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  • admin 9:58 am on September 15, 2016 Permalink
    Tags: Companies, , , , Unleashing   

    Teradata Unleashing the Potential of Great Companies 

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  • admin 9:53 am on April 19, 2016 Permalink
    Tags: , , Companies, Premises   

    Why Companies with On Premises Choose the Cloud 

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  • admin 9:56 am on April 17, 2016 Permalink
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    What Are Companies Doing with Teradata Cloud 

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  • admin 9:53 am on April 15, 2016 Permalink
    Tags: , Companies, , Else,   

    What Else Are Companies Doing with Teradata Cloud 

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  • admin 9:53 am on April 14, 2016 Permalink
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    Why Companies Are Doing Analytics in the Cloud 

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  • admin 9:51 am on November 15, 2015 Permalink
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    4 Reasons Oil & Gas Companies Are Going To Fail In A Big Data World 

    In Gartner’s latest Hype Cycle, you won’t find the term “big data” listed anymore – because it’s no longer considered hype. Big data has made it all the way from its emergence in West Coast dotcoms to East Coast financial institutions, Far East manufacturing companies, and many more diverse places and industries around the globe.

    Doing a quick Google search for ”big data” and Oil and Gas, you’d think that these worlds have merged now too. But no. Not only are Oil Companies not there yet, they are in danger of missing out on the whole opportunity.

    Here are four serious reasons why:

    1. Oil companies still manage their business data like librarians

    Or should I say, museum curators?

    To run the gamut from exploration to development, to production, there are many different formats of business data to be managed. Some are documents – engineering drawings from the development phase – and are managed as such. Some are physical things – rocks, fluid samples – that need to be catalogued and archived as physical things.

    But a lot of it is digital data, and oil companies are not even successfully taking advantage of this data that is already available in digital format. Instead of loading digital data in an easily accessible format, oil companies store the original measurement (and any contextual data) for posterity, as a single unit.

    Like a book in a library. Or a rock in a core store.

    But if you don’t make the data readily available for analytics, how can you make data-driven decisions?

    2. Oil companies just want to buy applications

    Oil and Gas retains a strong preference of choosing to buy end-to-end data management solutions off the shelf, especially in subsurface.

    Commonly we hear: “IT and data management infrastructure are not core business for us – we will not develop any custom solution”. But if you look at the industries and organisations who are benefiting the most from big data analytics and data-driven businesses –the absolute opposite is true; if what differentiates your company from your competition is how well you can turn your available data into insights, then this is core business.

    It gets worse when we consider workflows that regularly need to take in data from outside the thick walls of the subsurface domain – how can you perform repetitive, integrated studies across reservoir and production data without a data management framework that spans all of Exploration and Production (E&P)?

    3. Oil companies have lost their (geo)technical capability

    The inventors of the Raspberry Pi were concerned our children’s understanding of computing would be how to use an iPhone or a word processor rather than how to write programmes themselves.

    Tools like Petrel are replacing the holistic approach and even deterring people from testing science-driven hypotheses.

    Cast your mind back to the days before the integrated workstation interpretation suites, when it was important to understand first principles. But we are losing these capabilities every day – the long-threatened “Big Crew Change” is now visible daily as oil companies contract under low oil prices.

    The result is a lack of candidates to become the upstream data scientists that can discover new insights in the available data. If nobody in the Oil Company can apply the science, then analytical discovery just can’t happen.

    4. Oil companies implement IT in geological time

    The oil business is a strange one to outsiders. The financial numbers – both revenues and costs – are astronomical, the uncertainty is extremely high and the time to profit on a new project is long. Decisions made today may not take effect for a decade.

    In the North Sea, for example, if you discover a new oil field today, you are unlikely to see first oil from it for 8 years. What will the oil price be then? The world demand? And will the technology chosen in today’s Front End Engineering Design (FEED) study still be a good choice when the field enters its second decade of production? Who knows.

    In complete contrast, over in Dotcom land everything is now. Companies like eBay are constantly carrying out A-B tests on their website, constantly tweaking and changing their offering – continuous incremental improvement is the norm.

    The big data technology landscape is evolving fast, and this is not the time to pick a technology and version and standardise for the future. Especially if your data formats and analytical techniques are different from the ones prioritised by the Dotcoms.

    The only sure-fire way to ensure you get a big data strategy that works for you is to join in –build some systems, load some data, join the open source communities, test out new strategies, push the limits, and commit back. It certainly wouldn’t hurt your career prospects!

    If – as I suspect – oil companies are not willing to show up and take part, there is a strong chance that the big data technologies that emerge the winners will not meet their needs. And that will be a huge opportunity lost.

    The post 4 Reasons Oil & Gas Companies Are Going To Fail In A Big Data World appeared first on International Blog.

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  • admin 9:52 am on July 24, 2015 Permalink
    Tags: Companies, , , ,   


    by Brian JoreQ2-15_industry-eye

    Utility companies face an increasingly complex matrix of challenges in their quest to generate and deliver power efficiently. Traditional concerns such as reducing operating costs, ensuring reliability and meeting regulatory requirements are layered with myriad new demands in an ever-changing landscape.

    While automation has vastly streamlined data collection processes, utility operations and financial management teams are under relentless pressure to enhance and protect revenues, lower maintenance costs and reduce unnecessary debt write-offs. Utilities collect massive amounts of data, yet despite investments in applications and analytics tools, many struggle to transform their information into actionable insights that generate real value across the business spectrum.

    Invigorate Operations and Energy Delivery

    Analytic solutions, along with an industry-specific understanding of how to adapt the solutions to the needs of an individual utility, support tightly integrated initiatives that can produce results. Those results are prominent in four key areas:

    • Operations and financial management
    • Energy efficiency and demand response
    • Load management and power quality
    • Regulatory and rate design

    By uncovering gaps in operations and understanding their financial impact, sophisticated data integration and advanced analytics enable utility companies to sift through meter, billing and collections information. This allows them to quickly identify potential problems and gain a better understanding of which alarms and service orders demand priority, and which can wait. As a result, utilities can operate more efficiently and reduce costs.

    Similarly, energy efficiency and demand response programs have become strategic concerns for utility companies. Most are keenly aware of load reduction targets set by regulatory bodies, and utilities would rather manage their current resources effectively than build budget-busting new power plants.

    Integrated data and analytics allow better customer segmentation, enabling utilities to foster increased customer participation in efficiency and demand response programs. Organizations can also better measure their programs’ performance and metrics. Utilities benefit from stronger revenues, enhanced insights for regulatory bodies, and even improved load management and power quality.

    Shed Light On Forecasting and Costs

    Organizations are faced with the urgent need to improve grid efficiency while ensuring customers and regulators of the reliability and availability of energy. Data and analytics solutions help with that challenge. They provide the insights needed to improve load forecasting and capacity planning, which reduces power generation costs.

    Another opportunity is the correlation of “meter events,” such as power outages, for improved power quality and delivery. To better manage outages, a utility can leverage data from smart meters and network management systems for outage verification or to incorporate fleet management and geographic information systems to restore power.

    By realizing and optimizing the benefits of integrated data, utilities also gain more control over load management and power quality to contain costs and enhance brand reputation. In addition, reliable, current data improves load profiling, rate case performance and rate design. Moreover, the information allows a better understanding of revenue variances while shedding light on opportunities for new rates and improved margins.

    Utilities can also gain insights into pricing. This information can support numerous adjustments that help organizations apply price variables to meter or customer groups to understand how margins are impacted by customer segments, such as low income households or small businesses.

    No More Blind Spots

    Combining data with the power of analytics provides business units with an end-to-end view of the utility that eliminates blind spots. This view—which delivers increased transparency and shared insights—enables departments within any utility to pursue key initiatives both individually and jointly.

    Utilities can tailor approaches that source and integrate data according to industry best practices. The companies can then implement solutions that foster stability and growth.

    Brian Jore is a director of Business Consulting for Teradata. He works with organizations to increase business insights through analytics and BI. 

    Read the this article and more in the Q2 2015 issue of Teradat
    a Magazine

    The post UTILITY COMPANIES GET ENERGIZED WITH INTEGRATED DATA appeared first on Magazine Blog.

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  • admin 9:52 am on July 18, 2015 Permalink
    Tags: , Companies, , , Stronger,   

    How Telecom Companies can Build Stronger Customer Relationships 

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  • admin 9:51 am on March 6, 2015 Permalink
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    5 Ways Big Data Marketing Helps Companies Be Competitive: Part 2 

    (Part 2 of a post illustrating how marketers are creatively leveraging big data to secure competitive advantages.)

    Big data leveraged into insights have a strong likelihood to distinguish organizations from their competitors. Because of the infancy of this movement, few big data insights to date have been turned into marketing advantages – so early entrants into big data marketing have a distinct advantage. Consider the following big data marketing examples for a view into how other early adopter enterprises have sought advantage from big data:

    1. Next Generation Customer Retargeting

    As big data analytics become more sophisticated, marketers will find better ways to retarget customers. Imagine, for example, retargeting based on items that are viewed online but not clicked on. This and other tactics will provide more customizable methods than the retargeting currently being used.

    2. Use Heat Map Technology to Track In-Store Customer Preferences

    Use on premise camera systems with a heat map technology to view in-store customer traffic – just as websites use technology to register online activity. This offline traffic information can be contrasted with online data to tell retailers how products perform online versus offline in order to adjust marketing programs.

    3. Leverage Geospatial Data to Communicate with Customers

    Use geospatial data to prepare targeted offers AND drive online customers to store locations. Wireless carriers have increased revenue per user with targeted marketing campaigns and combined offline and online marketing efforts.

    4. Analyze Social Media to Increase Revenue

    Use social network analysis to identify and impact influential customers. Wireless carriers have found that by implementing social analysis they can increase the revenue that their top 10 percent of influential customers impact – from 35 percent to an impressive 80 percent.

    5. Focus On Conversions

    Marketers should talk in the language of conversions and place their focus there. “What is the source of leads that has the highest conversion?” “What type of content inspires the strongest brand advocates?” “Which channels host the highest rate of conversions.” Use big data to inform and drive all aspects of conversions.

    Look at 6 Ways Big Data Marketing Helps Companies Be Competitive: Part 1 for more examples of leveraging big data marketing.


    The post 5 Ways Big Data Marketing Helps Companies Be Competitive: Part 2 appeared first on Data Points.

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  • admin 9:52 am on February 26, 2015 Permalink
    Tags: Companies, , , , , ,   

    6 Ways Big Data Marketing Helps Companies Be Competitive: Part 1 

    Big data – business changing data – is giving marketers new ways to be innovative and step ahead of competitors. A creative strategy or advertising campaign is only scratching the surface of mechanisms available today to drive revenue. Effective CMOs must appreciate the power of new and diverse data sources and demand marketing directors interpret and use statistical business and customer insights to create smart strategies and quality predictive analysis.

    Understanding some of the more clever big data marketing examples helps to illustrate how marketers should be thinking analytically and creatively with non-traditional data. Consider the following big data marketing examples:

    1. Measure Social Media Impact

    Companies can measure the impact of social media with custom analytics solutions or social network analysis.

    2. Identify Your Brand Evangelists

    Identify alpha influencers and use these individuals in active marketing campaigns. Find alpha influencers not just through traditional transactions (recent purchases, customer service calls) but also through social media.

    3. Translate Big Data Insights into Actionable Marketing Tactics

    Translate big data insights into actionable marketing tactics with teams of different disciplines. The most successful are teams that work fast and are highly iterative – business, IT, and analytics specialists rapidly review real-world findings, recalibrate analyses, adjust assumptions, and then test outcomes.

    4. Create Customer Buying Projections

    Use historic behavioral data for a defined target as an indicator for behavior against a different category of product offering. For example, test payment history or upgrade likelihood for a utility service as indicators of behavior for an entertainment offering or emerging credit offering. Test into success.

    5. Understand True Value of Different Marketing Channel

    Combine sales data from traditional media and social-media sites to create a model that highlights the impact of traditional media versus activity reflected on social media (like call center interactions). Bad customer experiences are more powerful sales drivers than traditional media activity. Spending behind improving customer service can be more effective than funding advertising – to drive revenue.

    6. Pinpoint Sales Opportunities by Zip Code

    Rather than overloading sales reps with reams of data and complex models to interpret, create powerful sales tools with simple, visual interfaces that pinpoint new-customer potential by zip code. It’s a proven tactic for increased sales.

    Look for Part 2 for more examples of clever examples of big data marketing. In the meantime, see other big data examples.


    The post 6 Ways Big Data Marketing Helps Companies Be Competitive: Part 1 appeared first on Data Points.

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