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  • admin 9:57 am on July 30, 2017 Permalink
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    Your Data Needs You – Why driving change is the key to successful analytics 

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  • admin 9:54 am on March 4, 2016 Permalink
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    Monsanto Capitalizing on Analytics to Advance and Meet Customer Needs 

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  • admin 9:53 am on January 29, 2016 Permalink
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    Timing Is Everything: Why The Connected Car Needs Smarter Analytics 

    While other industries are just now coming to grips with sensor data and other forms of big data, the automotive industry can smugly say that they are veterans in this area. Since the late nineties, car manufacturers have been using data from the Engine Control Unit (ECU), Controller Area Network (CAN) and telematics to improve and enhance their vehicles.

    Fast forward to today, and while car manufacturers are comfortable with big data, there’s a new challenge looming – lots of data. The connected car has been called a “gigantic data-collection engine” for good reason.

    Just how much data do the experts think car manufacturers are going to have to be prepared for? Let me give you an illustration. Today, car makers might be downloading 100 – 200 kilobytes of data from a car, once a year, during its annual service. With the connected car, kilobytes of data can be downloaded every day. In addition, connected cars will have remote diagnostics capability to record data on-demand as needed, so engineers can study anomalies in detail.

    The scale of the data deluge becomes clear when we take into account that analysts, Gartner, predict that there will be 250 million connected cars on the road by 2020.

    Just what sort of data would a car manufacturer be able to collect from a connected car? Here are just a few examples – Vehicle Generated Data, User Generated Data, Network Generated Data, Vehicle Configuration, Geolocation, Vehicle Owner, and Diagnostic Trouble Code – and that’s just the tip of the iceberg.

    It’s clear that car manufacturers will need to decide whether it makes business sense to collect all the data available, considering the cost of transferring and managing the data.

    Don’t Drive Round in Circles

    And it’s not just about deciding what data to collect. Car manufacturers also need that data to parlay into value for the company.

    With industry trends indicating that the nature of car ownership itself is changing, the data from the connected car can play an integral role in helping car makers position themselves to offer alternatives such as pay-as-you-drive insurance, car leasing or shared-usage businesses.

    For car manufacturers, their data journey should not only include data from the connected car, it should also take into account other sources of data held by the company. It is when these are analysed with the right technology, that car manufacturers can get real business insights.

    Timing is Everything

    Car manufacturers often get themselves in trouble when thinking that all this data needs to be analysed in real-time. But not only would that be prohibitively expensive, it would also be a drain on valuable resources. Instead, car manufacturers need to think about how the data can be used and to what benefit. Often analyzing the data minutes, hours, and days after the data is collected still yields actionable insights.

    Here are some examples of the value that data analytics performed at the right time rather than in real-time can provide:

    Sub-Seconds –Combining the data fed from forward facing radars, with the connection of the vehicle to infrastructure, the ability to see around corners and other cars, crashes can be prevented with seconds to spare.

    Seconds to Minutes -Traction control systems sensing slippage on a wheel sends data to other cars approaching that location, warning them of the hazardous conditions.

    -Transmitting alerts to owners via anti-theft devices if a vehicle is suspected to have been stolen, based on entry mode or location.

    Hours-Detecting quality issues of cars in the field or targeting offers and services to connected owners as the car passes a certain position.

    –By analyzing the usage patterns and behaviors of customers, car companies can propose deals for pay-as-you-drive insurance or information on a car-sharing program.

    Months -Feeding usage information back to design teams, so that changes can be implemented, for instance, if sensor read-outs suggest that back doors of certain models are not often opened and closed, design teams can make a decision to only manufacture a 3-door version of that model instead of the 5-door version.

    This approach of performing analytics on the data at the right time, rather than in real time, means that companies can put the ability to query the data in the hands of frontline staff, not just strategic or middle-management levels.

    Call centre operatives, showroom sales staff and service centre repair engineers can see all the other touch points and conversations that a particular consumer has had with the company. This means that they can respond intelligently to the customer, which, in the long-term, means satisfied and loyal customers, better efficiency and profitability.

    If you’re interested in this topic, you will find in-depth analysis and innovative examples of how connected car data is being used in Winning the Connected Car Data Wars.

    This post first appeared on Forbes TeradataVoice on 29/10/2015.

    The post Timing Is Everything: Why The Connected Car Needs Smarter Analytics appeared first on International Blog.

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  • admin 9:53 am on January 23, 2016 Permalink
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    Premier Platform Handles Wide Range of Intelligence Needs 

    by Brett Martin

    The newest data warehouse solution within the Teradata® Unified Data Architecture™, the Teradata Active Enterprise Data Warehouse (EDW) 6800 platform, enables higher performance and more efficiency with better storage and compute power density. The platform is available with hard disk drives (HDDs) only or with hybrid storage that adds solid-state drives (SSDs). Andy Hawkins, Teradata Hardware Platforms product manager, explains how the new offering supports increasing tactical and analytical workloads while meeting a wide range of operational and strategic intelligence needs.

    What are the most exciting enhancements for this new release in the 6000 series?

    Hawkins: This release uses the latest Intel® Xeon® dual 14-core Haswell processor and faster DDR4 memory. The SSD size has quadrupled from 400GB to 1.6TB. Plus, there are multiple configurations available to best match the customer’s TPerf [the Teradata measure of EDW performance] requirements. As a result, businesses should experience faster performance and higher query throughput from the more powerful CPU, improved memory and increased read speeds of the new SSDs.

    How do these improvements affect in-memory processing and query throughput?

    Hawkins: This platform has been optimized for in-memory processing. The new Haswell processor includes updated vector instructions that, when combined with Teradata Database 15.10, facilitate more efficient processing of queries. Hyper-threading support is also provided to enable the best use of the CPU resources.

    Turbo boost technology allows the processor to speed up its clock for a brief time when heavily loaded to provide an extra “kick” of performance. The clock speed can jump from 2.6GHz to 3.5GHz with the turbo boost. The addition of two more cores in the processor increases the platform’s computational horsepower by up to 25 percent—that means more queries can run simultaneously. In addition, I/O resources are fully optimized to use all of this new processing power for queries. Organizations will also benefit from the new DDR4 memory, which enables up to 15 percent faster access to data stored in memory.

    Where does Teradata Database 15.10 come into play?

    Hawkins: A smarter use of data temperature for Teradata Intelligent Memory, process pipelining and new vector processing in the database deliver the highest performance and system efficiency. Teradata Database 15.10 has been specifically engineered to take advantage of new CPU instructions to process groups of values together rather than individually. This so-called vector processing performs many operations at one time rather than working separately on each value. This allows the database to operate more efficiently and takes advantage of the data in memory more efficiently for higher query performance.

    You mentioned the new SSDs. What benefits do they offer?

    Hawkins: Not only are the new SSDs four times larger, they read twice as fast as the SSDs used in the previous generation of the Active EDW. And since customer workloads are typically about 80 percent read and 20 percent write, the improved read speed should drastically improve performance.

    Organizations like the working data set to fit within the SSDs for the best performance, and the larger size will enable a much bigger hot data set that can be accessed more quickly than the warm or cold data stored on the HDDs. Larger SSDs also allow more total data capacity in each cabinet—almost 50 percent more in one design center configuration. The Teradata storage subsystem, powered by NetApp, is a vital component to the platform’s blazingly fast performance and reliability.

    Can you explain the major differences between the Teradata Active EDW 6800 and the Teradata Data Warehouse Appliance 2800?

    Hawkins: Both are powerful integrated data warehouses [IDWs] within the Unified Data Architecture. However, each one is designed for a targeted segment of data warehousing workloads. The Active EDW 6800 is the premier platform designed for active data warehousing, very advanced workloads, and nearly unlimited applications and concurrent users. Companies demand tight management across many mixed workloads, active real-time loads and mission-critical availability, which are enabled by the Active EDW 6800’s hot standby nodes.

    The Active EDW 6800 utilizes SSDs for the strictest performance requirements, and Teradata Active System Management is available for the most granular workload control. The Active EDW 6800 also has flexible configuration options to help customers best meet their performance and capacity needs. The solution offers configurations with 8, 10 and 14 cores, along with Workload Management Capacity on Demand to enable a TPerf range of approximately 115 to 300.

    The Teradata Data Warehouse Appliance 2800 is for organizations that want an enterprise-class IDW, but don’t require unlimited user concurrency, applications or workload management features. The appliance can also be used for special-purpose applications, analytical “sandboxes,” ETL offload and disaster recovery.

    Is there anything else new in the Teradata Platform Family?

    Hawkins: Teradata has just launched the next generation of its 1000 series, the Teradata Integrated Big Data Platform 1800. This model has the latest Haswell CPU, DDR4 memory and 4TB drives. Each appliance cabinet can hold up to 333TB of uncompressed user data, making it a perfect complement to the IDW for cost-effectively expanding storage and analytical capacity.

    Moreover, with Teradata QueryGrid™, users can easily join older data stored on the Integrated Big Data Platform 1800 with current data in the IDW. And since the Integrated Big Data Platform 1800 runs the Teradata Database, users benefit from all of the Teradata analytic capabilities at the lowest cost per terabyte in the Teradata Platform Family.

    Brett Martin is the editor-in-chief of Teradata Magazine.

    This article originally appeared in the Q4 2015 issue of Teradata Magazine. To learn more about the Teradata Active Enterprise Data Warehouse (EDW) 6800 platform, visit TeradataMagazine.com.


    The post Premier Platform Handles Wide Range of Intelligence Needs appeared first on Magazine Blog.

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  • admin 9:48 am on January 16, 2016 Permalink
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    Marketer on the Street Identifying Individual Customer Needs with Teradata 

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  • admin 9:50 am on December 23, 2015 Permalink
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    Teradata Chosen to Assist bonprix to Respond to Customer Needs 

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  • admin 9:46 am on October 29, 2015 Permalink
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    Chief data officers 'need to play an offensive role' and industry needs more of them, says Teradata governance chief 

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  • admin 9:52 am on September 24, 2015 Permalink
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    Lights On, Profits Out? Why Energy Innovation Needs Enterprise Analytics 

    Cast your mind back to the early 2000s when the chatter amongst those in the energy industry most likely focused on ‘keeping the lights on’ in the face of a growing demand for power versus available generation. There would also have been some talk of saving the planet by lowering carbon emissions.

    And while these remain key objectives for energy providers in many countries, it has become clear that the industry needs something more.

    Why? Because those energy firms that have made the most progress, they have destroyed their own businesses in the process. Ironic, isn’t it? The fact is, legacy generation revenues are not what they used to be and they’re no longer exclusive to the ‘old-school’ utilities either. Revenues now also flow to prosumers, energy co-operatives, and smaller renewable generation firms. This double whammy of lower prices and lower revenues is evident when you take a look at the annual reports of many large integrated European utilities in particular.

    The net result? Well, to put it bluntly, utilities need to innovate and transform, or die. The core markets for energy are evolving – so what can companies do to avoid a bleak future? Or to put it another way – how can archaic energy giants compete with the new, nimble, market entrants who are focused on specific value services at a lower cost point?

    It is not all doom and gloom. Large traditional utilities have some aces up their sleeve – sector experience, deep knowledge and historical insight, in the form of rich, untapped data sets held throughout these organisations. And when we combine this data with ‘new’ external data, at an enterprise level, that’s when the magic happens.

    These are just a few areas where data can power innovation:

    • Making smarter choices for the Smart Grid. Balancing the demands of the prosumer with fluctuating capacity from renewable energy sources to more effectively operate, maintain and build smart networks.
    • Securing financial stability in the new capacity generation market. Running the most financially preferential generation mix at any time, based on timely or accurate asset and market data.
    • Turning risk into profit in commodity trading. Leveraging inputs from customers, assets and other business units across the value chain in a dynamic trading environment.
    • Offering customers the right products and services, at the right time. Matching customer demand with the right product and service mix in order to build loyalty and drive business in new value orientated utilities retail markets.
    • Focusing on renewable investment with the best ROI. Evaluating the best investment opportunities whilst accounting for the impact of government policy and other market factors for energy production and storage by wind, solar, biomass, marine, or tide.

    These areas of innovation cover all aspects of the energy value chain. But excelling in one area and not in another is not enough. Having all these elements merge together within a more holistic energy system is. Again, enterprise analytics is an essential enabler for this.

    And this has never been more important than it is today, with the imminent blurring of traditional industry boundaries by the Internet of Things driving smart city and smart mobility opportunities for players outside the energy industry to enter and dominate the utilities’ ecosystem.

    To compete, utilities do not just need to leverage data to innovate and transform their businesses. They need to integrate and mine as much data as they have from right across the utilities value chain, internally and externally.

    And, I would argue (regulation permitting of course), all parts of the utility need access to a single view of this goldmine of data in order to flourish in their particular value chain area. Enterprise analytics becomes the central engine driving energy innovation. Innovators need access to an enterprise scale analytics ecosystem that will give them the ability to make accurate decisions, innovate with data, and discover new insights that drive transformation and prevent extinction.

    This post first appeared on Forbes TeradataVoice on 18/09/2015.

    The post Lights On, Profits Out? Why Energy Innovation Needs Enterprise Analytics appeared first on International Blog.

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  • admin 9:51 am on March 8, 2015 Permalink
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    Who Needs a Big Data Strategy, Anyway? 

    by David Kelly

    Whether you fully believe the hype or not, big data will continue to have a fundamental impact on your business over the next few years.

    Yet jumping on the big data bandwagon can backfire in a very expensive way without a concise blueprint for leveraging, acting on and benefitting from the information. As former U.S. Secretary of State Henry Kissinger once said, “If you don’t know where you are going, every road will get you nowhere.” That adage is applicable to where many organizations are today with their big data. They want to use it, and maybe are using it, but to what end?

    To exploit big data, better predict outcomes and improve every aspect of the business, organizations need to first recognize that data is an asset that’s as valuable and essential as any other capital asset in the business. And they must have a big data strategy—one that melds into the overall corporate plan.

    “A big data strategy is mandatory in my opinion,” points out Claudia Imhoff, president of Intelligent Solutions, Inc., and founder of the Boulder Business Intelligence Brain Trust. “Otherwise, you’re investing in a solution looking for a problem.”

    The strategy itself needs to start with an end goal in mind. It must document how opportunities afforded by data will align with the strategic priorities of the business, enabling the company to reach its objectives. Of course, the strategy should access all available data and prioritize its usage to allow the organization to become more agile while operationalizing big data to inform business decisions in real time.

    Know Where You’re Going

    When creating a strategic plan, it’s critically important to know where you’re going—or where you want to go—even if you don’t know the best path for getting there. This entails identifying the opportunities and potential challenges to leveraging the data.

    Taking advantage of all available data requires substantial changes for most companies in how they collect, process and act on information. “When it comes to big data, the first challenge for many organizations is to determine how the business is going to handle the huge volumes that can be associated with big data,” says Imhoff. “The second challenge is to figure out where the value is in all that data.”

    Once the value is identified, the prospects flowing from an intelligent strategy to capitalize on that value can be significant and wide-ranging. “There can be a tremendous opportunity to drive waste out by using big data,” says Dan Vesset, vice president, Business Analytics and Big Data Program for IDC.

    Big data can help organizations see new and unique opportunities to optimize discounting, identify more relevant product or service recommendations and increase customer retention. Simply having access to nontraditional information such as social networking or call center voice analysis can drive bottom-line benefits.

    Make Your Case

    The first step in the strategy is to identify problems and the business case. From there, the organization needs to figure out whether it can address the identified issues with the technology and skill sets it already has or if new solutions or personnel are needed.

    Most importantly, a big data strategy needs to circle back to what can be done with the results of data analysis and how those insights can be embedded in organizational processes and decisions. “Otherwise, deploying a big data solution is an interesting technology exercise,” says Imhoff. “But one that will have little actual impact on the business.”

    While there may be some small element of “build it and they will come” to these types of solutions, a business case needs to be developed for capturing an optimal return on the investment. Specific business cases will vary by company and by market. For instance, electricity companies might use the data from smart meters to change their business model by providing data services and intelligent applications to help customers consume energy more cost-effectively. Or a fire department might use the data and advanced analytics to optimize operational readiness by predicting where and when fires have the highest likelihood of occurring.

    Investing time and effort upfront prepares organizations to choose a technology solution that truly supports the current and future needs of the business. Or if one is already in place, leverage it to maximum value.

     Create A Business-Centric Plan

    Strategies will undoubtedly involve a range of technologies to help integrate, store, analyze and manage an increasing variety and number of data sources. Still, it’s important for organizations to concentrate on the business goals, not the technology tools.

    “A big data strategy needs to be more than a technology discussion. It also needs to focus on defining a business goal, identifying who you are going to work with to achieve it, and how you’re going to find or train the necessary talent,” says Vince Dell’Anno, managing director, Information Management – Data Supply Chain, Accenture Analytics. “We’ve seen a real shift over the past three years from organizations having an awareness of big data and its potential, toward organizations pursuing pilots and proof-of-concepts and experiencing the value from big data.”

    In addition, organizations should foster an operational mindset for practicing data-based decision making. They need a company-wide culture that encourages and rewards data usage, positive outcomes and value delivered to the business. In addition, they should document how opportunities afforded by the data will complement other data initiatives to drive innovation.

    “Organizations need to have a focus on innovation,” says Dell’Anno. “Beyond technology innovation, they should explore how to most rapidly gain insight from their data to initiate new innovative ideas for the business and its consumers. Increasing your ability to analyze data from across the enterprise at an accelerated pace can help make you more competitive.”

    A Foundation For The Future

    “Big data is rapidly changing our economic environment,” says Arent van ’t Spijker, author and senior consultant at BlinkLane Consulting in Amsterdam. “The question isn’t whether or not organizations should leverage big data opportunities—they have to. The market is changing because of the availability of so many diverse forms and sources of data that organizations need to proactively take advantage of big data opportunities to gain competitive advantage.”

    He points out that businesses must evolve to keep up with the data, market and economic changes. “If companies don’t have a strategy for using big data and simply stick to their old processes, they’ll end up at a competitive disadvantage,” van ’t Spijker explains. “Organizations simply can’t stick with their existing business model. They need to change to adapt to their changing environment.”

    Even if you’re not yet ready to jump feet first into big data, now is the right time to start working on your big data strategy so you’ll be ready to answer when opportunity—or disruption—comes knocking at your door.

    Read more of the special section on big data in the Q1 2015 issue of Teradata Magazine.

    David A. Kelly is a Boston-based freelance writer who specializes in business, technology and travel writing. 

    The post Who Needs a Big Data Strategy, Anyway? appeared first on Magazine Blog.

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  • admin 9:55 am on February 20, 2015 Permalink
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    Why Digital Government Needs a Focus on Data & Analytics 

    There is definitely a move in Australia to a focus on Digital Government at both the State and Federal levels. At the Federal level the government has recently announced formation of the Digital Transformation Office (DTO).

    In the announcement, the Prime Minister and Minster for Communications stated, “The DTO will comprise of a small team of developers, designers, researchers and content specialists working across government to develop and coordinate the delivery of digital services. The DTO will operate more like a start-up than a traditional government agency, focussing on end-user needs in developing digital services.” The announcement went on to state an overarching goal for the DTO as “The DTO will use technology to make services simpler, clearer and faster for Australian families and businesses.”

    This is all good, however without including a focus on data and analytics the transformation will not reach the full potential of reducing the need for people to “come into a shop front or make a phone call” as stated in the announcement.

    From the start, the digital experience should be built in such a way that interactions can be captured and then subsequently analysed to improve functionality, inform future designs, prioritise applications or even suggest new applications.

    Government can and should learn from the experience of both non-profit and commercial organisations that have been successful in utilising a data driven approach to transforming the customer experience.   The American Red Cross is Transforming into One Red Cross with Data Driven Insights. A key to the success will be to “design the ARC customer experience and craft it around data.”

    World class digital insurer, Aviva is Driving Forward with Data to be a World Class Digital Insurer. Aviva employs data driven marketing to develop a relationship with customers on all channels. Unifying customer data into a single repository, appropriately named Cyclops, the global insurance company is able to proactively interact with customers on multiple channels, increasing campaign ROI as well as meeting regulatory compliance quickly.

    Retail companies are using path analysis in combination with visualisation (illustrated in the use case below) to improve the online experience. When the data includes data from both the online (website or application) channel as well as other channels (such as call centres and shop fronts) the analysis can be expanded to include the path from digital to in-person channels.


    This type of analysis is beneficial to understanding how people are using (or not using) the digital assets. Results of analysis will lead to evidence-based recommendations for improving the digital experience, leading to more people choosing the digital channel.

    Establishing the Digital Transformation Office is a good start towards the vision of Digital Government. In order to ensure that people are able to effectively use the digital channel, data and analytics must be a core capability within Digital Government and not an afterthought.

    Monica Woolmer has over 25 years of IT experience and 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’s focus is assisting Public Sector clients across Australia and New Zealand. Connect with Monica via LinkedIn.

    The post Why Digital Government Needs a Focus on Data & Analytics appeared first on International Blog.

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