Big Data and the Goldilocks Principle

I was inspired to write this post (I can hear all of you sighing ‘Yet Another on Big Data’) due to another ‘Big’ reason. I listened to a TED (www.ted.com) talk by David Christian titled ‘The History of the World in 18 minutes’ in which he narrates a complete history of the universe, from the Big Bang to the Internet, in a riveting 18 minutes. This is “Big History”: an enlightening, wide-angle look at complexity, life and humanity, set against our slim share of the cosmic timeline. Check out his website – www.bighistoryproject.com , and I promise you that this ‘Big’ has nothing to do with Big Data, as we know it. But what got me interested in his talk is his reference to the ‘Goldilocks moment’ – a moment so precisely right for certain thresholds to be reached to enable higher forms of complexity (life) in the universe.

That got me thinking – Is Big Data the ‘Goldilocks moment’ for organizations with respect to analytics helping them towards achieving better business outcomes?

I think the answer is ‘Yes’ and this stems from the following hypothesis – An organization can utilize analytics for better business outcomes if:

a)      they have more data points to be analyzed (volume)

b)      have the ability to perform sophisticated analysis on large and diverse datasets (variety)

c)       and can do it at a much faster rate than before (velocity)

In that context, I really liked the picture (given below) from one of the IBM articles, which illustrates how Big Data when synthesized  properly along with standard transactional data can help in better business decision making (in this case, it was Fraud Detection)

Source: IBM – Understanding Big Data by Paul Zikopoulos

On the other hand, the exponential increase in processing power of CPUs, the steep fall in memory prices and high bandwidth availability, have enabled the practical use of Big Data techniques. From the human angle, people are creating digital data, viz. social media chatter, video sharing, blogs, mobility etc. at a rapid pace that organizations (with help of Big Data techniques, of course) can potentially solve the ‘Innovators Dilemma’ by providing new products and services that the consumers did not ask for simply because they couldn’t figure out what they actually want.

All in all, I think we are at a precise moment in history (the Goldilocks moment) where organizations can greatly increase their ability to provide better products & services for their consumers using Big Data techniques.

Source: http://blogs.hexaware.com/business-intelligence/big-data-and-the-goldilocks-principle/

Business Focused Analytics – The Starting Point – Part 2

Business decisions are the cornerstone of a successful BI implementation. Cataloging the decisions taken by the key stakeholders in an organization is the first step in understanding the information requirements for a data warehouse. Capturing business decisions – strategic and operational, is not a simple task as most business decisions tend to be complex requiring diverse data points. Further, when all decisions are collected, how will we know the decisions that have the most impact on the business?

Here’s a simple framework based on the six primitive interrogatives that Hexaware has effectively used while assessing information requirements. This framework helps systematically uncover important dimensions of information and organize them in a format that is easy to comprehend.

Question	Description	Comments	Example
Who?	The decision-maker	Stakeholder	Service Delivery Manager (SDM)
What?	The decision	A decision requiring supporting data / information	Resourcing for a project
Why?	The motivation for the decision	The significance of the decision to the business	Getting the right project team is critical to the success of a services project.
When?	When is the decision made	frequency (or) a point in time	Made during the planning stage and reviewed at periodic intervals
How?	The basis for making the decision – KPI / metrics / a logic	the metrics and datasets required for making the decision	By comparing the skill set requirement and project schedule in the Project plan to the  availability of resources (HR and PMO databases) with the right skills (skills database) and good track record (appraisal database)
Where?	The place where the decision is made	Specifies mobility /  additional access requirement	This information needs to be accessed through extranet

Depending on the stakeholder (mostly), the decisions could be strategic or operational. A manager responsible for carrying out a business process will have an operational view of information and will be making operational decisions relating to his/her sphere of operations.  Decisions taken by top management personnel with longer-term business responsibilities tend to be more strategic.  The above framework helps capture both strategic and operational decisions along with the datasets required to make the decisions.

Successfully capturing the decisions and the relevant metrics and the datasets is only half the story in the assessment for a data warehouse. Let’s reserve the other half for a subsequent blog.

Hope this information was useful. Please do share your comments/suggestions.

Marketing Automation in B2B – Separating the Wheat from the Chaff

The B2B landscape in its inherent form is a complex jar of beans primarily because the initial connection needs lots of nurturing with the right mix of appropriate communication to ensure the “best weather” for sales interaction. Marketers not only have to measure outcomes right up to revenue but also find the “sweet spot” for marketing and sales to drum up the right notes.

The year 2009 and the first half of 2010 saw a marked shift towards marketing automation worldwide. All of this has helped channelize information and reach out to prospects better, yet it is pertinent to note the today’s internet savvy prospect is also armed with qualifying information about your brand, your products and your competitors as never before. To get inside the mind of the B2B buyer, marketers not only need to understand his intent from his digital body language but also ensure that automated lead generation processes in place scale up in terms of the following pertinent factors at any point of time.

• Are lead recycling programs in place for not-sales-ready leads?
• Has social media, inbound marketing and marketing automation been integrated seamlessly?
• Is your marketing communication supported by buyer-centric collaterals that help the buyer decide in your favor?
• Has your data been data washed and scrubbed clean?
• Does your web metrics provide actionable information for user profiling and conversion?
• Are sales and marketing on the same latitude to proving your prospect the best buying experience?
• Does your Social media spin influence the markets conversation about your brand effectively?
• Is your opt-in list getting fresh brew in the form of persuasive communication and supporting newsletter value?
• Is your marketing funnel measurable and process definitions flexible to innovation?
• How effective is your conversation model, does it ensure that you are at top of mind when prospects decide to bite the bait?
• Are you able to capitalize on marketing automation’s great benefit – reporting effectively and use it as a strategic tool?
• Does your data-centric marketing plans lead the way for greater customer intelligence since value of data will not be a constant ?

The above are just a few important cogs that can make or break your lead generation wheel. As marketers brace themselves to capitalize on marketing automation to enhance pipeline opportunities, trends all point to an explosive growth in marketing automation adoption. It is highly imperative that automation vendors provide more sophisticated reporting, better sales engagement processes and social media integration.

To help marketing efficiently separate the wheat from the chaff, marketing automation should not just serve as a driver of operational efficiency but more importantly enhance continuity of dialogue with prospects throughout the decision making/buying cycle at all relevant touch points.

Ultimately it is all about the harvest – the pipeline and revenue, the executive leadership would not mind how you do it.