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Posts from the ‘Data Visualization’ Category

23
Dec

2014 Year in PreReview for Big Data Analytics


In the movie “Minority Report,” set in 2054,Time Travel Tom Cruise plays the captain of the “PreCrime” police force, which uses “precognitive” abilities of mutants to stop crime before it happens. Silicon Valley futurists have sometimes used this reference in the context of the art of the possible with Big Data. We have another 40 years to go to see how analytics can accurately forecast future events based on human behavior. Meanwhile, imagining the future with some level of accuracy is within our reach today.

Value creation in the data economy made headlines in 2014. While Big Data continued to be the buzzword of the year in 2014, solutions that created economic impact were center stage.  Trending terms such as “predictive analytics” and “advanced analytics” approached the levels of “Big Data” on Google Trends during the year. “ROI,” which was vaguely referenced in the last two years, became the most commonly used term with Big Data in 2014. Here is a cross-section of 2014 events.

Apple announces TopsyTV

This is their next-generation TV appliance that integrates social media engagement with the TV watching experience. Earlier in 2013, Apple acquired Topsy Labs, a reseller for Twitter content for $200M. This was followed by a series of less publicized acquisitions of social media data companies. Apple is characteristically tight-lipped about its plans for monetizing this product with advertising, but speculation is rife that Apple is poised to get a piece of the $600 billion that is spent on advertising today.

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25
Jun

Power of 1% Improvement – ROI & Use Cases for Industrial Big Data


RiseOfTheIndustrialInternetJust like Business to Consumer (B2C)  e-commerce and Business to Business (B2B) e-commerce…we are seeing a similar distinction emerge in Big Data.

The “real meat and potatoes” use cases behind big data actual adoption might be around B2B machine data management and Industrial analytics enabled by wireless, battery-free sensor platforms.

While social, consumer, retail and mobile big data get a lot of PR, the big data business cases around industrial machine data analytics or “things that spin” actually make economic sense.  These projects tend to show tangible Return on Investment (ROI).

The concept of Internet-connected machines that collect telemetry data and communicate, often called the “Internet of Things or M2M” has been marketed for several years:

-       I.B.M. has its “Smarter Planet” initiative

-       Cisco has its “Internet of Everything” initiative

-       GE has its “Industrial Internet” initiative.

-       Salesforce.com has its “Internet of Customers” theme

To compete with GE….Hitachi, United Technologies, Siemens, Phillips and other industrial giants are all getting on the band-wagon as the vision of M2M is now viable with advances in microelectronics, wireless communications, and microfabricated (MEMS) sensing enabling platforms of rapidly diminishing size.

Industrial Internet – making smart use of sensors, networked machines and data analytics - is the big vision, but the business driver is in no unplanned downtime for customers.

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23
May

Data Monetization: Turning Data into $$$


DataExplosionThe billion dollar question facing executives everywhere:

  • How do I monetize my data? How do we turn data into dollars?
  • What small data or big data monetization strategies should I adopt?
  • Which analytical investments and strategies really increase revenue?
  • What pilots should I run to test data monetization ideas out?

Data Monetization is the process of converting data (raw data or aggregate data) into something useful and valuable – help make decisions (such as predictive maintenance) based on multiple sources of insight.  Data monetization creates opportunities for organizations with significant data volume to leverage untapped or under-tapped information and create new sources of revenue (e.g., cross-sell and upsell lift;   or prevention of equipment breakdowns).

But, data monetization requires a new IT clock-speed that most firms are struggling with. Aberdeen Research found that the average time it takes for IT to complete BI support requests, with traditional BI software, is 8 days to add a column to a report and 30 days to build a new dashboard.  For an individual information worker trying to find an answer, make a decision, or solve a problem, this is simply untenable. For an organization that is trying to differentiate itself on information innovation or data-driven decision making, it is a major barrier to strategy execution.

To speed up insight generation and decision making (all elements of data monetization) business users are bypassing IT and investing in data visualization (Tableau) or data discovery platforms (Qlikview). These platforms help users ask and answer their own stream of questions and follow their own path to insight. Unlike traditional BI that provides dashboards, heatmaps and canned reports, these tools provide a discovery platform rather than a pre-determined path.

Also companies like Marketo which create marketing automation software are getting into the customer engagement and data monetization game. Their focus is to enable marketing professionals  find more future customers; to build, sustain, and grow relationships with those buyers over time; and to cope with the sheer pace and complexity of engaging with customers in real time across the web, email, social media, online and offline events, video, e-commerce storefronts, mobile devices and a variety of other channels. And in many companies, marketing knits these digital interactions together across multiple disconnected systems. The ability to interact seamlessly with customers across multiple fast-moving digital channels requires an engagement strategy enabled by data and analytic insights. 

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11
Dec

20 must read Infograhics on Big Data


Big Data emphasizes the exponential growth of data volumes worldwide (collectively, >2.5 Exabytes/ day).

Big Data incorporate the following key tenets: diversification, low latency, and ubiquity. In parallel, the emerging field of data science introduces new terms including, predictive modeling, machine learning, parallelized and in-database algorithms, Map Reduce, and data monetization.

A variety of infographics have been published around Big Data, Data Scientists.  Here is a compendium of some very interesting ones.

The Real World of Big Data  (Click image to see a larger version and article)

Real World of Big Data

Big Data Big OpportunityBig Data Big Opportunity

A Data Scientist StudyA Data Scientist Study

2
Jul

Enabling SoLoMoMe + Omni-channel Analytics


At the Analytics Executive Forum, I facilitated a session on Omni-channel analytics. It struck me how every leading consumer facing firm seems convinced that mobile is becoming the dominant B2C interaction channel.  Mobile is the gateway to insight based marketing and the “always addressable customer”….

Insight-based interactions –  The company knows who you are, what you prefer, and communicates with relevant, timely messages, using the power of analytical intelligence to detect patterns, decode strands of information and create meaningful offers and value.

The “always addressable customer.” This is a consumer who fits the bill on three fronts simultaneously: (1)

  • Owns and personally uses at least three connected devices; (2)

Goes online multiple times throughout the day;  (3) 

  • Goes online from at least three different physical locations

The opposite of insight-based is “spray-and-pray” marketing - The company has very limited knowledge about who you are, forgets what you prefer, and tries to reach you with off-target communications that alienate you – based on fragmented data, poor data quality and  inadequate integration, resulting in confusing, chaotic interactions.  A good example: “I have 2 million frequent flyer miles with your airline and still do not get any recognition, respect or value from this loyalty.”

As companies architect new insight based mobile use cases I suggest that they look at what is coming next. With IOS 7, Apple is delivering several new features – Passbook, Beacon.

Retailers, banks and other customer facing firms/brands better pay attention. 100+ million iPhones are automatically getting this feature with the new OS upgrade making this a mega-disruptor in the coveted target segment everyone is chasing. Read more »

4
Jun

Omni-channel Conversion Optimization with Social Marketing/Media Analytics


Brooks Brothers is investing in tools & testing to improve the online experience – and ↑sales. In a test involving one product category page: men’s shirts. The retailer using Bazaarvoice Ratings & Reviews software, used customer reviews to sort items on the product page. Items with five ♥♥♥♥♥- the highest rating – appeared on the top of the page. The result:  a 9% lift in conversions   [Adobe Digital Marketing Symposium]

Are you ready to anticipate and influence your audience in a whole new way? Value migration from traditional marketing to 24×7 digital marketing is happening in leading firms.  Real-time marketing and conversion is now becoming possible.

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26
Mar

Machine Data and Cloud Analytics: Splunk


Machine data or “data exhaust” analysis is one of the fastest growing segments of “big data”–generated by websites, applications, servers, networks, mobile devices and other sources. The goal is to aggregate, parse and visualize this  data – log files, scripts, messages, alerts, changes,  IT configurations, tickets, user profiles etc – to spot trends and act.

By monitoring and analyzing data from customer clickstreams, transactions, log files to network activity and call records–and more, there is new breed of startups that are racing to convert “invisible” machine data into useful performance insights.  The label for this type of analytics – operational or application performance intelligence.

In this posting we cover a low profile big data company, Splunk. Splunk has >3500 customers already. Splunk ended its first day on the stock market with amazing 108.7 percent bump in price from its $17-per-share IPO. Splunk’s potential comes from its presence in the growing cloud-analytics space. With companies gathering incredible amounts of data, they need help making sense of it and using it to optimize their business efficiency, and Splunk’s services give users the opportunity to get more from the information they gather.

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20
Mar

Next Best Offer Design: Solution Architecture


Next best offer, next best action, interaction optimization, and experience optimization typically have similar architecture.  Machine learning and multivariate statistical analysis are at the heart of these cutting edge Behavioral Analytics strategies. Typically firms use statistical tools for segmentation models, behavioral propensity modeling, and market basket analysis.

The bleeding edge in next best offer is increasingly around:

  • Applying machine learning to find connections between product tastes and different affinity statements
  • Developing low-latency algorithms that help show the right product at the right time to a customer
  • Developing rich customer affinity profiles through a variety of feedback loops as well as third-party data source (e.g. Facebook user demos and taste graph)
In this blog posting we examine a more traditional next best offer solution architecture.

Targeted Offer Solutions

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2
Nov

IBM CIO Study: BI and Analytics are #1 Priority for 2012/2013


“Running a company is an endless quest to find out things you don’t know“

– Jeff Immelt, CEO GE

What will 2012 bring?  Recently, I attended the CIO Executive Leadership Summit in Greenwich, Connecticut. I was particularly intrigued by the presentation by the new CIO of IBM, Jeanette Horan where she presented the projects she was tackling and how IBM is thinking about business analytics.

IBM is making a bet that “true leaders” will develop the capabilities required for making good and timely decisions in unpredictable and stressful environments.

IBM is adapting to this new data analytics reality by a rapid-fire acquisition strategy:  Cognos,  Netezza, SPSS, ILog, CoreMetrics, Algorithmics, OpenPages, Clarity Systems, Emptoris, DemandTec (for retail).  IBM also has other information management assets like Watson, DB2 etc.  They are building a formidable capability around the value chain: “Raw Data -> Aggregate Data -> Intelligence ->Insight -> Decisions” . They see this as a $20Bln opportunity. Read more »

26
Sep

Sentiment Analytics, Twitter, Federal Reserve and Consumer Pyschology


Social media captures consumer sentimentWhat do these have in common:  The Federal Reserve Bank, Text Analytics, Facebook, Statistical Computations, Big Data and Keyword/Phrase/Boolean searching?

Interestingly these are more related than you think.

The Federal Reserve wants to develop a next generation Consumer Listening Platform based on social media sentiment analytics (or opinion mining) to know what people are saying and commenting about the economy.

The goal for the Fed is to better understand which way consumer confidence is trending. Microeconomics and psychology have always been interlinked. With social media, a real-time opportunity exists to monitor local, national and even global consumer psychology. And, coupled with analyzing e-commerce transactions, insightful linkage between consumer psychology and behavior (what they are spending money on and where) is possible. Read more »

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