Is Social Mining already deciding your forecasting and Pricing?



big data analytic services

A recent study conducted by Oracle Corporation in the retail sector revealed that customers are more social media savvy and the reason behind selecting a particular brand as the best brand is customer service (post sale). If you have visited Japan, Australia or India you may have seen an “Oxygen Bar.” These are establishments that sell oxygen for recreational and consumer usage…seriously. Visit www.o2bar.com.au or Google it. When I first saw the statistics below, I felt that maybe what I really need is a “free air” bar, as in free of Social Media. But this seems impossible in today’s digital world. Social Media has not only played a major role in connecting people, it has also brought a paradigm shift in the way enterprises conduct business.

Here are some quick facts about the ever-present role social media now plays in our relationships and buying decisions:

– How demand is influenced (Forecast)

– 20% of time on PCs is spent on social media. On mobile devices, people are on social media 30% of the time (Nielson)

– Consumers are 71% more likely to make a purchase based on social media referrals (Hubspot)

– Social networks influence nearly 50% of all IT decision makers (LinkedIn – learn more at TechConnect ’12)

– 74% of consumers rely on social networks to guide purchase decisions (SproutSocial)

– Facebook is the most effective platform to get consumers talking about products (SproutSocial)

– 44% of automotive consumers conduct research on forums (Mashable)

– 81% of US respondents indicated that friends’ social media posts directly influenced their purchase decision (Forbes)

– 78% of respondents said that companies’ social media posts impact their purchases (Forbes)

It is not enough for a company to say, “I am mining social data and using Big Data technologies.” Instead companies need to clearly state and understand “What are you mining?”;”Do you understand the ROI?”; ”Do you know how it integrates with demand and pricing management?” If the answers to these questions are not clear, you may not be there yet; but should any sense of complacency arise, just ask, “Is my competitor ahead of me with social mining?”

Read More

How Tableau Helps CIOs See DATA Differently

With Tableau’s IPO this past Friday (trading under the ticker symbol “DATA”) and IDC’s title as “the world’s fastest growing Business Intelligence Company” back in 2012, Tableau is making big strides as a top data visualization tool. With more than 100,000 users, Tableau is being used in revolutionary ways to solve diverse problems from maximizing customer loyalty for a clothing brand to minimizing patient wait time in hospitals.

BI Analytics tool

Tableau is a BI analytics solution that lets you probe, question, interact and understand data through visualization. Tableau allows you to instantly filter, zoom, compare, sort and group your data to tell a story. From GI mapping to heat maps, Tableau offers many visual formats to help discover data-driven answers to business questions.

What distinguishes Tableau from other tools is how seemingly user friendly it is. Data analytics is now becoming accessible to the masses with Tableau’s easy drag-and-drop interface.  Analysts who were previously limited to interpreting reports are now empowered to build their own unique views of the data. IT developers who used to create these reports now have more time to work on proactive technology initiatives. You can share Tableau reports to a viewer, group of viewers or even the public. Tableau is additionally accessible in any web browser or on tablets.

CIO’s are looking to Tableau as a top Business Intelligence analytics tool for their businesses, recognizing even its structural power to increase efficiency amongst analysts and enable IT to delve into other projects. The accessibility and usability further brings Tableau to the forefront of CIO’s interests.

Hilary Perry is a business analyst with Bodhtree who focusses on emerging Big Data technologies.  Bodhtree empowers enterprises to navigate Big Data challenges and opportunities with its GPS solution portfolio: Growth, Productivity and Security.  To explore what your business can achieve with Big Data, contact Bodhtree at business@bodhtree.com.

Read More

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.

Read More

Why are so many customers failing in their Big Data initiatives?


I strongly believe that companies with a successful Big Data strategy have an information-centric culture where all employees are fully aware of the possibilities of well-analyzed and visualized information. Better data visualization can help you make better decisions

As a matter of fact, Gartner’s top predictions for 2012 and beyond included this prediction about Big Data: “Through 2015, more than 85 percent of Fortune 500 organizations will fail to effectively exploit Big Data for competitive advantage.” This leads to the question “Why are so many customers failing in their Big Data initiatives?”

The success of a Big Data implementation is directly proportional to the maturity model of the organization.

Remembering the Big Data project implementation experience I would like to share the approach that includes three assessment steps as mentioned below. I thought it would be insightful if I also mention here the recommendations which lead to a successful Big Data implementation.

I. APPROACH

II. RECOMMENDATION

Recommend a model, which will demonstrate the real value of Big Data as it is applicable to an organization. The final recommendations and roadmap, based on our learning’s yield one of two possible outcomes:

• If an organization already has all the necessary tools, processes, systems, and solution to solve the existing problems, then we will recommend through a business case that they are not a good contender to adopt Big Data technologies but can resolve their problems with existing ecosystem

• If an organization demonstrates the potential value of a Big Data investment, then we would recommend moving forward with next steps: take the executable roadmap and blueprint to engage in a Big Data proof of concept (POC)

III. METHODOLOGY

Organizations that approach big data from a value perspective with partnership between the business and IT are much more likely to be successful than those which adopt a pure technology approach. For this reason, making appropriate investments in both technology and organizational skill sets to ensure enterprise capability in extracting value from big data is essential.

Don’t wait, start now

Start collecting massive amounts of data and store it centralized with Hadoop, hire or train your data scientists and change your culture to an information-centric organization. This will help to drive innovation and stay ahead. Don’t wait, as Big Data is the only way forward.

Phani Kumar Reddy is a Manager Analytics at Bodhtree, Managing presales of BI with expertise on various big data technologies, like Hadoop, Big query , Passionate leader in Data Analytics, Business Intelligence, and Big Data services

Read More