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  • admin 9:54 am on August 25, 2016 Permalink
    Tags: , Fast, , , Punches, , SlowMoving, ,   

    Teradata Punches the Value Accelerator: Pushes Slow-Moving IoT Projects to the Fast Lane 

    Launches IoT analytics based on proven IP to transform sensor data streams into ROI streams
    Teradata United States

  • admin 9:51 am on September 4, 2015 Permalink
    Tags: , Fast, Quicker,   

    Fail Fast, Succeed Quicker 

    Q3-15_Feature_fail-fastby Chris Delker

    After years of pursuing their elusive dream of flight and suffering setback after setback, Wilbur and Orville Wright considered themselves failures. In a moment of despair, Wilbur wrote: “If man ever flies, it will not be in our lifetime.” But little more than a year later, Orville became the first person in history to achieve powered flight.

    What if they had given up? What if no one else had been seeking their vision of powered flight? Well, one thing is certain: Our world would look a lot different today. But they didn’t quit. The Wrights and other innovators have demonstrated that persistence and patience, and accepting that failure is part of life, often lead to success.

    Build Upon the Foundation

    The truth is that no matter the business or industry, failure is commonplace. For every flash-of-brilliance “Eureka!” moment, there are hundreds, if not thousands, of ideas that are conceived, attempted, tested and rejected. These are the stepping stones to achievements.

    While each failure is a component that, of itself, may seem insignificant, each one strengthens a foundation upon which a solid, strong new product, service or invention can be built.

    Venture capital firms and pharmaceutical companies are examples of organizations that produce more failures than triumphs. In fact, their business models are based on it. They know that each intermittent success can be lucrative enough to compensate for the cost of myriad ideas that didn’t work.

    Create the Right Environment

    Organizations that have a tolerance for experimenting benefit by being able to identify problems earlier in a project’s life. This provides a measure of stop-loss protection against massively expensive failures. That’s why forward-thinking companies believe that failing quickly—which leads to learning and future success—is what sparks innovation. High-tech firms even have a name for it: failing forward.

    “I generally recommend a sort of subliminal, ‘fail quickly’ mindset to my employees,” says Franz Amesberger, managing director of TCI Consulting. “I would distinguish between failures that happen practically only once, or only in special situations, and the systematical ones. If I detect such a systematical failure, I insist on making everybody understand why this can happen so regularly. We end up in a common search for possible solutions. This is clearly positive.”

    Certain personality types are more likely to thrive in this type of environment. Sri Raju, CEO of Smartbridge, explains that it’s important to objectively evaluate whether a team consists of the personality types that will productively adapt to a fail-fast culture. “You want people with very strong critical-thinking and problem-solving capabilities—people who can think well, communicate well, organize well and are team players,” he points out.

    Tap Big Data for Faster Results

    The age of big data has enabled a refinement of the fail-fast, fail-forward ideology by allowing information to be more fully harnessed in near-real time. In this environment, test scenarios can be cycled through faster, which exponentially improves the ability to harvest the knowledge and insights that are sown with the seeds of every failure.

    Despite the advantages of data-driven development, many companies are not leveraging their data assets. “Big data analytics is really still no more than a concept at the majority of companies,” Raju observes. “A lot of companies are crawling, and many are walking, but not many are running yet in tapping into the benefits of big data.”

    With today’s advanced analytics and the massive volumes of data available, businesses can now test, experiment and fail forward faster—and succeed more quickly—than ever. Big data and analytics allow for rapid trial-and-error experimentation that enables research and discovery-based business functions such as marketing and new product development that do not require “perfect” data for testing.

    The Road to Success

    No individual or organization can escape failure. It is an inherent, inescapable component of business. It’s also a truism that the road to success is littered with failures. These days, progressive organizations no longer consider failure a negative-value dead end. Instead, they expect to draw benefits from it.

    The very inevitability of not succeeding the first time proves the wisdom of the fail-forward, fail-fast approach. It’s what helped the Wright brothers ultimately grasp the prize of flight that eluded hundreds of their contemporaries. Sure, failure can be costly and discouraging. But when managed properly, it can pave the way to monumental success.

    Chris Delker is a B2B writer based in Texas. 

    Read this article and more on TeradataMagazine.com.


    The post Fail Fast, Succeed Quicker appeared first on Magazine Blog.

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  • admin 9:51 am on April 14, 2015 Permalink
    Tags: , , , Fast, , ,   

    Think Big Dashboard Engine Powers Fast Access to Hadoop 

    Dashboard Engine for Hadoop makes business intelligence reporting available for data lakes
    Teradata News Releases

  • admin 9:51 am on March 27, 2015 Permalink
    Tags: , Fast,   

    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 9:47 am on March 25, 2015 Permalink
    Tags: Fast, hits, , ,   

    SiriusXM Hits the Marketing Fast Lane 

    Teradata Web Casts

  • admin 9:48 am on March 5, 2015 Permalink
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    On the fast track towards advanced analytics and true customer insight with Teradata s Active Data Warehouse 

    Teradata Case Studies

  • admin 9:51 am on March 3, 2015 Permalink
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    Data-Driven Design: Smart Modeling in the Fast Lane 

    In this blog, I would like to discuss a different way of modeling data regardless of the method such as Third Normal Form or Dimensional or Analytical datasets. This new way of data modeling will cut down the development cycles by avoiding rework, be agile, and produce higher quality solutions. It’s a discipline that looks at requirements and data as input into the design.

    A lot of organizations have struggled getting the data model correct, especially for application, which has a big impact on different phases of the system development lifecycle. Generally, we elicit requirements first where the IT team and business users together create a business requirements document (BRD).

    Business users explain business rules and how source data should be transformed into something they can use and understand. We then create a data model using the BRD and produce a technical requirements documentation which is then used to develop the code. Sometimes it takes us over 9 months before we start looking at the source data. This delay in engaging data almost every time causes rework since the design was based only on requirements. The other extreme end of this is when a design is based only on data.

    We have always either based the design solely on requirements or data but hardly ever using both methods. We should give the business users what they want and yet be mindful of the realities of data.

    It has been almost impossible to employ both methods for different reasons such as traditional waterfall method where BDUF (Big Design Up Front) is introduced without ever looking at the data. Other reasons are we work with data but the data is either created for proof of concept or testing which is farther from the realities of production data. To do this correctly, we need JIT (Just in Time) or good enough requirements and then get into the data quickly and mold our design based on both the requirements and data.

    The idea is to get into the data quickly and validate the business rules and assumptions made by business users. Data-driven design is about engaging the data early. It is more than data profiling, as data-driven design inspects and adapts in context of the target design. As we model our design, we immediately begin loading data into it, often by day one or two of the sprint. That is the key.

    Early in the sprint, data-driven design marries the perspective of the source data to the perspective of the business requirements to identify gaps, transformation needs, quality issues, and opportunities to expand our design. End users generally know about the day to day business but are not aware of the data.

    The data-driven design concept can be used whether an organization is practicing waterfall or agile methodology. It obviously fits very nicely with the agile methodologies and Scrum principles such as inspect and adapt. We inspect the data and adapt the design accordingly. Using DDD we can test the coverage and fit of the target schema, from the analytical user perspective. By encouraging the design and testing of target schema using real data in quick, iterative cycles, the development team can ensure that target schema designed for implementation have been thoroughly reviewed, tested and approved by end-users before project build begins.

    Case Study: While working with a mega-retailer, in one of the projects I was decomposing business questions. We were working with promotions and discounts subject area and we had two metrics: Promotion Sales Amount and Commercial Sales Amount. Any item that was sold as part of a promotion is counted towards Promotion Sales and any item that is sold as regular is counted towards Commercial Sales. Please note that Discount Amount and Promotion Sales Amount are two very different metrics. While decomposing, the business user described that each line item within a transaction (header) would have the discount amount evenly proportioned.

    Data driven design graphicFor example – Let’s say there is a promotion where if you buy 3 bottles of wine then you get 2 bottles free. In this case, according to the business user, there would be discount amount evenly proportioned across the 5 line items – thus indicating that these 5 line items are on promotion and we can count the sales of these 5 line items toward Promotion Sales Amount.

    This wasn’t the case when the team validated this scenario against the data. We discovered that the discount amount was only present for the “get” items and not for the “buy” items. Using our example, discount amount was provided for the 2 free bottles (get) and not for 3 bottles (buy). This makes it hard to calculate Promotion Sales Amount for the 3 “buy” items since it wasn’t known if the customer just bought 3 items or 5 items unless we looked at all the records, which was in millions every day.

    What if the customer bought 6 bottles of wine so ideally 5 lines are on promotion and the 6th line (diagram above) is commercial sales or regular sales? Looking at the source data there was no way of knowing which transaction lines are part of promotion and which aren’t.

    After this discovery, we had to let the business users know about the inaccuracy for calculating Promotion Sales Amount. Proactively, we designed a new fact to accommodate for the reality of data. There were more complicated scenarios that the team discovered that the business user hadn’t thought of.

    In the example above, we had the same item for “buy” and “get” which was wine. We found a scenario, where a customer bought a 6 pack of beer then got a glass free. This further adds to the complexity. After validating the business rules against source data, we had to request additional data for “buy” and “get” list to properly calculate Promotion Sales Amount.

    Imagine finding out that you need additional source data to satisfy business requirements nine months into the project. Think about change request for data model, development, testing etc. With DDD, we found this out within days and adapted to the “data realities” within the same week. The team also discovered that the person at the POS system could either pick up a wine bottle and times it by 7 or he could “beep” each bottle one by one. This inconsistency makes a lot of difference such as one record versus 7 records in the source feed.

    There were other discoveries we made along the way as we got into the data and designed the target schema while keeping the reality of the data in mind. We were also able to ensure that the source system has the right available grain that the business users required.

    Grover Sachin bio pic blog small

    Sachin Grover leads the Teradata Agile group within Teradata. He has been with Teradata for 5 years and has worked on development of Solution Modeling Building Blocks and helped define best practices for semantic data models on Teradata. He has over 10 years of experience in IT industry as a BI / DW architect, modeler, designer, analyst, developer and tester.

    The post Data-Driven Design: Smart Modeling in the Fast Lane appeared first on Data Points.

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