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  • admin 9:47 am on November 9, 2015 Permalink
    Tags: , Power, ,   

    Teradata releases the power of IoT data 

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  • admin 9:47 am on September 4, 2015 Permalink
    Tags: Electric, JapanGDF, Power, SUEZ   

    Japan-GDF Suez Gas and Electric Power 

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  • admin 9:46 am on July 16, 2015 Permalink
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    Teradata taps Cloudera to power next-gen Hadoop appliance 

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  • admin 9:52 am on April 6, 2015 Permalink
    Tags: , , , Funnel, , Playbook, Power   

    Funnel Analysis: an Approach from the Power Marketer Playbook 

    funnel imagePower marketers are always interested in the most effective ways to track, measure, and analyze customer experiences for more relevant engagement. I’d like to share an approach that is less known yet potentially quite powerful.

    Businesses across global markets are re-thinking data, analytics, platforms, and research methods to better understand their customers. Event analytics offers a new view of the customer, leveraging best technologies and diverse data sources, to obtain actionable insights in real time. Traditional methods help us understand consumers in terms of the following aspects: who, what, when, and where. Yet two of the most important questions for understanding consumers (“why” and “how”) are un-answered. The answers are key to obtaining business value because they can help us understand the why and how of consumers’ interactions with a company.

    Traditional approaches focus on how the customer looks to the business. For example, what do you buy? What segments are you in? When was your last visit? However, the more important question should be “how does the business look to the customer?” How do our customers experience our products and brands? How do customers feel at each touch point?

    One major advantage of event analytics over traditional methods is that it can improve our understanding of the customer’s view of the business. Traditional systems are not designed to solicit, extract and stitch together customer experience data well. Event analytics obtains information about the entire customer experience in detail, threading together many sources of information from different applications that combine to deliver the full view of customer experience.

    To conduct event analytics, businesses need to create a “customer experience universe” that stitches customers’ experiences together, allows for easy behavior pattern recognition and facilitates visualizations of customer behaviors. This universe includes social media, customer experience, marketing channels, mobile apps, and devices. Then, machine learning algorithms are used to run through all the data to identify patterns.

    Event analytics is an ecosystem that includes, for example, streaming ingestion of events, event repository, event metadata, guided user interface for business analysts and machine learning algorithms. One category of use cases is called funnel analytics which help us to understand customer behavioral patterns and what triggers their experiences.

    Funnel analysis provides visibility across a series of customer experience events that lead towards a defined goal, say, from user engagement in a mobile app to a sale in an eCommerce platform. Funnel analyses are an effective way to calculate conversion rates on specific user behaviors, yet funnel analytics can be complex due to the difficulty in source categorization, visitor identification, pathing, attribution and conversion.

    Funnels can be built using a single guided user interface without needing to write code or move data. As a result, event analytics can scale at the speed of business. It is a smart analytic approach because it helps create visibility to the path that users are most likely to follow to achieve their goals.

    The value of having this insight is of great significance since it gives marketers a deep, data-driven line of sight into the customer experience universe.

    James Semenak

    James Semenak

    James Semenak is a Principal Consultant with Teradata – known as an evangelist and architect for Event Analytics as well as Big Data Analytics and strategies.  James consults in all things related to data and analytics around the internet, and has worked with Shutterfly, Expedia, eBay Enterprise, Charles Schwab, Nokia, eBay, PayPal, Real Networks, Overstock.com, Electronic Arts, and Meredith Corp.



    The post Funnel Analysis: an Approach from the Power Marketer Playbook appeared first on Data Points.

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

    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 9:50 am on February 21, 2015 Permalink
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    The Power of Understanding the Customer Experience Journey in Telecommunications and Beyond 

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  • admin 9:46 am on February 20, 2015 Permalink
    Tags: , , , Energys, , , , Power, , ,   

    Using Analytics to Power Your People How Data Drives Xcel Energys Insights for HR and Workforce Management 

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  • admin 9:46 am on February 20, 2015 Permalink
    Tags: , Load, , Power, ,   

    Analytics for Load Management and Power Quality in Utilities 

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  • admin 9:51 am on November 7, 2014 Permalink
    Tags: , , , Engineers, , Power,   

    Why Can’t Power Engineers and Data Science Just Get Along? 

    Having spent much of the summer looking at the ‘fluffier’ customer side of Utilities, I have recently focused more on the ‘hard stuff’, in distribution and generation (although they too need, and indeed are starting to do ‘fluffy stuff’ with analytics… but that is a whole new blog).

    I understand these parts of a utility operate (and indeed are incentivised) differently to their retail cousins, but based on the increase in the data their assets and operations will produce going forward, they must need analytics as well right?

    My answer is clearly ‘yes’, otherwise I would not pose the question. However, in my role as a ‘fixer’ between the analytics community run by data scientists, and these Utilities sectors run by engineers – I am often more a marriage counsellor than a thought leader. So why is such an obvious fusion so hard to initiate and maintain?


    From an engineering perspective, electricity in particular is all about science. The science tells you how to build, how things work, and governs your world. You look at data day to day anyway to make things work, and to be blasé about it you see data scientists as pointed headed, sandal wielding lunatics that claim to understand your world in which you have worked for a long time (and often studied for even longer to get there) with some fancy software and a few algorithms.

    Conversely, data science find the engineering community hard work. A bunch of pointy headed, hard hat wielding lunatics who ignore the clear and obvious case ‘in the data’ to do things entirely differently. Why bash your head off a brick wall challenging the premise of science with engineers, when you can talk to retailers, and people in other industries that ‘get it’.

    There are of course exceptions on both sides! However, I guess this stand-off will not come as news to many. The reason for this blog is to give an alternative view on why we have got to this stage, and how to resolve it. Many people put the root cause of this disconnect down to a simple difference in approach, and categorise it as ‘too difficult to resolve’. This is both short sighted, and unhelpful.

    The key word here is ‘science’. Both parties believe in science, and mathematics. It is just that one party is more mechanically based, and the other more statistical. The fact science exists on both sides of this debate, and that the science is different causes this disconnect. However the science is exactly why both parties can, and should work together well. The key here is not talking about engineering and data science as ‘different’ – but instead for both parties to raise their eyes, and to accept the science the other brings as insight based on which they can constructively challenge each other. Engineers bring the science of the assets, and what is ‘known’ today.

    The data scientist should never challenge this viewpoint – but instead bring the science to enable engineers themselves to challenge what they know, by bringing out ‘unknowns’ in the data that allow them to do this. It is arrogant of the data science community to believe they can understand an industry better than those that built it – and they should remember this fact. They are enablers in this relationship. Likewise, engineers have to accept that the demands on their industry from regulators and customers dictate the use of data to understand, build and operate power generation and distribution differently. They need to ‘open up’ to accelerate change, and take guidance from other asset intensive industries where engineers work hand in glove with data science today.

    To complement and conclude, I would like to touch upon one related point where I do sympathise with engineers especially. There are too many people coming to them with big data, and analytics for their industry – much of which is paper based marketing pomp. I do not blame many engineers for being sceptical about what can be done in this area based on this. But do remember, not everyone bringing you this message is the same – some of us can make a difference with analytics. Our customers and industry analysts can testify to this. Come speak with them, and us.

    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.

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