools and languages that can cater to analytics these days. When it comes to business, you can drill down to Sales, Marketing, HR, Finance, Operations and so on!
You can choose your level playing field and try to learn something new each day. That’s the key to elevate I believe.
Here are the generic skill sets that I think are most relevant:
I firmly believe that any skill can be learned. By and large, Analytics is a multi faceted field! Four critical non-technical skills that are required are:
Curiosity: This is the most important trait one should have. Curiosity to learn, be it in any domain, matters a lot. You can be really good in Sales/Marketing and if you wish to take up an assignment in HR, you should be open-minded to learn new business aspects.
Analytical Thinking: Most often, we face problems that would be really challenging to solve! You need to frame the structured problem statement from the unstructured & vague problem area.
Problem-Solving: You don’t need to be a pro in say programming. When you’re clear about what problem you need to solve using programming, you can research and design an algorithm. In good old school days, we use paper and pen. Nostalgic, right? We can get back to that era again by drafting the solution using the same way which is effective. Read articles, whitepapers online which would help you understand how somebody resolved the problems.
Insights Storytelling: Interpretation of data from the analysis you carried is yet another crucial step of the analytics project. You would need to present the findings/solution to the leadership team in a layman’s language! Conveying your efforts in the form of a story and influencing others to buy your deliverable is an art!
The above holds true for data scientist skill sets as well. However, I’d encourage you to go through an article at KDnuggets.
What do you think is the number one skill required for Analytics? I’m looking forward to read your thoughts via comments below.
Often, we might hear the words – Analysis and Analytics – being interchanged in our usage. Are they quite similar? The answer is no. There’s a fine line of difference between these two terms. Here are my two cents 🙂
Analysis is a way to interpret the data and derive meaningful insights from the data. Essentially, you may use the analytical tools such as Microsoft Excel to plot the graph, pivot, chart to delve into the subject of interest. Let’s take a very simple example: Your executive wants to know, “Who are the top 10 salesforce folks who exceeded the targets this year in U.S. region?”. Well, you can extract the U.S. sales data from the tool and sort it by descending order to arrive at the top 10. Your leadership team might think of a surprise gift vouchers to them as a token of hard-work and determination!
Analytics: This also holds true in deriving meaningful insights from the data. The difference is, analytics involves statistical tools & techniques with business acumen to bring out the hidden patterns, stories from the data. I would say analysis is a sub-set of analytics whereas the latter involves some complex techniques to solve the problem. Ex: Google recommends you search ideas when you start typing your keywords. Let’s say, you want to know “how to make a website”. Google has the search data from your country’s demographics who had already searched about the similar keywords. Using machine learning algorithm in real-time, your search query is suggested by the search engine before you complete the keywords!
In a nutshell, Analytics = Business + Statistics (Applied Maths) + Computer Programming. Using the statistical tools & techniques, a business problem is solved and that’s analytics for you guys!
Next time, if somebody switches these two terms, ensure you get it clarified! It doesn’t change the context drastically but technically you should be well informed about the discussion points.
If you have any questions or anything to be shared, please write it in the comments section below. I look forward to hear from you.
