Updates from February, 2015 Toggle Comment Threads | Keyboard Shortcuts

  • admin 9:56 am on February 28, 2015 Permalink
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    J D Williams and Co Ltd Selects Teradata Aster to Build Omni Channel Picture of Customer Behaviour 

    Prominent UK retailer will use Aster technology to gain customer insights and track effectiveness of marketing campaigns.
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  • admin 9:55 am on February 28, 2015 Permalink
    Tags: , , Lifecycle,   

    Data Security is a “Lifecycle” Commitment 

    Data security is one of those mission-critical issues people are always talking about and building solutions and policies around. But, even when we focus on the right problems, too many businesses are doing so at the wrong time – namely, after a breach has happened.   Determined hackers have proven again and again that they can violate just about any perimeter put in front of them. Yet somehow, many companies still wait for a breach and then use it as a catalyst for updating security designs and policies moving forward. It’s a permanent response-mode that can be futile, frustrating and costly.

    500634_data_discovery

    Better information security requires that we think more proactively and understand two basic realities:  1) The next breach is an inevitable “when, not if” kind of occurrence that will happen sooner or later, and 2) Data security is a lifecycle that involves steps you can and should take before, during and after a breach.

    Once we understand the need for continual work in the face of ongoing danger, we start to see that there is no silver bullet, no single answer to fully protecting data. And, the multi-faceted blend of technology and processes that we do come up with needs to address the inevitable nature of the threat. There are many ways to go about it. One successful approach I’ve used is to focus on three interrelated priorities.

    Manage Access to the System

    Yes, a breach will ultimately happen, but we still do as much as possible to secure normal operations and ensure that only authorized users can access designated parts of your systems. Look to incorporate robust IP filters and password controls, along with external authentication and authorization systems like Kerberos and software protocols like LDAP.  Use fine-grained access rights and privileges for user authorization, and pay special attention when managing credentials on client systems, or using applications with connection pools.

    Secure Sensitive Data

    Limiting access to certain information once someone is in your system helps govern your own users’ movements within your data architecture. But, it becomes crucially important mid-breach when you’re trying to limit damage in real-time from an intruder.  Good systems leverage strong network traffic encryption along with options like row-level security, column-level data encryption and tokenization, full disk encryption, and tape/disk backup encryption.  By compartmentalizing access, you compartmentalize damage.  The trick is to protect data while still keeping the system agile, so compartmentalizing doesn’t turn into a silo experience for your trusted users.

    Actively Audit and Monitor

    System administrators should implement strong security practices that monitor data access and flag unusual patterns in the data.  This can be crucially important when you are in the process of getting hacked and when it’s over. You can find clues via the access controls you have in place to protect the perimeter, but you can learn a lot more by also maintaining comprehensive audit logs. Forensics like this may seem like extra work in cases where the damage is already done, but understanding patterns of breach behavior can offer crucial insights. Just remember that the only thing worse than suffering a breach is suffering through the uncertainty afterward when you don’t know who hacked you, how and what data they accessed.

    Whatever approach you take, make sure you deploy, operate and accredit your systems to comply with rigorous security standards like the PCI Data Security Standard and ISO 27000 standards.

    By now you see it takes a lot of work, but a comprehensive and proactive approach to security is the surest way to protect your data. Hackers are agile and always vigilant; we should be too.

    Scott Gnau

    The post Data Security is a “Lifecycle” Commitment appeared first on Enlightened Data.

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  • admin 9:54 am on February 27, 2015 Permalink
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    Analytics Roadmap Services 


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  • admin 9:51 am on February 27, 2015 Permalink
    Tags: Appoints, CoPresidents,   

    Teradata Appoints Co-Presidents 

    Bob Fair to lead Teradata Marketing Applications Division and Hermann Wimmer to lead Teradata Data and Analytics Division
    Teradata News Releases

     
  • admin 9:51 am on February 27, 2015 Permalink
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    Do You Need A Big Data Plan? 

    Turning data into value doesn’t happen on its own. It requires a comprehensive strategy for gathering, analyzing and acting on a vast range of integrated information. Even if you’re skeptical about all the hype surrounding big data, the fact is it will play a significant role in shaping your business over the next few years.

    The new issue of Teradata Magazine poses the question, “Who needs a big data strategy, anyway?” The answer is simple—you do. Having a comprehensive plan will enable you to overcome today’s business challenges while seizing tomorrow’s opportunities. Whether you already have a plan or you’re just getting started, you’ll want to check out our Special Section, which delves into:

    • Why a blueprint is essential to exploit big data
    • Expert advice for developing best practices for strategy execution
    • Three priorities for big data success
    • Statistics demonstrating how businesses gain an advantage through big data

    Whatever your business goals—predicting outcomes more effectively, growing revenues, reaching a new customer base—a big data strategy can pave the way.

    Read the big data Special Section and more in the new Q1 2015 issue of Teradata Magazine.

    Brett Martin
    Editor-in-Chief
    Teradata Magazine

    The post Do You Need A Big Data Plan? appeared first on Magazine Blog.

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  • admin 9:47 am on February 27, 2015 Permalink
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    Driving New Insights through Next Gen Transportation Analytics 


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  • admin 9:52 am on February 26, 2015 Permalink
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    6 Ways Big Data Marketing Helps Companies Be Competitive: Part 1 

    Big data – business changing data – is giving marketers new ways to be innovative and step ahead of competitors. A creative strategy or advertising campaign is only scratching the surface of mechanisms available today to drive revenue. Effective CMOs must appreciate the power of new and diverse data sources and demand marketing directors interpret and use statistical business and customer insights to create smart strategies and quality predictive analysis.

    Understanding some of the more clever big data marketing examples helps to illustrate how marketers should be thinking analytically and creatively with non-traditional data. Consider the following big data marketing examples:

    1. Measure Social Media Impact

    Companies can measure the impact of social media with custom analytics solutions or social network analysis.

    2. Identify Your Brand Evangelists

    Identify alpha influencers and use these individuals in active marketing campaigns. Find alpha influencers not just through traditional transactions (recent purchases, customer service calls) but also through social media.

    3. Translate Big Data Insights into Actionable Marketing Tactics

    Translate big data insights into actionable marketing tactics with teams of different disciplines. The most successful are teams that work fast and are highly iterative – business, IT, and analytics specialists rapidly review real-world findings, recalibrate analyses, adjust assumptions, and then test outcomes.

    4. Create Customer Buying Projections

    Use historic behavioral data for a defined target as an indicator for behavior against a different category of product offering. For example, test payment history or upgrade likelihood for a utility service as indicators of behavior for an entertainment offering or emerging credit offering. Test into success.

    5. Understand True Value of Different Marketing Channel

    Combine sales data from traditional media and social-media sites to create a model that highlights the impact of traditional media versus activity reflected on social media (like call center interactions). Bad customer experiences are more powerful sales drivers than traditional media activity. Spending behind improving customer service can be more effective than funding advertising – to drive revenue.

    6. Pinpoint Sales Opportunities by Zip Code

    Rather than overloading sales reps with reams of data and complex models to interpret, create powerful sales tools with simple, visual interfaces that pinpoint new-customer potential by zip code. It’s a proven tactic for increased sales.

    Look for Part 2 for more examples of clever examples of big data marketing. In the meantime, see other big data examples.

     

    The post 6 Ways Big Data Marketing Helps Companies Be Competitive: Part 1 appeared first on Data Points.

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  • admin 9:49 am on February 26, 2015 Permalink
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    Peer Advantage Presents Monsanto Company SAP R3 Data Acquisition in Near Real Time 


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  • admin 9:49 am on February 26, 2015 Permalink
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    Digital Marketing Center Solution Insight Brochure 


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  • admin 9:51 am on February 25, 2015 Permalink
    Tags: Certain, , Philosophy, Uncertainties   

    Certain Uncertainties or Philosophy of Big Data 

    What does philosophy have to do with Big Data, I hear you ask. Bear with me – all will be explained.

    Donald Rumsfeld famously said “There are known knowns. These are things we know that we know. There are known unknowns. That is to say, there are things that we know we don’t know. But there are also unknown unknowns. There are things we don’t know we don’t know.”

    Donald Rumsfled, February 2002

    But the data world is not this clear-cut; not only are there things we know or don’t, there are also whole domains of data where we are just not sure.

    So we need to add two new boxes to the diagram:

    Domain 1: “I am certain of its uncertainty” – I can quantify the level of unreliability.

    Domain 2: “ I am uncertain of its uncertainty” – I know that it is not reliable data, but I don’t know how unreliable it is. Simplistic (and arguable) example: Trip Advisor data; even with a large number of reviews, I can’t be certain that they represent reality.

    Let’s first place these two new domains on Rumsfeld’s diagram, then look at a real-life example.

    So, where would the “certain uncertainties” and “uncertain uncertainties” fit?

    I would place them somewhere around the middle, as in the second diagram.

    Let’s look at a real-life example

    A Telco wants to sell socio-economic information about its customers, for direct-marketing purposes. The problem is that it knows close-to-nothing about its pre-paid customers: they buy a SIM without giving any personal information.

    Can the Telco find out any socio-economic parameters about this population?

    The only data we have is usage data: we know the location and duration of calls; we know the location and web-address of web-surfing activities.

    Using Teradata Aster solution, we try the following:

    • Identify the gender by analysing web activities. Using known subscribers, we identify the top gender-specific web sites (men use more gambling, sport, etc; women use more dating, picture-sharing, online clothes-shopping etc’. Hey, don’t shoot the messengerJ). We then use this on a test-set and achieve 75% success in ‘guessing’ the gender. Now we can be certain of our uncertainty when applying this to an unqualified data set.
    • Identify higher-income customers by locating frequent domestic flyers. We identify subscribers who made a call from the vicinity of a domestic airport and another call from the vicinity of another airport with a time-gap shorter than the possible driving time between them. Once again, trying this on a known data set results in 80% confidence in this approach. Another certain uncertainty.
    • Find where people live, then use this to identify their income level. The team does this by assuming that calls made before 7am and after 10pm are made from home. It identifies calls made at these times from the same location on different dates and takes that as their home location. It then uses publicly-available socio-economic data about neighbourhoods to assign an income-band to each subscriber. This technique achieves 42% match (compared with known data) and is thus discarded. This is an uncertain uncertainty. Therefore the risk of using it is too high.

    To summarise: we start with completely unknown data and explore several avenues. We use known data to estimate our confidence (our level of uncertainty). Some avenues lead to successful and repeatable results; some are a dead-end (which is a very certain uncertainty). We have identified our uncertain uncertainties and converted our certain uncertainties into known-knowns.

     Finally, what about the philosophy angle?

    Socrates said (and Plato wrote down) “…only these two things, true belief and knowledge, guide cor­rectly, and that if a man possesses these he gives correct guidance.” (Socrates, in Plato’s Meno Dialogue, 99A).

    In other words, you need to know your uncertainties to have true knowledge. Otherwise, it’s only a guess.

    Ben Bor is a Senior Solutions Architect at Teradata ANZ, specialist in maximising the value of enterprise data. He gained international experience on projects in Europe, America, Asia and Australia. Ben has over 30 years’ experience in the IT industry. Prior to joining Teradata, Ben worked for international consultancies for about 15 years and for international banks before that. Connect with Ben Bor via Linkedin.

    The post Certain Uncertainties or Philosophy of Big Data appeared first on International Blog.

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