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February 20, 2013

Big Data, Multimedia, Sentiment Analysis & Monetization

As I mentioned in my previous post, Big Data must create value - real and tangible economic value for it to be meaningful. And it must do more than just what a traditional Datawarehouse could achieve. Taking structured transactional data, putting it in a Datawarehouse and mining it for statistical analysis to obtain customer insights is nothing new or revolutionary for all of us to spend so much time talking about. The maximum amount of data (and one with largest growth rate) that is being generated nowadays is unstructured and with cameras in every phone, a lot of this data consists of multimedia like video and pictures. Tagging these videos with metadata and annotations tries to put some structure around these large files. For example, when you watch a video on Youtube, how does it know what else to "recommend", what to "feature" and what to "suggest"? Youtube's search ranking algorithm tries to constantly stay ahead of the game by delivering the most relevant and engaging content, so as to optimise the return-on-investment for its advertisers. Similarly, TripAdvisor's ability to put structure around the large volume of its unstructured data (namely hotel reviews, pictures, ratings, etc.) is proving to be financially successful for both itself and its partners. One reality of all this large volume of data being created rapidly is how do you stay ahead of the game and continuously innovate to make more money than your competitors?

 

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For purposes of illustration, we can look at Hotel industry, its adoption of Sentiment Analysis and how it has used the technology to positively influence its room pricing abilities. Hotel industry is abuzz with something called Online Reputation Management. Companies like Radian6 (SalesForce Subsidiary in Canada), ReviewPro (Spain), Brand Karma (US) Hootsuite (US), ViralHeat (US) and SocialNuggests (US), ClaraBridge, SentiRate and TrustYou(with US based subsidiary called Review Analyst) along with the field's largest player TripAdvisor are all trying to tame the large volume of data consisting of hotel reviews in meaningful manner. Additionally, it is creating substantial all-round economic value. For hotels it's creating the ability for them to charge higher premiums compared to their competitors. For customers it is giving them the ability to get maximum value for their money. For technology providers, it is creating ability to offer a host of new age services both to the businesses like hotels as well as directly to customers. Larger technology players like Tripadvisor are minting money multiple ways. From offering value added services to customers by becoming the default site for hotel value comparisons (both price and reputation comparisons), they are able to sell digital real estate to the highest bidder for ad space as well as charging commissions to hotels for bookings generated from their website. In the end both the customer and the hotels get exactly what they are looking for -increased value for their money....

Monetization of Big Data is one of biggest challenge that the technology players are constantly working to solve and when done right, it unlocks tremendous economic potential for everyone...

Next imagine a world of video reviews (like Youtube) and picture reviews (hint: Pinterest) all getting organised and sorted to provide meaningful analysis for customers shopping the market for their ideal honeymoon destination or their dream vacation or just a weekend getaway... Tremendous opportunity with lots of money for everyone to make...

Big Data Enabled Enterprise

Larger enterprises have extremely high volumes of data, coming in at a rapid pace from a wide variety of sources. Without a proper Big Data solution, finding relevant relationships is like fishing in the dark. For chief information officers, priority is to enable their businesses to make better decisions faster.

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February 19, 2013

Big Data: Custom Analytics Helps Gain Competitive Insight

In my last blog, we briefly touched upon how critical it is for enterprises to identify the right data sources for their big data strategy. The next step is to break down the data to a greater level of granularity in order to glean relevant insights.

Custom analytics is the coming together of breadth of knowledge about how social data mining works and deep knowledge of the industries that the enterprises are focused on.

Consider for example, a scenario for the health care industry. Flu is a frequent health problem, and it spreads like wild fire. Pharmacists and hospitals would do well to stock up on the required medicines, and more importantly, the vaccines to prevent virulent attacks. How can data help here? 
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Health care providers are probably gathering data from multiple web sites where people post symptoms and ask questions about various health problems. Around the time flu peaks, people probably start posting similar symptoms.

A smart combination of data gathering coupled with the right algorithms to capture these symptoms, match patterns, and provide reports that warn of a potential flu outbreak, will help pharmacists pre-order flu vaccines. It will also help hospitals gear up to handle the deluge of flu patients, and give them the required care.

Along a similar vein, the next step for enterprises, after identifying the right data sources, is to work with experts to help build custom analytics solutions for their data. How an enterprise wants to interpret its data is driven by the business context of the enterprise, and what is most relevant to their customer base.

Custom algorithms help enterprises establish the impact of who did what and when and take course-corrective action promptly.

Predictive analytics gains more power with more real-time data. With access to sources such as company web sites and logged in customer profiles, it becomes simpler to provide real-time recommendations to customers based on a trail of their previous actions.

This smart and real-time analytics lends itself to a custom analytics hub where enterprises can build use cases on the fly, process them in instantly, and create a transaction-based marketplace that helps customers make informed choices, and helps businesses make inroads into unchartered waters.

February 14, 2013

What actually is Big Data? - The different definitions

When I first heard the term, it resonated strongly with me. It was probably because Database Management, Relational Databases, Database schemas, DataWarehousing and Data mining had always been my field of interest, right from the days when I first started my career as Business Analyst in Singapore Airlines (SIA) several years ago. Even back in the day (late 90s, early 2000s), SIA used to get competitor fare data from MIDT, among other sources to try and optimise the potential fares that it could charge on the various sectors (and combinations of sectors) that it used to fly to. In Airlines, this practise of using historical data (both your own and competitors) to optimise future fares is known as Yield Management and/or Revenue Management. The practise has been officially in existence since the mid-80s when American Airlines then CEO - Robert Lloyd Crandall invented and named it. It later spread to other related sectors like hotels and hospitality. Revenue management in hotels is an equally sophisticated field today. Top organisation like IATA (International Air Transport Association) have started offering courses for "Airline Revenue Management" and Cornell University's School of Hotel Administration offers similar programs for hotels' revenue management professionals. It has spawned an entire sub-industry within the IT Products sector catering to Revenue management for Airlines like Sabre's Revenue Manager, Amadeus's Altea Revenue Management among others. Revenue Management for hotels gave rise to IT  product companies like IDeaS and EasyRMS. So with such sophisticated IT products and business analytics that have existed in these sectors since almost the advent of Internet, what is changing now, how is Big Data affecting it and what are the opportunities that Big Data is creating for Airlines and Hotels?

But before we get into those details, I wanted to establish the baseline/benchmark about what exactly is Big Data? So I scoured the Internet to find the multiple viewpoints that exist on its definition and try to reconcile all that into a single coherent and useful definition.  The definitions I encountered from reliable sources are verbatim as per below (intentionally leaving out Wikipedia as Wikipedia is an information aggregator and not a creator or original content):

Gartner:  Big Data in general is defined as high volume, velocity and variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.

Forrester: Big Data is the frontier of a firm's ability to store, process, and access (SPA) all the data it needs to operate effectively, make decisions, reduce risks, and serve customers

IDC: Big data technologies describe a new generation of technologies and architectures, designed to economically extract value from very large volumes of a wide variety of data, by enabling high-velocity capture, discovery, and/or analysis.

McKinsey Global Institute: "Big data" refers to datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyse. This definition is intentionally subjective and incorporates a moving definition of how big a dataset needs to be in order to be considered big data--i.e., we don't define big data in terms of being larger than a certain number of terabytes (thousands of gigabytes). We assume that, as technology advances over time, the size of datasets that qualify as big data will also increase. Also note that the definition can vary by sector, depending on what kinds of software tools are commonly available and what sizes of datasets are common in a particular industry. With those caveats, big data in many sectors today will range from a few dozen terabytes to multiple petabytes (thousands of terabytes).

Oreilly: Big data is data that exceeds the processing capacity of conventional database systems. The data is too big, moves too fast, or doesn't fit the strictures of your database architectures. To gain value from this data, you must choose an alternative way to process it.

Microsoft: The increasingly large and complex data that is now challenging traditional database systems

Oracle: Big data is the data characterized by 4 key attributes: volume, variety, velocity and value

IBM: Big data is the data characterized by 3 attributes: volume, variety and velocity

If I look at the myriad definitions above and try to create something that is relevant and meaningful to my cause, I would define Big Data as follows:

Big Data is high-volume, high-velocity and high-variety information assets  which require reasonable levels of veracity in turn creating substantial economic value  and helping in effective operations, revenue enhancement, decision-making, risk management and customer service.

Agree? Disagree? Suggestions for improvement? 

I look forward to hearing from you, as I further pen my thoughts on Big Data and its application in the ever so dynamic and exciting field of revenue management in Travel & Hospitality sectors,specifically ... Stay tuned...