Linkedin Inmaps

I thought I'd use an illustration of my connections on LinkedIn Inmaps to emphasize both the power of Visual Analytics (A picture is worth a thousand words. Let's see if this blog-post reaches 1000 words :p) as well as talk further about what differentiates an analysis from a report.

Before I get into what this image reveals, I wanted to highlight a couple of things. This image is a unique way to represent very complex and large n-dimensional dataset. In the age of Visual Analytics and the popularity of various VA tools such as Tableau, Qlikview, Microstrategy and JMP, this is a neat neat way of representing really complex multi-dimensional data (All people who you are connected to and everyone they are connected to is probably going to become a Million rows x Million columns dataset. I am not sure if this visualization has looked at second level connections in which case the data becomes even more complex). Another key insight from this is the fact that Color is one weapon that we don't use often enough in projecting charts to represent multi-dimensional complex data.

This representation captures quite a few things and projects these insights in a simplistic fashion:

1. What are the various social/professional/educational networks you are a part off?
1. Big Clusters in different colors
2. I had 4 major clusters (HKUST/LBS on the West representing the global network largely based in HK and London , Mu Sigma and Dell on the East representing analytics in Bangalore and UVCE and Infosys/Gap on the South and North respectively representing the IT side of Me)
2. How well-connected are these networks?
1. Size of the clusters
2. HKUST is a large well-connected cluster and so is Mu Sigma. UVCE is much smaller and less well-connected.
3. Who are the people who are connectors across networks?
1. Dots that are between two clusters and of a reasonable size
2. Santosh Atre, Tapan and Navin D connects Infosys and Mu Sigma and Bharath Murali connects Bethany and Mu Sigma networks. Deepak GD connects PESIT Gang and HKUST.
4. Who are the key people in your network?
1. Size of the bubble indicates how well connected they are.
2. If you can identify 4-5 people in each network spread across different networks and stay in touch with them, it might be more efficient than trying to be in touch with everyone in your network. This does have a materialistic angle to connecting to people. But all I am saying is if you had to choose 3 people to get in touch with to get the scoop on what's happening in your school gang, this map probably gives you a good idea.
5. Who will be most useful when you have a unique need?
1. Possibly someone who is not very well-connected to any of your networks who appears to have a good network (Big Bubble).
2. Reasoning: They have good exposure to multiple people in possibly a different field or atleast a different network and will be able to connect you to a unique network of people that you don't have access to. This could be a distant cousin in the real estate broking space who can connect you to a builder friend who will help with building your next house.

Now that I've given you some key questions that get answered with that one image, is the image a report or is it analytics. It depends on who's talking about it (I guess). LinkedIn would call it the best analytics ever whereas someone who saw it but did not get enough context to what this was about or why it is important to him/her thought it was a useless report. While this definitely fits the bill of a Visual Analytics representation of complex data, it would be good analytics only if backed by reason why this is useful to the viewer, what the key insights are and what actions he should take based on the insights. Does this blog-post then qualify as good analytics? ;-) I'll let the readers of the blog decide.

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What really is Analytics?

I get this question so so often. I finally decided to dedicate a blog post to this oh so often asked question. I sometimes try and make an honest effort to make people understand and more often than not, give in and say yes to everything they describe analytics as just so that I don't need to give an hour long explanation and still not be able to make people understand. 


This reminds me about how when we first got into the IT industry, everyone assumed IT was all about Y2K and how we would all be jobless post 2000. I used to get tired of explaining that the industry wasn't going to close down so soon. It didn't help that the dot com bust happened exactly around that time making it even tougher for us to defend the IT industry and our reputations as IT professionals.


Coming back to the topic of this post, let's start off with what Analytics is not. 

1. Analytics is not BI even though there's nothing less intelligent about Business Intelligence practitioners/experts. 
2. Analytics is not Data Warehousing even though it was the most cutting edge technology/phrase a few years back and we all (including yours truly) aspired to be part of the DW brigade purely because of the demand in the job-market. 
3. Analytics is not Reporting even though it has a very critical place in the data-driven decision making space. They provide some really important data to analytics practitioners that if used well can save tons of effort for us.
4. Analytics is not about OLAP Cubes nor is it about Databases even though OLAP cubes are super-cool tools and can really help you do analytics. 

Now that we know what it is not, what is Analytics really? 

1. Analytics is making sense of raw data, identifying trends and recommending actions to people who either don't have access to the data, don't have the time/ability to make sense of the data or need help thinking of clear actions that can be taken based on the trends. 
2. Analytics is as close as one can get to Management Consulting without getting into BS land or Tell me what you want want to hear and I'll do so land (I know I'll have a few people ready to throw stones at me for this statement. But I'll still stand by it. For a long long time, Management Consulting has been about a few standard frameworks and making sure you use the frameworks to arrive at a solution that the customer wants to hear). Don't get me wrong the frameworks have its advantages and being in a nascent industry as analytics, we do have a fair share of opportunity to define some  useful frameworks.
3. Analytics is about using all of the existing reports to get a story and also drilling down further when required to identify the Whys of the trends. 
4. Analytics is about using every tool in the book (SAS, Omniture Sitecatalyst/Insights, Tableau, R, MS SSAS, Microstrategy and a million others) and every data source (Behavioral Data, Survey Data, Sec Research, Competitive Analysis among others), but with the intention to identify trends and make recommendations to leverage/overcome a trend. 
5. Analytics is about recommendations/insights that Change the Business (CTB) and rarely about Run the Business (RTB). 
6. Analytics is in short about enabling customer insights and data-driven decision making instead of gut based decision making.

These are my 2 cents on what I feel is the best way to define/describe Analytics. Have suggestions on a simpler/better way to explain what analytics is. Please feel free to share your thoughts below in the comments section or connect to me on Twitter @rhebbar or on Facebook.

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Sitting is killing you

Received this on my office e-mail and thought this was really relevant and worth sharing with you all. The thought is around how you can change your life, improve your health, lose weight, live longer by just sitting a little less and standing a little more as was the habit a few decades and a lot more a few centuries ago.

Via: Medical Billing And Coding