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Posts tagged ‘Google’

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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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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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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