Why data of all sizes and complexities including “Big Data” should be “Happy”

My blog is centered on the theme of making a conscious decision to begin to view data as if it was “alive” with all the complexities and mysteries of a human being. By taking this approach I hope to provide a journey and a platform to spark a conversation on how this perspective can then begin to change on how we act towards data and how our decisions around data might then change. Yet, if I do take this premise then it is in my personal opinion that at the end of the day that my data or data that I personally interact with it or have responsibility of will be “Happy Data”.

When I studied biopsychology (combo of psychology + neuroscience) at UCSD we would often look at how biological processes interact with emotions and cognition. As I was earning my degree too often the common debate of nature vs. nurture would be highlighted in this branch of psychology. Over the years, I truly believe that the difference of nature vs. nurture is very important but there is strong importance the relationship between both of them. By nature you carry the traits that might define you but you are nurtured to become the human being that you become as an adult by your interactions. Those interaction can start with your family environment (data in your organization), your extended family (data loosely related) , peer experience (how data interacts with other data), and extending to influences in socio-economic status(will you make different decision around your data in you are economical sound). So if my goal is to make sure that at the end of the day my data is happy what can I do to make sure this happens? What should I consider in the DNA of my data? What things should I consider to provide a positive environment as my data is maturing in my ecosystem?

Nurture: In the beginning there was “Little Data”

I have a strong passion for Analytics so a lot my examples going forward would probably gravitate towards that subject. (Yet, I will try to change around in future postings)Lets then look at my first example on when an organization has decided to launch existing product line in a different channel. Let’s say that this organization has traditionally provided this product only via direct to consumer over the web and print channel (catalog). Now they want to have physical presence were can have a more intimate relationship with the customer and have begun to roll their products via kiosk in a mall. It is anticipated that mix % of these new distribution channel might increase to 15% in 12 month period so they are being cautious not to tarnish the brand but also cognizant that there is certain opportunity cost if they do not move fast enough. Both the folks in marketing and product development might have decided that it was more important to the launch the product quickly then to see if the proper process of capturing the entire 360 degree touch points of the customer. In this example, the organization rolled out the product and did not think about the various components that the transactions with the customer might be different that on the web. Thus, it is treating the data with a limited view. So the data is small and young at the beginning of this process. If the data was alive like a human being would you wait until the data grows or would you try to deal data at a different cycle of the process? It is best to think about it, listen to it, analyze it, interpret it, treat it, nurture it, and protect it (we will talk about security in detail in later blogs) at a stage that it is not as complex and the size is manageable. You also have a stronger chance to nurture it along the process and can influence the outcome of this data by beginning a relationship with it earlier on. You are able to change some the environment and process when you begin to understand the importance of this data in the future.

I will try to get into more details on different examples on different stages in maturity and complexities of data going forward on other postings. I did not get into too much detail given that I wanted to introduce this subject first. I am excited to see in other discussion what we should consider in your organization if the data might be unstructured and rebellious, how then you would then need to act around it. Also, if you have old enterprise data that has been there a long time what are different ways to deal with historical and older data. Regardless, your data should be happy and you should consider how to get there. Can you provide an example in your organization that if you had taken this approach the outcome would be different? Did I miss something or angle that I should have considered? Thank you and please share your comments.

Kain A Sosa VP, Analytics at Bodhtree with expertise on various big data technologies, like Hadoop, Big query, Passionate leader in Data Analytics, Business Intelligence, and Big Data services.

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