Updates from November, 2015 Toggle Comment Threads | Keyboard Shortcuts

  • admin 9:52 am on November 25, 2015 Permalink
    Tags: Appeal, , , , , , Shop   

    Do Your Marketing Campaigns Appeal To Grandmas Who Shop Like Millennials? 

    Mobile ApplikationenThe holiday season is upon us, and so – no surprise – I’ve been thinking about shopping. More specifically, I’ve been contemplating how consumer behavior has evolved and how millennials now shop differently than their grandparents.

    Take my mom, for instance. She’s a prolific shopper, and she’s developed a rather interesting habit: When she shops, she tends to use the preferred channel of the person she’s shopping for. That might sound strange to you, so let me explain…

    When my mom shops for herself, she follows the traditional route, visiting a brick and mortar store and even timing her trips to jive with the schedule of salespeople she’s come to know and trust with her business. But that’s not at all how she shops for her grandchildren. When my mom is shopping for her grandkids, she engages digitally – like they do. She’s joined loyalty programs for the retailers her grandkids like. She gets emails from these brands and will even respond to location-based triggers from them when she’s in the mall.

    It’s an intriguing disconnect. My mom is a grandparent who, at times, shops like a millennial. She’s not 100% brick and mortar. She’s not 100% digital. I like to classify her shopping behavior as “traditional, but also transitional.”

    And from what I’ve observed, this behavior is fairly typical among my mom’s friends and many others her age.

    As marketers, we need to be tuned in to these types of disconnects. Granted, your customers may all be on the same journey (to buy something), but each one of them is going to have a different experience. That’s why the path through that journey has to be individualized and the contextual messages you deliver have to be tailored. Your marketing campaigns need to be guided by data driven solutions, so that you can capture information from your customers wherever they are on the experience continuum and then use that information in a way that meets a need at the inflection point, when the customer is most ready to buy.

    Don’t capture data just for the sake of “having” it. You need to use that data for insights so you can extend the right offer at right time across right channel for each individual – whether that individual is a grandma, a millennial, or a grandma shopping like a millennial.

    For more of my thoughts about how you can adapt to changing consumer behaviors, check out the interview we recently streamed over Periscope.

    The post Do Your Marketing Campaigns Appeal To Grandmas Who Shop Like Millennials? appeared first on Teradata Applications.

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  • admin 9:47 am on November 25, 2015 Permalink
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    Teradata 2015 Partners 10 things to watch out for by industry 

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  • admin 10:33 am on November 24, 2015 Permalink
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    Teradata Universe 2016 

    Teradata Universe Business and Technical Conferences are the premier International events dedicated to data warehousing, analytic solutions and applications. Held yearly in Europe, Middle East and South Asia, these gatherings bring together the vanguard of high-level business and technical professionals who rely on Teradata for daily business decisions.
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  • admin 9:54 am on November 24, 2015 Permalink
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    A Race Towards Cloud: Commodisation of Systems and Analytics? 

    Having recently attended Teradata Partners 2015 user conference organised by and for clients of Teradata, the push towards Hadoop based solutions is relentless – the vision of benefits remain strong while the evidence of business value actually realised remains small. Perhaps it’s just early days and organisations are still learning. The big buzz is around cloud.

    Perhaps the interest in Hadoop is partly driven by the opportunity to get the IT services in to the cloud. This solves a number of complex IT management challenges. By outsourcing platform provisioning, data centre management, capacity planning, specialised skills development and retention, the cloud offering can potentially deliver on the analytical agility and flexibility required by the business. We now have organisations that are going through their application and analytical systems one by one and determining what can be outsourced to the cloud, particularly to Amazon.

    So we can have a future where any organisation just need to sign-up to a cloud provider who would have the environment (and perhaps applications) to get the business going. They can get core financial systems up and running, get access to product applications when they need it and even access operational and analytical CRM solutions this way.

    In its full maturity, this environment leads to IT systems and analytics as no longer a source of competitive differentiation. We have seen the bandwagon effect of IT staff outsourcing (everyone’s doing it now) and this is definitely following the same path. Although Tom Davenport’s recent article shows a reversing trend.

    This got me thinking about what then becomes the sustainable competitive advantage of companies. Strategy? (most organisation have similar approaches to the market): People (they have been outsourced and worse comes to worse, pirate key staff): Ability to Execute (alot of products are IT dependent and hopefully will no longer be a constraints – so is this mainly customer management?).

    What about analytics? Is it the ability to see things happen ahead of competition; formulate decision options backed by data and execute quickly; and be ready for the next challenge?

    I see a lot of initiatives starting to focus in to getting the IT services and platforms commoditised in to the cloud which is perhaps the early start of a major transformation in the industry. But here is the thing. . . have organisations focused enough in preparing and improving their analytics capability if it is to become a more prominent source of a company’s competitive advantage? Moreover, analytical agility is not solely a platform issue. Often it is the inflexible business and IT process governance that has not adapted to the new paradigms.

    Organisations can start the preparation process by doing a stock-take of their current capabilities and organisational environment beyond platforms and IT provisioning. Best to let people outside the organisation do an analytical maturity assessment to get a fresh and unencumbered view.

    Renato Manongdo is a Senior Financial Services Industry Consultant at Teradata ANZ and is also the practice lead for Business Value Assessment in Asia Pacific. Connect with Renato Manongdo on Linkedin.

    The post A Race Towards Cloud: Commodisation of Systems and Analytics? appeared first on International Blog.

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  • admin 9:47 am on November 24, 2015 Permalink
    Tags: , , , , , includes, MapReduce, , , , ,   

    The Teradata Aster Discovery Platform includes the Aster database Discovery Portfolio a graph processing engine MapReduce and a version of R 

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  • admin 9:51 am on November 23, 2015 Permalink
    Tags: , Bieber, Bill, , Gates, , Justin, , ,   

    OJ, Bill Gates And Justin Bieber Walk Into A Pub To Talk Big Data Analytics. What Are The Odds? 

    About the same as any of them winning the UK lottery jackpot, I reckon.

    Especially now that lottery operator, Camelot, is determined to make it even harder for a punter to hit the jackpot, by adding an extra 10 balls to the current 49.


    Which means odds will lengthen from an already breathtaking 14,000,000/1, to a flabbergasting 45,000,000/1. Putting that into perspective, you’re:

    • five times more likely to be stuck by lightning twice during your lifetime (9,000,000/1), or
    • 12 times more likely to be killed by a shark (3,700,000/1)

    than land the big one.

    Life, as they say, is a bit of a lottery.

    Analyzing the Analyzers

    I don’t play the Lottery in fact, but as a Financial Services Industry Consultant with little formal statistical training, I do have a vested interest in getting to grips with the associated mathematical concepts, and what makes data scientists who use them, tick.

    A couple of years ago, I came across ‘Analyzing the Analyzers’; an insightful book that runs the rule over 250 data scientists and their considerable skills.

    And what a disparate bunch they are. As you might expect, some excel in maths and statistics and some, programming. Others show more of an aptitude for business, and so on. According to the author, these differences throw up four distinct personae – Data Businessperson, Data Creative, Data Developer, and Data Researcher.

    All hands to the analytical pump

    Which I love, because the category ‘Data Businessperson’ makes me a data scientist, too (well, that’s my story and I’m sticking to it – got to raise the tone of my CV somehow).

    It also supports my belief that getting value from Big Data demands more than a PhD in Advanced Statistics or Machine Learning.

    That said, the truly-experienced, propeller-hat-wearing data scientist who can also demonstrate the required business knowledge, expertise, and experience, is a rare pearl. Not surprisingly, this makes them a very expensive resource. So, wouldn’t it be great if I, and my fellow Data Businesspeople, could help out a bit? You know, performing the kind of sophisticated analytics on structured and unstructured data, normally the exclusive province of the propeller-heads. Expanding (the availability) while reducing (the cost of) the resource, so to speak.

    A meeting of minds

    Happily, there are advanced analytic solutions available today that improve the odds of this happening. They wrap-up many of the latest and most powerful analytical tools and techniques – MapReduce, R, and Path & Graph analyses, for instance – in pre-packaged analytic functions, SQL-like syntax, and analytic apps. This is a whole lot easier than crafting hundreds or thousands of lines of Java.

    Of course, analytic capabilities have to be highly scalable – accessing and moving both structured data in various databases and unstructured data in Hadoop, as well as pushing-down analytic processing.

    But whatever the prerequisites, if organisations are going to get the most out of their existing data and Big Data, there needs to be more of a crossover between roles and skills like mine, and those of the traditional data scientist. More understanding; a meeting of minds. I need more analytical nous; they need more business awareness.

    Meanwhile, back in the pub…

    By now, The Juice is working the bar, telling anyone who will listen, that turning people like me into analysts would be like trying to get a lottery operator to discover insights and extract maximum business value from another organisation’s data. At the dartboard, Bill weighs-in with another point of view: “lottery odds are fixed, but you can shorten Big-Data-value odds by equipping Data Businesspeople with powerful analytical capabilities and easing the data science workload.”

    And in the skittle alley, Justin chews the fat over the merits of using advanced analytics on structured and unstructured data.

    Unlikely? Stranger things have happened. Like David and Kathleen winning more than £1 million in the Euro millions lottery, twice in two years.

    The odds? An astronomic 283,000,000,000/1.

    So, listen-up all you Industry Consultants! You’ve got more chance of winning the Data Businessperson Lottery than a philanthropist, an infamous ex-footballer, a teen-town heartthrob, and a middle-aged couple from Lincolnshire UK.

    Why shouldn’t it be you?

    This post first appeared on Forbes TeradataVoice on 18/09/2015.

    The post OJ, Bill Gates And Justin Bieber Walk Into A Pub To Talk Big Data Analytics. What Are The Odds? appeared first on International Blog.

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  • admin 9:46 am on November 23, 2015 Permalink
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    Teradata Simplifies Critical Data in IoT Environment 

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  • admin 9:51 am on November 22, 2015 Permalink
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    The Evolution Of Black Friday Reveals Improved Understanding Of Customers 

    RET-1008-LOver the past few years, Black Friday sales have been starting earlier and earlier – so much so that some stores have begun welcoming shoppers “pre”-Black Friday, even as early as Thanksgiving morning. This fall, however, there are signs that “Black Friday creep” is losing its appeal.

    At least 20 chain popular chain stores have announced that they are not opening on Thanksgiving Day. In addition, other retailers are being petitioned to reconsider their holiday schedule, and at least one – REI – will be closed not only on Thanksgiving, but on Black Friday, as well. Here’s how Jerry Stritzke, president and CEO of REI, explained the company’s decision:

    “Black Friday is the perfect time to remind ourselves of the essential truth that life is richer, more connected and complete when you choose to spend it outside. We’re closing our doors, paying our employees to get out there, and inviting America to OptOutside with us because we love great gear, but we are even more passionate about the experiences it unlocks.”

    At first, a strategy like this may seem misguided. Why on earth would a retailer want to close its doors on the busiest shopping day of the year? Who says “no” to Black Friday?

    But dig a little deeper and you’ll see what I do: a business move that’s fresh, bold and, for this particular company, quite savvy. By closing on Black Friday, REI is staying true to its brand message and proving it truly knows, and respects, its customers.

    In today’s always-on, always-distracted global marketplace, it’s becoming more and more difficult to stand out from the crowd. Essentially, you have two choices: Either, grow hoarse from trying to be heard above the noise (as you shout about a Black Friday sale that’s happening early… no, earlier… no, even earlier) or distinguish yourself by putting the focus precisely where it belongs – on your customers.

    To succeed, your marketing campaigns need to be customer-centric. You need to know your customers as people, and you need to understand what makes them prefer your brand above all others. As I’ve said before, the best way to maintain and evolve a healthy brand is to be intentional and consistent about establishing it – so much so that the “noise” can’t drown out the tune you want the world to hear.

    A wealth of marketing applications are available to help you determine how to proceed – because, keep in mind, what works for one company, might not be the right fit for yours. For REI, the answer clearly lies in connecting with customers through a shared appreciation of the outdoors. Here’s more from Stritzke:

    “As a member-owned co-op, our definition of success goes beyond money. We believe that a life lived outdoors is a life well lived and we aspire to be stewards of our great outdoors. We think that Black Friday has gotten out of hand and so we are choosing to invest in helping people get outside with loved ones this holiday season, over spending it in the aisles. Please join us and inspire us with your experiences. We hope to engage millions of Americans and galvanize the outdoor community to get outside.”

    Has your Black Friday strategy changed over the years? Does your strategy resonate with your customers?

    The post The Evolution Of Black Friday Reveals Improved Understanding Of Customers appeared first on Teradata Applications.

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  • admin 9:46 am on November 22, 2015 Permalink
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    How the C Suite Can Create a High Impact Big Data Culture 

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  • admin 9:51 am on November 21, 2015 Permalink
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    Will IoT & Analytics Really Make Full Automation Possible for Utility Power Networks? 

    There are now machines that have conquered the game of chess and starred on TV game shows. Soon we may even be able to chat and befriend them, a reality that perhaps isn’t too far off, when companies like Facebook are investing heavily in it to ensure secretive development on how its apps might be able to better help you find and communicate with your friends – automatically.

    What about in our Utility firms? Will we see machines roam more freely in the area of power network control? There is much debate around the level of automation that Internet of Things (IoT) based technologies, driven by analytics, will enable in network control, and over what timescales.

    But in reality, is full automation actually even possible?

    I’ve written in a previous blog post that today, analytics within the Utility networks business falls into three categories:

    • Distributed network solutions implemented on the network itself which have “productised” analytics at the heart of what they do;
    • Virtual control and monitoring solutions that continually run and assess the state of assets based on configurable analytics algorithms; and
    • Advanced analytics, and companies with “utilities big data” offers that integrate all data from across the Utility for analysis in conjunction with other relevant external data.

    Distributed network solutions” by their nature are automated, needing little human interaction. So this area is not contentious. But in “control and monitoring”, the idea of automation certainly is more contentious.

    So is full automation possible? In my opinion, “yes”, in theory at least. Although it may well be that automation is implemented very slowly given that installed physical infrastructure is a long way off managing and responding to the sophisticated control signals required, especially on low voltage networks.

    But we have to remember that culturally, this is a big deal. Network control is safety critical, and there will always be much that is unknown about how a power system might operate. full automation is never trusted enough to be implemented.

    Today virtual control and monitoring as described above is gaining traction, and helps engineers operate networks better. But this alone will not enable full automation. However, there is a trend emerging that challenges my own categorization of the use of analytics within the Utility networks business, which could lead us towards full automation. The three categories as I outline them above are merging.

    We are already seeing control and monitoring solutions emerging that can push analytics packets onto distributed assets real time, based on internal and external analytics triggers. Advanced analytics, and big data platforms are moving ever closer to real time, allowing more and more network data to be analysed in near real time to improve network operation, in combination with more parameters that a human could nominally apply manually.

    I believe that what we’re seeing is just the start. As the industry matures in its use of IoT, and analytics on the data from IoT, network analytics will only accelerate and become ever more intertwined. The way that analytics is performed today – separately – will become a thing of the past.

    Many in data and analytics talk about merging data from IT and OT systems for analytics purposes. Longer term, I see a single environment not only “doing analytics”, but gradually automating network operation, as well as the execution of many other businesses processes in the digital network business of the future. This is the latest potential of IoT for Utility power networks.

    Interested in discovering how IoT data can generate more value for your company when combined with business operations and human behavioural data? Read on.

    Iain Stewart is the principal utilities expert for Teradata in the EMEA region, with over 13 years of experience in utilities sector. Iain also has in depth experience of both smart metering and smart grids, including how these link to and support the wider sustainability agenda. Other areas of experience include renewable energy, and smarter cities. Connect with Iain Stewart on Linkedin.

    The post Will IoT & Analytics Really Make Full Automation Possible for Utility Power Networks? appeared first on International Blog.

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