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Posts tagged ‘Facebook’

12
Aug

Quantified Self, Ubiquitous Self Tracking = Wearable Analytics


google_glassesThe future is here. It’s just not evenly distributed yet.”   – William Gibson

Self-tracking,  Seamless Engagement and Personal Efficiency improvement’s new frontier is Personalized Big Data and Digital Health. This is really becoming a viable idea around wearable and sensor computing and the basis for new data platform wars.

The new platforms for digital life or data driven life — that collect, aggregate and disseminate — will  cover a wide range of new User Experience  (UX) use cases and end-points… medical devices, sensor-enable wristwear, headset/glasses, tech-sensitive clothing.  All of them are going to collect a lot of data, low latency analytics, and enable  data visualization. Several new firms are entering the activity tracker market LG (Life Band Touch), Sony (the Core), Garmin (Vivofit), Glassup, Pebble, JayBird Reign etc.

Data collection is just one piece of the solution. The foundation for personalized big data is Descriptive and Predictive Analytics.  Ok…What do i next? what is the suggestion? in the form of predictive search (automated deduction or augmented reality).

How do i discover useful patterns, analyze, visualize, share, query and mobilize the collected data?  A wide range of start-ups – Cue, reQall, Donna, Tempo AI, MindMeld, Evernote, Osito, and Dark Sky – and big companies like Apple, Google, Microsoft, LG and Samsung are working on predictive apps — aimed at enabling new robo-assistants that act as personal valets, anticipating what you need before you ask for it.

DataLeverage

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11
Jun

NSA PRISM – The Mother of all Big Data Projects


Prism9As a data engineer and scientist, I have been following the NSA PRISM raw intelligence mining program with great interest.  The engineering complexity, breadth and scale is simply amazing compared to say credit card analytics (Fair Issac) or marketing analytics firms like Acxiom.

Some background… PRISM – “Planning Tool for Resource Integration, Synchronization, and Management” – is a top-secret data-mining “connect-the-dots” program aimed at terrorism detection and other pattern extraction authorized by federal judges working under the Foreign Intelligence Surveillance Act (FISA).  PRISM allows the U.S. intelligence community to look for patterns across multiple gateways across a wide range of digital data sources.

PRISM is unstructured big data aggregation framework — audio and video chats, phone call records, photographs, e-mails, documents,  financial transactions and transfers, internet searches, Facebook Posts, smartphone logs and connection logs – and relevant analytics that enable analysts to extract patterns. Save and analyze all of the digital breadcrumbs people don’t even know they are creating.

The whole NSA program raises an interesting debate about “Sed quis custodiet ipsos custodes.” (“But who will watch the watchers.”) Read more »

31
Oct

Email Marketing is a Predictive Analytics Problem


targeted segmentation for email using big dataDigital Marketing from 1999 to 2012

In his book Permission Marketing, Seth Godin referred to email marketing as “the most personal advertising medium in history”.  That was 1999.

Where does email marketing stand in 2012 in the age of social media, omni-channel marketing and big data analytics? Here are some interesting data points.

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22
Oct

Sizing “Mobile + Social” Big Data Stats


“Welcome to the Internet of Customers. Behind every app, every device, and every connection, is a customer. Billions of them. And each and every one is speeding toward the future.” Salesforce.com 

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Mobile and social are major data exhaust producers. Mining this data is the new frontier. Did you know that every 60 SECONDS, a tidal wave of unstructured data is being produced, consumed and archived via mobile devices.  As you read this ask yourself: what does this mean?

The smartphone has revolutionized the way we communicate, search, shop, share, purchase and stay connected. What were only concepts 10 years ago are reality today.

The smartphone industry is massive, with close to 2 billion devices shipped annually and total spending on wireless-related services of more than $1.6 trillion across the world. As mobile devices increasingly serve as the center of the consumer’s world, their importance to a range of companies is increasing.

I am convinced that analytical insights coupled with Mobile Technology (action enabler and insight consumption channel) will profoundly change consumer behavior and the basis of competition. All customer touch points (loyalty, e-mail, web, social, payments, e-commerce, coupons) are converging on the mobile phone. A whole new form of mobile customer engagement is just starting to take shape.

Companies are racing to comeup with new ways to leverage “next best action/offer” analytics in a world where customer experience is getting more complex.  Take retail for instance. In a multi-channel and multi-device world, as consumers move across channels, new techniques are needed to capture and increase conversion rate. (Conversion rate is the percentage of people who come to your website and take desired actions, such as purchasing something or requesting more information.)

Imagine this scenario…. let’s say a friend tweets about a new 60 inch Samsung smart TV they bought at Best Buy. You read the tweet, but click on the URL on the mobile device and check it out.  Even though that was the last click, what made the transaction happen was a satisfied friend posting a recommendation via social media and retrieved on a mobile device.  The ability to convert the visitor requires analytics… where they came from, what caused them to come to the site, what offer to present, etc.

Social technology adoption and usage by consumers is no longer an early adopter market — it’s a mainstream activity.  Mobile is accelerating this trend. All this means a “new customer interaction” model powered by big data is emerging.

Why is big data analytics a good lens for creating value around social:

  • New data is coming across multiple dimensions – demographic, geographic, psychographic, behavioral, socialgraphics
  • Business decisions approach real-time. Time available to capture data is decreasing.  Analysis of increasing data volumes have to become faster. Operational excellence requires immediate action.  Real-time capture and action is where the state of the art is.
  • Coupled with mobile and cloud, it means the emergence of a new Customer Interaction Model for corporations

All this data growth and value creation trends imply that data management, Big Data and real-time analytics is  a big focus in social and mobile data going forward.  Clearly a new style of IT is emerging (see this figure from HP Analyst Briefing which conveys the computing transformation message quite well).

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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 »

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 »

15
May

New Tools for New Times – Primer on Big Data, Hadoop and “In-memory” Data Clouds


Data growth curve:  Terabytes -> Petabytes -> Exabytes -> Zettabytes -> Yottabytes -> Brontobytes -> Geopbytes.  It is getting more interesting.

Analytical Infrastructure curve: Databases -> Datamarts -> Operational Data Stores (ODS) -> Enterprise Data Warehouses -> Data Appliances -> In-Memory Appliances -> NoSQL Databases -> Hadoop Clusters

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In most enterprises, whether it’s a public or private enterprise, there is typically a mountain of data, structured and unstructured data, that contains potential insights about how to serve their customers better, how to engage with customers better and make the processes run more efficiently.  Consider this:

  • Online firms–including Facebook, Visa, Zynga–use Big Data technologies like Hadoop to analyze massive amounts of business transactions, machine generated and application data.
  • Wall street investment banks, hedge funds, algorithmic and low latency traders are leveraging data appliances such as EMC Greenplum hardware with Hadoop software to do advanced analytics in a “massively scalable” architecture
  • Retailers use HP Vertica  or Cloudera analyze massive amounts of data simply, quickly and reliably, resulting in “just-in-time” business intelligence.
  • New public and private “data cloud” software startups capable of handling petascale problems are emerging to create a new category – Cloudera, Hortonworks, Northscale, Splunk, Palantir, Factual, Datameer, Aster Data, TellApart.

Data is seen as a resource that can be extracted and refined and turned into something powerful. It takes a certain amount of computing power to analyze the data and pull out and use those insights. That where the new tools like Hadoop, NoSQL, In-memory analytics and other enablers come in.

What business problems are being targeted?

Why are some companies in retail, insurance, financial services and healthcare racing to position themselves in Big Data, in-memory data clouds while others don’t seem to care?

World-class companies are targeting a new set of business problems that were hard to solve before – Modeling true risk, customer churn analysis,  flexible supply chains, loyalty pricing, recommendation engines, ad targeting, precision targeting, PoS transaction analysis, threat analysis, trade surveillance, search quality fine tuning,  and mashups  such as location + ad targeting.

To address these petascale problems an elastic/adaptive infrastructure for data warehousing and analytics capable of three things is converging:

  • ability to analyze transactional,  structured and unstructured data on a single platform
  • low-latency in-memory or Solid State Devices (SSD) for super high volume web and real-time apps
  • Scale out with low cost commodity hardware; distribute processing  and workloads

As a result,  a new BI and Analytics framework is emerging to support public and private cloud deployments.

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