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  • admin 9:45 am on March 2, 2016 Permalink
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    Create a Data Driven Analytic Advantage with the Teradata Automotive Analytics Framework 

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  • admin 9:47 am on February 18, 2016 Permalink
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    CMO Council Webinar Advantage 2016 Big Opportunities to Exploit in New Year 

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  • admin 9:45 am on February 10, 2016 Permalink
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    Create a Data Driven Analytic Advantage with the Teradata Consumer Goods Analytics Framework 

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  • admin 9:52 am on May 7, 2015 Permalink
    Tags: Advantage, , industries, ResourceConstrained,   

    Taking Advantage of Big Data in Resource-Constrained Industries 

    For those of you reading my blog for this first time, welcome.  In my first blog, I introduced myself including the fact that during the course of my career I moved from the US to Australia. Well I have moved again, this time not to another country but  rather from Canberra to Melbourne. In doing so I have shifted the primary industry I support from Public Sector to Retail. I say primary because I will still be involved in the Public Sector and will also have the opportunity to support other industries as well.

    During my initial meeting with retail customers, a common question (concern) I have heard is given the tight resource constraints they face, how can they position themselves to take advantage of the new analytic capabilities being introduced in the era of big data? This concern is not uniquely a challenge of retail organisations as many other industries are resource-constrained and there has been significant coverage of the lack of big data skills available in general.

    At the same time of these discussions Bill Franks, Teradata’s Chief Analytics Officer, published an article on CMS Wire: The Key to Analytics for the Masses in which he advocates rather than educating and training a broader cross-section of employees to understand and make use of analytics in their day-to-day-jobs focus on changing their actions to take the results of analytics into account. Bill goes on to describe what analytics-driven behaviour looks like.

    How can an organisation get the results of big data analytics into the hands of decision makers?

    To me it requires enabling two distinct pathways. The first is to be able to ‘publish’ the results of analysis to processes and applications, for example Call Centre applications such as Next Best Offer, Cross-Sell/Up-Sell, etc.   The second pathway is to enable self-service big data discovery for true business users who might not be SQL-savvy let along big data savvy and the secret to this lies in having the few (data scientists / business analysts) publish big data applications for the many (business users).

    Data scientists and business analysts are uncovering incredible insights; they understand the business, analytics and the data; they understand how to interact with big data tools. Business users need access to big data insights in an easy to user interface with the ability to interact with the results through visualisations. They need access via a self-service so that they can get answers when the need it.

    To close the skill gap and significantly reduce the complexity of big data implementations, Teradata, has introduced a solution called Big Data Apps, which is powered by Teradata Aster AppCenter. Big data apps bridges the gap between data science and the business community by delivering easy to use, scalable and resuable apps that extend the reach of big data to the people who can maximise on the value.

    ‘Teradata has taking a significant step forward with this new solution by expanding its technology solution to include applications that make the analytics accessible and consumable for the typical business user.’
    – Dan Vesset, VP, Big Data Analytics Research, IDC

    These purpose-built apps are designed around answering a key business question, such as what is the sentiment of customers or what is the sequence of events that lead to churn. The program logic, analytic expertise, domain expertise and data integration and modeling are all wraped up into a big data app. The enterprise users can interface through a web-based portal to click to insights. Teradata has developed pre-built apps in the form of templates to accelerate time to value where the business KPIs, data integration and models to visualisations are captured and ready to rapidly implement. The following figure provides a Retail App example:

    The bottom line is even resource-constrained industries can and should leverage big data analytics to deliver outstanding customer experiences, improve customer acquisition and retention and create significant revenue opportunities. Don’t look to create data scientists or even analysts out of your business users, rather empower them with actionable insights, and enable them to make informed decisions.

    Unlock your data. Unlock the insights. Leverage the few. Empower the many.

    Monica Woolmer has over 25 years of IT experience who has been leading data management and data analysis implementations for over 15 years. As an Industry Consultant Monica’s role is to utilise her diverse experience across multiple industries to understand client’s business, articulate industry vision and trends and to identify opportunities to leverage analytics platforms to support, enable and facilitate the client’s strategic business improvement and change agendas. Monica has a cross-industry focus and is currently primarily assisting Retail and Public Sector clients across Australia and New Zealand. Connect with Monica via Linkedin.

    The post Taking Advantage of Big Data in Resource-Constrained Industries appeared first on International Blog.

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  • admin 9:45 am on April 25, 2015 Permalink
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    SAS In Database Decision Management for Teradata Advantage Program 

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  • admin 9:51 am on April 17, 2015 Permalink
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    Harness Cross-Functional Centrality of Data Analytics for Competitive Advantage 

    00-11-HC-QOC-BQ-DataLabPart 2 of Two

    Zoomed in Data Analytics Graph
    (Healthcare Example)

    <—- Click on image to view GRAPH ANIMATION

    In the first part of this two part blog series, I discussed the competitive importance of cross-functional analytics. I also proposed that by treating Data and Analytics as a network of interconnected nodes in Gephi (1), we can examine a statistical metric for analytics called Degree Centrality (2). In this second part of the series I will now examine parts of the sample industry graph animation in detail and draw some high level conclusions from the Degree Centrality measurement for analytics.

    The zoomed-in visualization starts with a single source system (green) with its data elements (cyan). Basic function specific analytics (red) can be performed with this Clinical source system data. Even advanced analytics (Text Analysis) can be applied to this single source of data to yield function specific insights.

    But data and business never exist separately in isolation. Soon, cross-functional analytics will emerge with users looking to gain additional value from combining data from various source systems. Notice how these new analytics are using data from source systems in multiple functional areas such as Claims and Membership.

    This data combination or integration can now be executed at various levels of sophistication. This data could potentially be loosely coupled for analysis through data virtualization or in an agile data lab environment. On the other hand, if the requirements so dictate, it could be tightly coupled within an Integrated Data Warehouse.

    Analytics using algorithms such as Time Series and Naïve Bayes tend to utilize data from multiple source systems. Having a discovery platform that can not only allow data scientists to easily combine analytics, but also easily combine data at the right level of integration for these cross-functional analytics — can be critical to efficiently harvesting insights from new sources of data. (Details here)

    The full animation starts to reveal that analytics, as they emerge, display higher cross-functional Degree Centrality. This calls for a data management ecosystem that can easily harvest this higher cross-functional Degree Centrality. Such a data management ecosystem would support varying degrees of data integration, varying types of analytics, and varying types of data access based on data users. (Learn more about Teradata’s Unified Data Architecture.)

    The animation also reveals a possible clustering of analytics and data nodes. Some of these data nodes could be good candidates for tighter coupling within an IDW or a Data Mart.

    The Analysis
    This specific industry example is illustrative and subject to the limitations of assumptions and quality of the sample data mappings used for this study. Link analysis was performed on a network of 3428 nodes and 8313 directed edges. Majority of the nodes represent either Analytics or Source Data Elements.

    Many Analytics in this study tend to require data from multiple source systems resulting in cross functional Degree Centrality (connectedness). Some of the Analytics in this study display more Degree Centrality than others. Many of the Analytics utilizing advanced algorithms also require data from multiple cross-functional source systems.

    Degree Ranking for sample Analytics from the Healthcare Industry Graph

    0 ojustwin blog 2 degree


    Analytics demonstrating varying degree of cross-functional Degree Centrality can likely be supported with varying level of data integration. This can range from non-coupled data to loosely coupled data to tightly coupled data. As the number of Analytics with cross-functional Degree Centrality cluster together it may indicate a need to employ tighter data coupling to drive consistency in the results being obtained. The clustering of Analytics may also be an indication of an emerging need for a data mart or extension of Integrated Data Warehouse that can be utilized by a broader audience.

    In-Degree Ranking for sample Data Elements from the Healthcare Industry Graph

    0 - ojustwin blog 2 of two


    Similarly if Data Elements start to show high Degree Centrality it may be an indication for re-assessing whether there is a need for tighter coupling to drive consistency and enable broader data reuse. When the In-Degree metric is applied, Data being used by more Analytics appears larger on the graph and is a likely candidate for tighter coupling. To support data design for tighter coupling from a cross functional and even a cross industry perspective Teradata offers reference data model blueprints by industry. (See http://www.teradata.com/roadmaps-and-models/)

    The analysis described above is exploratory and by no means a replacement for a thorough architectural assessment. Eventually the decision to employ the right degree of data coupling should rest on the full architecture requirements including but not limited to data integrity, security, or business value.

    In conclusion, what our experiences have taught us in the past still holds true for the future:
    • Data sources are exponentially more valuable when combined or integrated with other data sets
    • To maintain sustained competitive advantage business has to continue to search for insights building on the cross-functional centrality of data
    • Unified data management ecosystems can now harvest this cross-functional centrality of data at a lower cost with efficient support for varying levels of data integration, analytic types, and users

    Contact Teradata to learn more about how Teradata technology, architecture, and industry expertise can efficiently and effectively harvest this centrality of Data and Analytics.

    Ojustwin blog bio



    Ojustwin Naik (MBA, JD) is a Director with 15 years of experience in planning, development, and delivery of Analytics. He has experience across multiple industries and is passionate at nurturing a culture of innovation based on clarity, context, and collaboration.

    The post Harness Cross-Functional Centrality of Data Analytics for Competitive Advantage appeared first on Data Points.

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  • admin 9:52 am on April 12, 2015 Permalink
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    Harness Cross-Functional Centrality of Data Analytics for Competitive Advantage – Part 1 of 2 

    High Level Data Analytics Graph
    (Healthcare Example)

     <—- Click on image to view GRAPH ANIMATION

    Michael Porter, in an excellent article in the November 2014 issue of the Harvard Business Review[1], points out that smart connected products are broadening competitive boundaries to encompass related products that meet a broader underlying need. Porter elaborates that the boundary shift is not only from the functionality of discrete products to cross-functionality of product systems, but in many cases expanding to a system of systems such as a smart home or smart city.

    So what does all this mean from a data perspective? In that same article, Porter mentions that companies seeking leadership need to invest in capturing, coordinating, and analyzing more extensive data across multiple products and systems (including external information). The key take-away is that the movement of gaining competitive advantage by searching for cross-functional or cross-system insights from data is only going to accelerate and not slow down. Exploiting cross-functional or cross-system centrality of data better than anyone else will continue to remain critical to achieving a sustainable competitive advantage.

    Understandably, as technology changes, the mechanisms and architecture used to exploit this cross-system centrality of data will evolve. Current technology trends point to a need for a data & analytic-centric approach that leverages the right tool for the right job and orchestrates these technologies to mask complexity for the end users; while also managing complexity for IT in a hybrid environment. (See this article published in Teradata Magazine.)

    As businesses embrace the data & analytic-centric approach, the following types of questions will need to be addressed: How can business and IT decide on when to combine which data and to what degree? What should be the degree of data integration (tight, loose, non-coupled)? Where should the data reside and what is the best data modeling approach (full, partial, need based)? What type of analytics should be applied on what data?

    Of course, to properly address these questions, an architecture assessment is called for. But for the sake of going beyond the obvious, one exploratory data point in addressing such questions could be to measure and analyze the cross-functional/cross-system centrality of data.

    By treating data and analytics as a network of interconnected nodes in Gephi[2], the connectedness between data and analytics can be measured and visualized for such exploration. We can examine a statistical metric called Degree Centrality[3] which is calculated based on how well an analytic node is connected.

    The high level sample data analytics graph demonstrates the cross-functional Degree Centrality of analytics from an Industry specific perspective (Healthcare). It also amplifies, from an industry perspective, the need for organizations to build an analytical ecosystem that can easily harness this cross-functional Degree Centrality of data analytics. (Learn more about Teradata’s Unified Data Architecture.)

    In the upcoming second part of this blog post series we will walk through a zoomed-in view of the graph, analyze the Degree Centrality measurements for sample analytics, and draw some high-level data architecture implications.

    [1] https://hbr.org/2014/11/how-smart-connected-products-are-transforming-competition

    [2] Gephi is a tool to explore and understand graphs. It is a complementary tool to traditional statistics.

    [3] Degree centrality is defined as the number of links incident upon a node (i.e., the number of ties that a node has).

    Ojustwin blog bio

    Ojustwin Naik (MBA, JD) is a Director with 15 years of experience in planning, development, and delivery of Analytics. He has experience across multiple industries and is passionate at nurturing a culture of innovation based on clarity, context, and collaboration.

    The post Harness Cross-Functional Centrality of Data Analytics for Competitive Advantage – Part 1 of 2 appeared first on Data Points.

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  • admin 9:53 am on April 1, 2015 Permalink
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    Top 4 Big Data Applications for Best Value And Competitive Advantage 

    Top 4 Big Data Applications There has been much talk and glorification of big data and the revelations that this can bring a new day to competitive advantage, there hasn’t been as much talk about the specific issues and problems that organizations can now address with great strength and insight.

    The following discusses four of the most valuable big data applications or uses of big data to bring value to the enterprise and to give organizations advantages in their competitive marketplace.

    1. Big Data Applications and Enhanced Cyber Security

    Cyber security should be first on the to do list of all enterprise IT and IT cyber security practitioners. When you discuss big data and security, it’s about the ability to gather massive amounts of data in order to discover insights that predict and help prevent cyber attacks. The opportunity for incredible results was always there, but now there have been huge leaps forward in technology. There are now tools and techniques that enable enterprises to stay ahead of the perpetrators. A combination of big data analytics with specific security technologies that yields today’s strongest cyber defense posture.

    2. Get The Most Advantageous View of Your Customers

    Construct a fuller 360o view of your customers by adding more data sources – internal, external, proprietary, open source. Paint a fuller picture, allowing the organization to better understand customers and find advantageous means of communicating with them. Understand what when and why they buy, why they don’t or what they might buy next time.

    3. Improving The Data Warehouse To Improve Business Insights

    Use big data applications to improve decision making. Data stored in many different systems can be brought together for greater access and better decision making. Fold in big data and leverage advanced data warehouse capabilities to increase operational efficiency – and enable new forms of analysis. Use new technologies like big data specific platforms to create the opportunity for analysis of disparate data types. More data and broader data sources yield insights for stronger competitive advantages.

    4. Big Sensor Data And Big Advantages Using Big Data Applications

    Consider the opportunities analyzing things like machine and sensor or operational data can do for improving customer service and overall business results. The boom in and current pervasiveness of IT machine data, sensors, meters, GPS devices and myriad more requires analysis and combination with pertinent internal and external data sources. By employing not so complicated big data analytics, organizations can gain real-time visibility into operations, mechanical situations, customer experiences, transactions and behavior. NCR now receives telematics data from devices around the globe to determine the health of the equipment. The benefit? NCR sends digital repair instructions remotely or sends technicians with the correct equipment, to the right device, at the right time. Downtime can be planned or even prevented.

    The benefits of big data analytics and tailored big data applications are very real. These are just four of the top uses for the new wealth of data. Many organizations have found many advantages in their explorations with big data.

    Learn more about Big Data Analytics.

    The post Top 4 Big Data Applications for Best Value And Competitive Advantage appeared first on Data Points.

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  • admin 9:48 am on March 26, 2015 Permalink
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    Peer Advantage Presents Enterprise Holdings Best Practices Reporting and BI Solutions 

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  • admin 9:48 am on March 19, 2015 Permalink
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    Creating a Data Driven Advantage in Financial Services 

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