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  • admin 9:53 am on March 31, 2015 Permalink
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    6 Ways to Find out if there is any Science in your Analytics 

    “All Science Analyses Data but not all Data Analysis is Science”

    We are currently blessed with more data than ever before. Yet, most of the conversations are volume, speed, or technology oriented. Big data is an evolution of data, but there is no such thing as big science. Only Science.

    Does Science matter? Yes, absolutely, definitely. New technology allows more and more analysts to process ever larger volumes of data, faster, and without barriers. This is a good thing as more models can be tested, more questions answered, and more phenomena explained than ever before.

    It is also a dangerous thing for your business. All data processing lead to results, but only a scientific approach will reliably lead to accurate results. The difference can be great.

    1. “Science is a Method, not a collection of facts or technologies”

    According to the dictionary, Science is: the intellectual and practical activity encompassing the systematic study of the structure and behaviour of the physical and natural world through observation and experiment

    The definition above says nothing of specific technologies, higher education degrees, or mathematical constructs that one often ascribes to the practice of science and the description of a scientist. In fact, science is a method, a way of life. An artisan (e.g., baker, cabinet maker) can be as much a scientist as a pathologist or particle physicist.

    A Scientist is not defined by the technology she/he uses or by the amount of facts or mathematics she/he knows, but by the dedicated practice of the Scientific Method

    Practically unchanged and in use for over 2500 years, the Scientific Method encompasses the aspects of Observation (Formulating a question, background resaerch and hypothesis formulation), Experimentation (Test, Validation) in a Systematic manner (iterative processes) resulting in a Theory and Explanation of the observed phenomenon.

    2. “Data exploration feeds the input to the method, not its output”

    Data mining, or exploration, is a starting point; it helps discover questions worth answering and patterns worth testing. What it does not guarantee is a definitive, or even valid, answer. The pitfall of bypassing the scientific method is that while such an approach can often sound convincing and be well presented, quantity of data is no substitute for systematic experimentation. Time gains resulting from circumventing the Scientific Method will be negated by the costs of realising the output is wrong, (much) later down the line.

    3. “Domain Expertise helps frame the question, not the answer”

    Science is technology and domain agnostic. Business domain expertise is necessary to devise a business question worth answering, filter out red herrings, and provide knowledge regarding state-of-the-art. The core of the scientific method, on the other hand, does not preoccupy itself with domain expertise. Its conclusions may be impractical or too expensive to implement, but the only recourse is to reframe the hypothesis.

    Replacing the scientific method with current domain expertise ensures that outcomes are neither novel nor robust.

    4. “Science is predictive”

    The output of the scientific method is, and has to be, a prediction. The prediction is what is being tested, under the constraints set by the experiment and the hypothesis. Every time a correlation or an insight is presented, they explicitly or implicitly represent a prediction. Always.

    5. “All results, positive and negative need to be reviewed and explained”

    Failure is not a part of the scientific method. A rejected hypothesis is a perfectly valid outcome of the method, and the rejection of the hypothesis adds to the general body of knowledge that is being investigated. Consequently, all outcomes should be reported and explained. Non-reporting of rejected hypotheses can have dire effects, as is regularly observed, post hoc, in drug trials. Censorship (self or otherwise) of results leads to wrongful hypotheses being accepted and will force other people to retest disproved hypotheses again and again, wasting time and effort for all.

    6. “Scientific results are temporary”

    Circumstances change, behaviours change, technologies change. Why should insights be any different? The iterative nature of the scientific method illustrates a permanent consideration that hypotheses and theories are only valid up to the point when they are disproved.

    New data, new methods, and new technologies bring greater experimental precision or novel information that can disprove long-standing theories. This is not only unavoidable; it is desirable because it allows our understanding to grow more precise and accurate.

    Clément Fredembach is a data scientist with Teradata Australia and New Zealand Advance Analytics group. With a background in Colour Science, Computational Photography and Computer Vision, Clement has designed and built perceptual statistical experiments and models for the past 10 years.

    The post 6 Ways to Find out if there is any Science in your Analytics appeared first on International Blog.

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  • admin 9:55 am on March 30, 2015 Permalink
    Tags: , , Execution, ,   

    Best Practices for Big Data Strategy Execution 

    by David A. Kelly

    To identify the essential components for any big data strategy, Teradata Magazine spoke with three noted experts who have conducted substantial research and handled the physical implementations of big data initiatives. Each one addresses a best practice you can adopt to develop your successful strategy:

    > View Big Data as a Valued Corporate Asset

    Like existing corporate data sources, the information in the ever-expanding world of big data needs to be viewed as an important asset. Organizations have to dedicate resources and capital to manage and take advantage of the new opportunities that big data provides, including using it in conjunction with all of their other data—it’s the combination of data that makes it the most powerful. One of the main benefits of the data is the ability to accelerate innovation and create new business or service models.

    “Before the advent of big data, there was often a feeling of ownership when it came to data that was generated or used within organizations. Departments would define set data as ‘theirs.’ But now, with big data, it’s no longer about just how a single group should manage or use data, it’s about how that data can benefit the organization as a whole. It’s really a different way of looking at data and the value it can bring to an organization. With big data, your corporate view has to be more expansive.”

    —Vince Dell’Anno, managing director, Information Management – Data Supply Chain, Accenture Analytics

    > Foster a Culture of Embracing Data

    To obtain the full benefits of a big data strategy, cultural changes are often needed on both the business and IT side. Organizations need to foster attitudes that value creativity, experimentation and taking data-informed risks. Business and IT leaders need to be willing to challenge, adapt and refine both their strategy and execution plans based on data and practice fact-based decision making.

    “With big data, the business side of an organization needs to be open to having its assumptions second-guessed. There’s no point in exploring big data if the results will have no effect on how the business is run. It can be uncomfortable, but a business needs to be open to the impact that the analysis of big data can have. At the same time, IT organizations need to take a different view—they need to be more open and encouraging about the use of different types of data, and be more business and user-driven.”

    —David Stodder, director of business intelligence, TDWI Research

    Collect Diverse Data, Then Follow Up With Action

    Organizations need to integrate data as a foundation for cross-functional analysis while also developing ways to measure and track data that can govern the business. Big data allows organizations to empower both front and back office functions through better access to more information. Employees can take action at the point of insight, increasing responsiveness and agility.

    “By collecting a wide variety of customer interaction data, including social media interactions, organizations can leverage data to understand the customer and customer experience better to improve customer retention and customer experience.”

    —Dan Vesset, vice president, Business Analytics and Big Data Program for IDC

    Read more about big data strategy in the Q1 2015 issue of Teradata Magazine.

    David A. Kelly is a Boston-based freelance writer who specializes in business, technology and travel writing.

    The post Best Practices for Big Data Strategy Execution appeared first on Magazine Blog.

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  • admin 9:51 am on March 29, 2015 Permalink
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    What Will Digital Marketing Success Look Like? 

    marketing toolkitThat’s the question on many of our minds as we forge through the first quarter of a year that’s jam-packed with lofty marketing goals. Although it’s nothing new, digital marketing has become a major game changer for both brands and consumers. Now that we are completely accustomed to digital technologies that were once trendy (email, mobile, social, e-commerce, etc.), “digital” has become the norm for traditional marketing. And given the consumer’s adoption of all things digital, I must say, “traditional” has never looked so good.

    Digital marketing is a huge win for global brands. Campaigns based on digital technologies provide quantifiable results like none before, and the multi-channel world opens up endless possibilities for executing complex marketing strategies. But how can sophisticated marketers keep pace with the onslaught of digital marketing technology? Where will it take us next? And how can marketing teams use digital channels to edge out competitors?

    The pace of change in digital marketing can be quite overwhelming. To keep up, digital marketers need to look at their strategies and find ways to mimic this pace. In many cases, that means we need to adopt a more short-term perspective. Digital marketers have inherited planning methods from bygone days of planning for non-digital channels (perhaps direct mail). Plans were drafted a year in advance. Digital marketing success requires a different approach:  agility.

    Marketing agility focuses on adaptive plans and development, allows for quick changes or improvements, and encourages these quick changes and a snapshot understanding of results. This model allows marketers to adapt to unpredictable consumer behavior. If an agile strategy gives you the shivers, consider just a portion of your strategy. See if you can actually plan to be more adaptive as your target market needs change. Then, slowly show results that will encourage an agile planning strategy that can be rolled out across the marketing organization for all of your upcoming digital marketing initiatives.

    The question at hand remains: What will digital marketing success look like this year? And from this question, many more evolve, such as:

    • Where will the digitization of customer interactions take your brand?
    • How can you anticipate what the ideal customer relationship will look like?
    • How will digitization affect your internal marketing operations?
    • Can we even look a full year ahead, or should we settle on more short-term goals?

    In one of Teradata’s newest white papers, “Moving to the Forefront of Marketing: The Next Edge for Digital Marketers”, we address what digital marketing success should look like. Of course, there will be obstacles, so we also need to sustain an open, yearlong dialogue that focuses on improvements that will get your digital strategies up to speed. Talent, budgets, and creative best practices must be invigorated in order to achieve more meaningful customer engagements and ultimately, higher revenue for your company, and perhaps bragging rights for the marketing department, too. You can download the whitepaper from our new toolkit for digital marketers.

    The post What Will Digital Marketing Success Look Like? appeared first on Teradata Applications.

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  • admin 9:46 am on March 29, 2015 Permalink
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    Data Driven Marketing and the Need for an Individualized Customer Approach 


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  • admin 10:33 am on March 28, 2015 Permalink
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    The Great Data Lakes How to Approach a Big Data Implementation 

    Register for this episode of The Briefing Room to hear veteran Analyst Dr. Robin Bloor as he explains the challenges facing organizations who endeavor on Big Data projects. He’ll be briefed by Rick Stellwagen of Think Big, a Teradata Company, who will outline his company’s approach to handling Big Data implementations. Rick will discuss the role of the data lake, and how timely response of queries is critical for reporting and analysis.
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  • admin 9:51 am on March 28, 2015 Permalink
    Tags: , , , , Repeatable, Sharable   

    Aster AppCenter – Sharable, Repeatable Big Data Analytics 

    I recently spent a week at the headquarters of Teradata Aster in Silicon Valley working on the recently announced Aster AppCenter.

    The basic idea of the AppCenter is to democratise access to so called big data analyses. The AppCenter allows a user to pre-package SQL, SQL-Map Reduce and SQL-Graph code into a one-click App which can then be shared with business users. When Apps are run they produce a report which can comprise tables, visualizations or a combination of both. The most basic type of App has its data source and input columns hard-coded in and will produce the report off whatever data is in the source table. Portable Apps allow the user to specify table and input columns – thereby running the App on different data sources.

    AppCenterDashboard

    AppCenter Dashboard

    Any analysis which can be coded and run on Teradata Aster can be packed into an App.

    This means that previously labour intensive tasks such as importing CSV data, moving data from other databases or from Hadoop, parsing semi-structured data such as weblogs, XML or json, and executing path, text and graph analyses can all be pre-packaged into a user-friendly one click app.

    The way I see it, the AppCenter is part BI tool (think Tableau or Cognos), part code repository (think Git or Subversion), and part collaboration and sharing space (think Confluence) – but it is not going to replace any of these. It is a way of making analytics repeatable and sharable.

    AppCenterDashboard_01

    Source: D3 visualizations, sharable through AppCenter  https://github.com/mbostock/d3/wiki/Gallery

    The AppCenter caters for a range of users. Business users can point and click on a pre-built app, SQL folks can package and share their code using built in visualizations, and java programmers can build customised functions and customized visualizations. For example they can write java converters to make D3 visualizations available via the AppCenter.

    To top it all off, there is a RESTful APi to facilitate the integration of AppCenter output into web applications and make them available on mobile devices.

    The AppCenter is one example of a wider trend (e.g. BigML) towards making analytics accessible, repeatable and sharable. In a way, this is all to the good as it leaves more room for data scientists and analysts to pursue those aspects of analytics which still require human thought and ingenuity such as designing experiments, studying and improving algorithms, explaining insights and understanding business problems and matching them to appropriate analyses.

    Ross Farrelly is the Chief Data Scientist for Teradata ANZ, Ross is responsible for data mining, analytics and advanced modeling projects using the Teradata Aster platform. Previously Ross ran Datamilk, an independent bespoke data mining consultancy specialising in data mining and advanced predictive analytics. Ross is a six sigma black belt and has had many years of experience in a variety of statistical roles including Business Development Management at Minitab and as a SAS Analyst at New Frontier Publishing. Connect with Ross Farrelly on Linkedin.

    The post Aster AppCenter – Sharable, Repeatable Big Data Analytics appeared first on International Blog.

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  • admin 9:50 am on March 28, 2015 Permalink
    Tags: , , , Policies, Renewing, Selling,   

    Data Strategies for Selling and Renewing More Insurance Policies 


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  • admin 9:50 am on March 28, 2015 Permalink
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    How to Deliver Results by Leveraging Individualized Insights 


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  • admin 9:51 am on March 27, 2015 Permalink
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    So You Want to Be Disruptive? Not So Fast. 

    magnifying glassSometimes, it feels like businesses know us better than we know ourselves. We let Facebook pick our friends and Twitter filter our news. Google knows where you’ve been and Uber knows where you’re going. All of this customer knowledge is driven by data analytics tools, but even the best analytics can get off track if companies lose sight of what their customers really need from them.

    There are three kinds of analytics that fuel data-driven marketing today: prescriptive analytics (What do my customers want today based on past behavior?), predictive analytics (What will my customers want tomorrow?) and disruptive analytics (What do my customers not even know they want yet?). Although we tend to think of the last two types as the most innovative, the reality is that innovation isn’t always about guessing the next step, especially if that step takes you further away from your customers.

    You can innovate by being prescriptive. For example, a retail store might “video scan” customers (with their permission) as they enter the store and match that image with the customer’s profile to make product recommendations based on past purchases.

    You can innovate by being predictive. A financial services company, for example, might analyze social media in real-time to measure consumer sentiment as a predictor for stock market shifts.

    You can innovate by being disruptive. Uber is a good example of a business that artfully combined different strategies—crowdsourcing, mobility and data analytics—to disrupt what had become a staid industry (short-distance transportation services).

    Trying to be innovative at everything won’t work, and choosing to disrupt a market where you have long-term customer relationships may lead to swift and negative consequences. Businesses need to align their data analytics around their brand and their customers. The right strategy could be getting closer to your customers by using your intimate knowledge to serve them better, or it could be driving a wedge between dissatisfied customers and the companies that underserve them.

    One best practice we’ve seen from successful companies is the separation of different analytics initiatives. Prescriptive analytics are commonly aligned with a data warehouse since they draw primarily from historical and transactional data. Predictive analytics might focus on massive amounts of external or semi-structured data (e.g., social media posts, weather data), and thus be a better fit for a big data project. Disruptive analytics might be assigned to a team of data scientists using complex and proprietary algorithms on yet a third platform (e.g., a data mart).

    These islands of insight need to be brought together, and marketing applications are the ideal place for that convergence. When businesses can boast both a 360-degree view of customers and a 365-day view into their past and future, they’ll be in a much better position to enrich the customer journey.

    The post So You Want to Be Disruptive? Not So Fast. appeared first on Darryl McDonald: Vision 2.0.

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  • admin 10:34 am on March 26, 2015 Permalink
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    Teradata Universe – Europe 

    Welcome to the 20th annual Teradata Universe Conference in Europe, this year held in the cosmopolitan city – Amsterdam, The Netherlands. Discover how to turn your company into a data-driven business. Together with data analytics experts from around the world, this event is your opportunity to learn.
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