Posts Tagged ‘Kognitio’

Monday’s Musings: Beyond The Three V’s of Big Data – Viscosity and Virality

Revisiting the Three V’s of Big Data

It’s time to revisit that original post from July 4th, 2011 post on the the Three V’s of big data.  Here’s the recap:

Traditionally, big data describes data that’s too large for existing systems to process.  Over the past three years, experts and gurus in the space have added additional characteristics to define big data.   As big data enters the mainstream language, it’s time to revisit the definition (see Figure 1.)

  1. Volume. This original characteristic describes the relative size of data to the processing capability. Today a large number may be 10 terabytes.  In 12 months 50 terabytes may constitute big data if we follow Moore’s Law.  Overcoming the volume issue requires technologies that store vast amounts of data in a scalable fashion and provide distributed approaches to querying or finding that data.  Two options exist today: Apache Hadoop based solutions and massively parallel processing databases such as CalPont, EMC GreenPlum, EXASOL, HP Vertica, IBM Netezza,  Kognitio, ParAccel, and Teradata Kickfire
  2. Velocity. Velocity describes the frequency at which data is generated, captured, and shared. The growth in sensor data from devices, and web based click stream analysis now create requirements for greater real-time use cases.  The velocity of large data streams power the ability to parse text, detect sentiment, and identify new patterns.  Real-time offers in a world of engagement, require fast matching and immediate feedback loops so promotions align with geo location data, customer purchase history, and current sentiment.  Key technologies that address velocity include streaming processing and complex event processing.  NoSQL databases are used when relational approaches no longer make sense.  In addition, the use of in-memory data bases (IMDB), columnar databases, and key value stores help improve retrieval of pre-calculated data.
  3. Variety A proliferation of data types from social, machine to machine, and mobile sources add new data types to traditional transactional data.  Data no longer fits into neat, easy to consume structures. New types include content, geo-spatial, hardware data points, location based, log data, machine data, metrics, mobile, physical data points, process, RFID’s, search, sentiment, streaming data, social, text, and web.  The addition of unstructured data such as speech, text, and language increasingly complicate the ability to categorize data.  Some technologies that deal with unstructured data include data mining, text analytics, and noisy text analytics.

Figure 1. The Three V’s of Big Data

Contextual Scenarios Require Two More V’s

In an age where we shift from transactions to engagement and then to experience, the forces of social, mobile, cloud, and unified communications add  two more big data characteristics that should be considered when seeking insights.  These characteristics highlight the importance and complexity required to solve context in big data. More…

Monday’s Musings: The Three V’s of Big Data

The Three V’s Traditionally Define Big Data

Traditionally, big data describes data that’s too large for existing systems to process.  Over the past three years, experts and gurus in the space have added additional characteristics to define big data.   As big data enters the mainstream language, it’s time to revisit the definition.

  1. Volume. This original characteristic describes the relative size of data to the processing capability. Today a large number may be 10 terabytes.  In 12 months 50 terabytes may constitute big data if we follow Moore’s Law.  Overcoming the volume issue requires technologies that store vast amounts of data in a scalable fashion and provide distributed approaches to querying or finding that data.  Two options exist today: Apache Hadoop based solutions and massively parallel processing databases such as CalPont, EXASOL, GreenPlum, HP Vertica, IBM Netezza,  Kognitio, ParAccel, and Teradata Kickfire
  2. Velocity. This characteristic describes the frequency at which data is generated, captured, and shared. The growth in sensor data from devices, and web based click stream analysis now create requirements for greater real-time use cases.  The velocity of large data streams power the ability to parse text, detect sentiment, and identify new patterns.  Real-time offers in a world of engagement, require fast matching and immediate feedback loops so promotions align with geo location data, customer purchase history, and current sentiment.  Key technologies that address velocity include streaming processing and complex event processing.  NoSQL databases are used when relational approaches no longer make sense.  In addition, the use of in-memory data bases (IMDB), columnar databases, and key value stores help improve retrieval of pre-calculated data.
  3. Variety A proliferation of data types from social, machine to machine, and mobile sources add new data types to traditional transactional data.  Data no longer fits into neat, easy to consume structures. New types include content, geo-spatial, hardware data points, location based, log data, machine data, metrics, mobile, physical data points, process, RFID’s, search, sentiment, streaming data, social, text, and web.  The addition of unstructured data such as speech, text, and language increasingly complicate the ability to categorize data.  Some technologies that deal with unstructured data include data mining, text analytics, and noisy text analytics.

The Bottom Line: Start With Your Business Objectives

In Stephen Covey’s book, Seven Habits of Highly Effective People, he starts with a saying, “Begin with the End in Mind”.  For big data projects, ask the key questions.  What patterns will you uncover that will change how you go to market or address fraud?  Can you apply sentiment and location to create new customer experiences.  What additional insights can help you create new and disruptive busienss models?  Big data is just a technology and tool.  How you apply this tool to your business models and objectives will determine whether big data is a luxury or a necessity.

Your POV

What business problem will require you to start with Big Data?  What are the key outcomes?  Where do you expect to move the needle?   Add your comments to the blog or send us a comment at R (at) SoftwareInsider (dot) org or R (at) ConstellationRG (dot) com

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News Analysis: Capgemini Immediate Delivers Cloud Services To Royal Mail Group

Capgemini Changes The Rules Of The Cloud Game

On July 27, 2010, Capgemini announced a six-year cloud computing deal with Royal Mail Group (RMG).  The partnership brings the capabilities of Capgemini’s Infostructure Transformation Services (ITS) and Capgemini Immediate to RMG.  As the UK’s second largest employer, RMG employs 188,000 people, handles over 80 million items per day, and delivers over 150,000 parcels per day via ParcelForce, its worldwide express parcel business.  Analysis of the deal reveals two key points:

  • Royal Mail Group chooses cloud computing for concrete business value. RMG sought a new eBusiness platform.  Through the RFP process, RMG determined that traditional on-premise software and hardware solutions on single stack technologies (e.g. Microsoft, Oracle, and IBM) did not meet current and future business requirements.  Requirements included decreasing the time to market to deliver new solution offerings, delivering pay-as-you-go services to meet the needs of the organization’s personal and small or medium business customers, and supporting RMG’s innovative parcel delivery services to keep up with the UK’s online shopping boom.  After careful analysis, RMG realized they would have to go best of breed.

    Point of View (POV):
    With over 3000 web pages and 100 applications, RMG felt the dual weight of transforming legacy applications and the need to free up resources for innovation.  As with many legacy systems, changes to their current eBusiness platform most likely took too long to implement and the integration challenges of managing a specialized and aging e-business environment became too cumbersome to manage.  RMG chose Capgemini Immediate because the solution delivered an ecosystem of solutions as one offering with Capgemini acting as both the services integrator and prime contractor.  RMG gained both the business value in best of breed solutions and the flexibility of the cloud computing model.
  • Capgemini Immediate mitigates the challenges of managing SaaS best of breed “hell”. Capgemini’s integrated best of breed cloud offering includes 18 initial SaaS and open source suppliers across the software-as-a-service (SaaS) and platform-as-a-service (PaaS) layers of cloud computing.  Key examples of core PaaS components delivered immediately to the customer include Drupal (Content Management), Apache Software Foundation (Common UI service), IBM Infosphere Datastage (ETL), Cordys (Business process orchestration), Attenda (Business activity management), and Talis (Semantic data management).  For example, the marketing and eBusiness SaaS offering includes Salesforce.com (Customer transactions), Demandware (eCommerce), Kognitio (Data Warehousing-as-a-Service), Ominiture (Web analytics), Eloqua (Online marketing) and Google (Search) see (Figure 1).

    POV:
    Leading companies who seek best of breed approaches often face challenges in integration and managing multiple vendor contracts.  The Capgemini Immediate offering reduces the risk of best of breed because clients sign one contract and Capgemini manages the delivery risk, SaaS and hybrid integration, and the management of partners.  In addition, the on-demand pricing and delivery model enables organizations to manage seasonal peaks such as holidays that may require excess capacity.  Best of breed solutions can link back to the RMG ecosystem with ease allowing for more choices among application solutions.

Figure 1. Capgemini Immediate Provides A Best Of Breed E-Business Platform In The Cloud

Source: Capgemini

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