SmartAnalytics

Every Business Analytics consultant should watch the movie Moneyball (2011). This movie is indeed one of the best movies I’ve watched for a long time that showcases the application of analytics and statistics. Here’s something about the plot summary that IMDB has to say about Moneyball.

It’s really a no brainer that Customer Satisfaction matters. Every IT or Business unit  I’ve known, considers Customer Satisfatction, a very high priority and does strive really hard to ensure they engage very closely with their customers. To ensure every customer query or concern is serviced well within time. If one does go by the assumption that servicing customers in a timely manner will keep them really happy and satisfied, one would tend to focus more on Service Level Agreements.

While most of big data is geared towards social media and stream analytics, traditional EDW can also best leverage the power of Big Data. The concept of Big Data is not new, banks have been doing it for a while using mainframe size computers. The reason it’s being talked so much now is that for the first time, cheap and massive computing power and even cheaper memory has put mainframe size power in the hands of every organization, right at the time when organizations have been struggling to justify the ROI in processing such exponential data volume.

It has been a little over a year since the term “Big Data” became a catch phrase, but it still appears to evoke the same kind of response as it did then with an interesting twist. The twist being, it evoked a sense of “awe” to begin with but that is turing into a sense of “confusion”.   There is still no one crisp, common, consistent definition of the term big data, which is one of the reasons for this confusion [reminds me of the .Net days, when it referring to everything from a coding framework, to a database to office products etc confusing the heck out of everyone]

The Big Data Analytics promise: enable “data monetization” through more timely, more accurate, more complete, more granular, more frequent decisions. So, what exactly are the types of business problems big data analytics likely to solve?  For this you need a mini-MBA in Big Data Use Cases.

There is no conversation or corporate strategy presentation today without mentioning Big Data. The focus of this write-up is to understand, what Big Data is and How HR will be impacted due to Big Data:  

'Social networking' and 'Collaboration' have become a part of our lives in this connected world. Let us see how the two can work together in helping us make better decisions.

‘Social networking’ has been a growing phenomenon in the past 2-3 years. The society that we live in has almost suddenly become very ‘social’ and all issues of privacy have almost disappeared. People want to share information about their private life (family, jobs, hobbies, etc.) and have more ‘friends’ and ‘followers’ than ever before. With the advent of mobile computing, the urge to share anything, anywhere and anytime has taken off beyond limits.

‘Collaboration’ on the other hand, has been in existence for a long time with people wanting to share their documents and content using tools like document repositories, email, web/video/voice conferencing, online groups, etc. People participate in forums and groups, share ideas, contribute and work towards achieving common goals.  Collaboration has been pervasive in areas of document sharing (like sharepoint) and video conferencing (like webex) but hasn’t really taken off in the area of our current interest…BI. Collaboration in BI is currently mostly limited to sharing reports on a web portal or through emails. How many of you have seen reports that are developed in a ‘collaborative’ environment or shared with multiple consumers for collaborative decision-making?

This new age can see ‘Social networking’ and ‘collaboration’ come together and provide a platform that is more powerful than ever.  Enterprises want to make use of this powerful platform to not only develop and share the reports through collaboration, but also seek advice, set departmental and corporate priorities, influence outcomes and ensure that every decision made has consensus and approval at the same time.  Collaborative BI software like Arcplan Engage and Lyzasoft are taking a lead in this area of unmet needs.

Most of us already use social networking platforms and get that concept very well. We know how to share our ideas and how to ‘like’, ‘follow’ and ‘rate’. The idea of instant validation of ideas through your network helps in refining your ideas even further.  The same concept is applied to BI. Reports are developed by users, shared with colleagues/managers on a this collaborative platform, advice/feedback sought using the social networking features, changes/suggestions incorporated, analysis and decisions made through consensus….could life of an analyst be any better? Add ‘context awareness’ to the mix and you will find data/reports that are relevant to what you are looking for with probably no need for re-writing queries/reports.

Though this concept has been around for a couple of years, it hasn’t really caught on with majority of enterprises. Reasons could vary from the traditional mindset of ownership of data/reports to sense of independent decision-making.  It will be a while before the BI user comes of age. Mobile computing and social networking are making ‘Collaborative BI’ more exciting and I hope this new platform will drive efficiency and effectiveness in decision-making.

Big Data Analytics is now moving beyond the realm of intellectual curiosity to having a tangible impact on business. At this stage of adoption of a new technology, it becomes important to discuss how a Big Data project can move from concept to execution inside an enterprise context. Questions being asked by early adopters of Big Data include: How do I execute a SAP HANA project? How do I execute a GreenPlum data applicance project? How do I extend my legacy application to incorporate Hadoop? How do we implement Oracle Exalytics, Exalogic or Exadata? How do I upgrade my data centers to handle Big Data? 

Just attended a whole day training about Pharmaceutical sales data by Kosta Tzavaras, the renowned author of books like ‘Pharmaceutical Sales Data 101’ and ‘Patient Data 101’. The information was quite insightful and definitely helped me better understand how different sets of data available from different sources are analyzed to serve the Pharmaceutical sales force.

By 2015, nearly 3 billion people will be online, pushing the data created and shared to nearly 8 zettabytes.  Centurylink created this cool infographic to highlight the data deluge and big data issues.