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  • admin 9:51 am on July 22, 2017 Permalink
    Tags: , company, Curiosity, , , , saves, ,   

    How Curiosity Saves Your Company … And Turns Your People Into Citizen Data Scientists 

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  • admin 9:52 am on April 5, 2017 Permalink
    Tags: biggest, Claim, , company, , , Deterrent, , , , , , Storing   

    Company Executives Claim Security is the Biggest Deterrent to Storing Data in the Cloud, but Mass Cloud Migration Continues Globally 

    Company Executives Claim Security is the Biggest Deterrent to Storing Data in the Cloud, but Mass Cloud Migration Continues Globally
    Teradata study predicts widespread cloud data storage adoption by 2019, by which time over half of enterprise IT, customer, and financial data will be stored in the cloud

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  • admin 10:04 am on July 28, 2016 Permalink
    Tags: , Assurance, Columbus, company, Family, , , , ,   

    American Family Life Assurance Company of Columbus Selects Teradata as Technology Partner 

    American Family Life Assurance Company of Columbus Selects Teradata as Technology Partner. Will update enterprise systems to adapt to big data requirements and opportunities, provide richer data insights.
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  • admin 9:51 am on April 21, 2016 Permalink
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    Saudi Telecom Company Selects Teradata Aster to Drive Customer Satisfaction 

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  • admin 9:53 am on April 15, 2016 Permalink
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    Think Big a Teradata Company Expands Capabilities for Building Data Lakes with Apache Spark 

    Spark deployment challenges prompt rising demand for Teradata’s big data services across the world
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  • admin 9:51 am on November 26, 2015 Permalink
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    Is Your Company Product or Service Centric? Do Your Customers Agree? 

    Mine their comments to find out.

    In an ideal world, all companies would have a portfolio of excellent products delivered with remarkable service. Competition in the marketplace however dictates that company resources have to be allocated where they can be most effective. Understanding whether customers see your company as product-centric or service-centric does therefore matter.

    Some companies have it easy: Apple for instance is (mostly) a product company. Airbnb is primarily a service company. But what about the retail side of Amazon (1)? What about banks, insurance companies, universities, or airlines? The line here is murkier, and all customers may not agree about the category. Moreover, ill-defined priorities increase the risk of a disassociation between the company internal view and its customers.

    For instance, a company can internally think of itself as product-driven (and communicate in that manner), while the majority of its customers are focused on its service side. The divergence of these views can generate disaffection among customers who don’t feel heard or taken care of by the company.

    Fortunately, there are steps one can take to understand what customers care about and align the strategic direction to the customers’ expectations. To do so requires stepping out of the ivory tower (academia is not alone in living in one) and listening to the voice of the customers.

    Directly asking people what they would like can be inaccurate because of the stated vs. revealed preference dichotomy often observed in consumer studies [1]. In addition, customers also view the world through their own prism, leading the famous Henry Ford quote: “If I had asked people what they wanted, they would have said faster horses” (Henry Ford probably never said these words, but they became memorable nevertheless).

    Reliable feedback can however be obtained from satisfaction studies, for instance when querying net promoter score (NPS). In that instance, customers are asked to give a numerical rating to a question such as “how likely are you to recommend company X”, and then are asked to justify/explain their rating. The collection of verbatim comments can then be analysed to provide valuable insight into perception customers have of the company.

    A great way to obtain actionable insights is to perform automatic topic discovery on the verbatim comments. Among the best performing methods to do so is Latent Dirichelet Allocation (LDA). LDA is an unsupervised machine learning model that automatically parses every comment and group similar ones together into a pre-defined number of clusters.


    Figure1: LDA example

    In the case of satisfaction studies, numerical ratings can be used as a pre-filter to understand topics customers are satisfied/dissatisfiedwith. For instance, with NPS studies, promoters and detractors can be separated prior to performing LDA (effectively we run 2 LDAs, one on promoter comments and one on detractor comments) to illustrate what customers like and dislike.

    As an example, we recently undertook the analysis of verbatim comments from NPS studies of a major client. We first separated the comments according to their promoter/detractor status (NPS score > 8 = promoter, NPS score < 7 = detractor) and performed LDA using Teradata Aster. The comments were clustered into 5 categories for both promoters and detractors. The key topics for each category is illustrated below:

    Promoter Detractor
    Personal Service All corporations are the same
    Never had a problem I don’t know enough, I am new
    Convenient, Easy Bad customer service
    Customer service polite and helpful Difficulties with products
    Good product experience Products too expensive


    One immediately sees that the quality of customer service and amount of customer effort (2) dominate the conversations, indicating that satisfaction (high or low) is primarily linked to service rather than products. As a result, service improvements will have a greater impact than changes in the product portfolio. There is also an opportunity for the considered institution to change its perception by showing to its customers it is not the same as its competitors.

    Verbatim comments offer a unique insight into users and customers due to being their own words. Carefully mined, these comments help define (or redefine) strategic priorities. Because focusing on products when customers care about service (or vice versa) will not make you successful in the long run.


    1 personally I view it mostly as service-centric

    2 In fact, customer effort score is probably a more predictive measure of loyalty and satisfaction than promoter score


    [1] “A comparison of revealed preference and stated preference models of travel behaviour”, M. Wardman, Journal of Transport Economics and Policy, January 1988

    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 Is Your Company Product or Service Centric? Do Your Customers Agree? appeared first on International Blog.

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  • admin 9:55 am on April 2, 2015 Permalink
    Tags: , , , company, , , ,   

    Boeing: Data-Driven Innovation to Power the Best Integrated Aerospace Company 

    It doesn’t get much cooler than Boeing, right? Ranked 30th on Fortune 500, manufacturer of commercial and military aircraft AND the prime contractor for the International Space Station – add in that they’re almost a $ 100B company and the incredible history of innovation and well… you have Boeing. Innovation doesn’t stop at the cool stuff Boeing pioneers, it is pervasive throughout the century old, global company.   With the vision of “What Others Dream.  We do.” Boeing is an elite member of the innovation club.

    Boeing needed to enable the organization to transcend the production of financial information and spend more time helping the business use facts to take actions that would achieve the strategic vision.  Boeing knew finance was uniquely positioned to help the company operate and transform into a fact-based, data-driven culture.

    “Overall Boeing was looking for one source of the truth to provide an ecosystem that allowed people to get better insights from the information, that will allow them to utilize a self-service model, and provide better answers at a higher quality with less effort.” – Howard Alexander, Director of Business and Supply Chain Systems

    Howard Alexander Boeing

    Howard Alexander

    The goal: give self-service BI to 20,000 HR, Finance and Supply Chain users through the integrated data warehouse.  Boeing I-T & Business had to find common definitions across business units and transform its systems infrastructure, which included consolidating hundreds  of data marts. Two key strategies ensured success. First, partnering with the business and second creating a foundation for analytics.

    “One focus area has been, from the standpoint of value of integrated data, in the finance area.  The key there, is that the finance professional has a seat at the table, and is more than just reporting what has happened; actually involved in helping drive the right decisions, bringing to the table insights that can help management make decisions that are going to gain a better result. The analytics and the business intelligence has been built around keeping them away from having to do a lot of searching for data, massaging of data, more about spending more time understanding what the data is telling them and understanding what assumptions can be derived from what they’re looking at.  That’s a perfect example of where we have been able to provide a framework that has helped an organization move toward what they consider a business imperative.”  – Howard Alexander, Director of Business and Supply Chain Systems

    Data-Driven BusinessThat foundation allowed for what Howard Alexander calls the “community approach to analytics” within risk, cost accounting and labor management. Boeing users got increased self-service through user-friendly discovery and analysis tools, empowering them to solve their own problems and answer their own questions before data latency rendered the questions null. Now, Boeing has financial visibility throughout the month so the business can solve possible problems before they’re problems (month-end insight is too late!) Business users spend their time looking for better outcomes.  Teradata’s Unified Data Architecture™ means Boeing can leverage its technology infrastructure to get the most it can holistically.  Alexander says Teradata Unified Data Architecture™ allows them to meet objectives for cost performance and power the business intelligence analytic needs.

    “We’re definitely a deeply data-driven business, from the data that’s coming from our products, our airplanes, and other products, from the data that we use to manage our business, it is about the information, and it’s gonna be the key differentiator in the future, as we develop fairly hot, complex systems that produce a tremendous amount of data, that we can use to make the products better, and also serve the mission of our customers better.” – Howard Alexander, Director of Business and Supply Chain Systems

    Thank you to the team at Boeing for sharing this data-driven story of success!


    The post Boeing: Data-Driven Innovation to Power the Best Integrated Aerospace Company appeared first on Insights and Outcomes.

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  • admin 9:53 am on April 1, 2015 Permalink
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    Boeing Data driven Innovation to Power the Best Integrated Aerospace Company in the World 

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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 9:53 am on February 19, 2015 Permalink
    Tags: , , company, , , Strong,   

    Big Data Analytics: Will Better Company Culture Trump A Strong ROI? 

    (This post discusses the results1 of Forrester Consulting’s research examining the economic impact and ROI of an online retailer that implemented a Teradata analytics solution. The new integrated platform runs big data analytics and replaces the retailer’s third party analytics solution.)

    What is more beneficial? The quantifiable pay out of a big data solution…or the resulting improvements in corporate culture like encouraging innovation and increased productivity?

    In this case, the big data solution is a Teradata Aster Discovery Platform.  Previously, the retailer relied upon a third-party web solution for analytics – which was inefficient, difficult to manage and not at all scalable. And with its limited IT support staff and ever exploding business requirements, the online business needed an easy-to-manage big data analytics solution able to handle its compiled customer data.

    The platform has the ability to analyze and manage unstructured data plus has data visualization tools to help illuminate key business insights and improve marketing efficiency. And, it’s easy on labor costs. Because of the platform’s ready-to-use functionality, acquiring data, performing analysis and presenting output can be done by a wide variety of IT skills sets and resources. The organization does not need a full team of expensive data scientists and engineers to manipulate and use data.

    Does it pay out? Forrester confirmed the retailer’s increases in new customer conversions, overall sales, savings from IT and end user productivity…all resulting in a direct impact to net bottom line profits.

     “For us, it has been relatively easy to monetize and

    justify our investment in Aster Discovery Platform;

    the changes that have resulted from the product

    have offered us much increase in revenue.”

    ~Director of data engineering, online retailer

    The cultural intangibles?  The retailer estimates 20% of its total employees (both IT and business) have a direct benefit, a gradual increase in their productivity from Year 1 to Year 3 due to how quickly business insights can be generated and the business practices optimized.

    Performance throughout the organization improved dramatically. With the Aster Discovery Platform, the online retailer avoids multi-step, non-scalable procedures to run analytics and instead can just type a simple query. The organization’s planning process has become tighter. Better forecasts and predictions using predictive insights allows the organization more efficiency within the product life cycle delivering noticeable impact across a variety of measures like incremental sales, customer retention and customer satisfaction.

    “We have a lot of test cases and product changes that we

    have been able to make internally as a result of the analytics

    that is taking place on the platform.”

    ~Director of data engineering, online retailer

    The Teradata Aster Discovery Platform is the industry’s first next-generation, integrated discovery platform. Designed to provide high-impact insights, it makes performing powerful and complex analyses across big data sets easy and accessible.

    1The Total Economic Impact TM Of Teradata Aster Discovery Platform. Cost Savings And Business Benefits Enabled By Implementing Teradata’s Aster Discovery Platform.  October 2014. © 2014, Forrester Research, Inc.

    Learn more about Teradata’s big data analytics solutions.

    The post Big Data Analytics: Will Better Company Culture Trump A Strong ROI? appeared first on Data Points.

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