Updates from January, 2015 Toggle Comment Threads | Keyboard Shortcuts

  • admin 9:54 am on January 28, 2015 Permalink
    Tags: ,   

    Millennials Roundup 


    Teradata Analyst Reports

     
  • admin 9:54 am on January 28, 2015 Permalink
    Tags: , , ,   

    Overcome Common Analytics Challenges 

    When businesses integrate all of their data for analysis, they gain greater insights that can lead to improved productivity, reduced costs, an enhanced customer experience, decreased churn and other benefits. Although big data analytics is quickly becoming a top priority for many organizations across all industries, there are still several common challenges:

    > Meeting expanded user needs

    > Growing volumes of data

    > Increasing demands on IT

    > Integrating technologies

    The Teradata® Unified Data Architecture™ can help any company swiftly clear these hurdles. The solution delivers an integrated data warehouse, data platform and discovery platform to capture insights from all types of data—both big and small. That actionable information can then support smarter decisions that improve and grow the business. 

    Carly Schramm
    Assistant Editor
    Teradata Magazine

     

    The post Overcome Common Analytics Challenges appeared first on Magazine Blog.

    Teradata Blogs Feed

     
  • admin 9:49 am on January 28, 2015 Permalink
    Tags: ,   

    Key Trends in eCommerce 


    Teradata Web Casts

     
  • admin 9:52 am on January 25, 2015 Permalink
    Tags: , , , , ,   

    NRF Show 2015: Look Where Data is Taking Retail! 

    The National Retail Federation’s Big Show, held annually in New York City, is THE show for everything that makes retail happen. Wanna see the latest in store design? You can see it at the Big Show. Who’s offering mobile payment? It’s at the Big Show. What’s next in HR for retailing? That’s there, too.

    The one common thread to it all? Data.

    Data is the backbone, the juice in retailing. And if the show is any indication, retailing continues to lead in the application of data of any kind.

    When we did our study of data usage for marketing applications in retailing and CPG firms last year (To access, please click here:  http://bit.ly/1Ji95K8 ) — we chose these retailing and CPG simply because we knew there would be wide variance in data usage. We expected to find some firms that were very mature and some that were not, and we weren’t disappointed. But retailing is where the cutting edge of data-driven decision making is in business.

    So where is data taking retail? Or rather, where will data take all of us?

    If NRF is the bellwether, then here’s a few places we should consider. The first is supply chain/demand chain linkage. I heard a lot of talk on the show floor around the idea of connecting data from the transactional side (who is buying what right now) to the supply side (getting product on the shelf). This connection has two huge implications.

    First, it’s really hard to keep the R in CRM. So hard, in fact, that few are really trying. Forget a relationship – what we’re trying to do is to manage this customer experience in such a way that we have permission for the next one. A stock-out may lead us to lose that next chance. Where a lot of pundits have gotten customer empowerment wrong is that they’ve focused on things like product reviews. ‘Be nice or get a bad review!’

    While those aren’t unimportant, the real power being wielded by today’s customer is the ability to shop around so efficiently. If you’re out-of-stock, you’re out of the next search. And out of stock doesn’t only mean you had it and now you don’t. It also means you never had it to begin with. That’s why the demand link is so critical.

    The second factor to this demand to supply link is margin. If you over-estimate demand and have too much stock, carrying costs are increased, mark-downs are more likely, and a host of other bad things. Better demand forecasting in real-time means better inventory management, avoiding stock-outs and overstocks while driving carrying costs out. We think this connection of supply chain and demand chain is so important, it is actually the subject of our next research project (if you’d like to participate or just chat about it, send me an email), assuming we can find enough good examples. My fear is that so many people are just starting to work on this, it may be a while before we know how this will play out.

    So one place we’re going is what I call vertical data integration – focusing on that supply and demand link.

    A similar theme of this year’s NRF Big Show is a collective “deep breath.” While PwC’s 18th annual survey of CEOs concludes that big data analytics are the most important technologies for investment this year, I really think that retailing is going to take a deep breath and get more strategic about managing what it has and how it is used. Rather than rush to acquire the latest technology, the questions now are going to be more about … “How do I connect the systems I already have and get the most from the data we’re collecting now?”

    Just as NRF was concluding, the holiday retail sales figures were released. If you just looked at Black Friday to Christmas, sales were disappointingly flat. But if you broadened your view, sales grew a healthy 4%. What we’ve observed is a fundamental shift in the way we shop for the holidays – with online sales happening sooner and the effects of gift-cards pushing sales in January. Holiday sales now begin at the beginning of October and are just concluding.

    This fundamental shift means that retailers don’t really know what’s going on – unless they are heavily engaged in analytics and Dynamic Customer Strategy, meaning they are testing and learning, not just mindlessly discounting. I think this period of learning about how consumers are really shopping is the one factor driving this pause.

    This collective deep breath is retailing’s version of two themes I’ve heard over the past six months at tech conferences: Innovate and integrate. If innovation is being driven by the business unit and integration by IT, then we have a problem, and Houston, we have a problem. That type of disconnect will lead to further technology bloat, or the adoption of over-lapping point solutions.

    Rather, I agree with Lisa Arthur, Teradata Applications CMO and author of Big Data Marketing (Wiley), who thinks that innovation will come through integration, and we both think retailing is where this will happen first. That’s not to say that we won’t see mobile payment systems being purchased, or that there won’t be additional investments in other customer-facing technologies, visual analytics, or other technologies. There will, but the organizations that are further along are focusing more of their efforts on making systems work together.

    Another theme I’ve heard at tech conferences is that IT needs to reclaim its seat at the strategic table. Nowhere is that more obvious than in retailing. The cloud and its subscription models enable marketing to buy technology and pay for it out of an operating budget. What I’ve been hearing from retailing CIOs is that this often gets done and IT gets brought in late in the process, if at all. As a consequence, not only is there technology bloat but resistance to integration becomes a problem. As I’ve heard over and over, business unit managers are quick to ask and expect data from other sources but loathe to provide data. And without someone who understands the big picture of data and IT sitting at the strategic table, it just doesn’t happen.

    But what is also different about retailing is that this is the first conference in at least a year where I spoke to an audience equally balanced between IT, data scientists, and marketing practitioners. That kind of participation (particularly by marketers) bodes well for bringing it all together.

    tanner1-1

    Dr. Jeff R. Tanner is Professor of Marketing and the Executive Director of Baylor University’s Innovative Business Collaboratory. He regularly speaks at conferences such as CRM Evolution, Teradata Partners, Retail Technology, INFORMS, and others. Author or co-author of 15 books, including his newest, Analytics and Dynamic Customer Strategy, he is an active consultant to organizations such as Lawrence Livermore National Laboratory, Pearson-Prentice Hall, and Cabela’s.

     

    The post NRF Show 2015: Look Where Data is Taking Retail! appeared first on Data Points.

    Teradata Blogs Feed

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

    Business Highlights in Big Data History 

    If you’re relatively new to Big Data, you might find this snapshot of the last 20 years of big data history helpful. Hopefully, you can build your understanding and figure out where you reside in the journey of Big Data development, adoption and optimization.

    chris twogood Gentlemen Start Your Spreadsheets (1995) The world wide web explodes and business intelligence data began piling up – in Excel documents.

    Data Storage and BI (1996) The influx of huge quantities of information brought about a new challenge. Digital storage quickly became more cost-effective for storing data than paper – and BI platforms began to emerge.

    Houston, We have A Problem (1997) The term Big Data was used for the first time when researchers at NASA wrote an article identifying that the rise of data was becoming an issue for current computer systems.

    Yes, Big Data was first considered a problem.

    Ask Nicely (1998) By the point that enough data was able to be stored, IT departments were responsible for 80% of the business intelligence access. At this time, “predictive analysis” forecasting was starting to also change how organizations do business.

    A Lotta Data (2000) The quantification of new information creation began being studied on an annual bases. In 2000, 1.5 exabytes of unique information is documented per year.

    Control Freaks (2001)Papers were being written about controlling the big data problem. To describe it, they had to define it and they did so with the three V’s….data volume, velocity and variety as coined by Doug Laney, now a Gartner analyst. Work begins on capabilities like language processing, predictive modeling and data-gathering.

    It Was A BIG Year (2003) The amount of digital information created by computers and other data systems in 2003 blows past the amount of information created in all of human (or big data) history prior to 2003.

    Problem Child Becomes Prodigy (2005) Web 2.0 companies are assessed by their database management abilities. The issue becomes a given or core competency. Big Data begins to emerge as an opportunity.Apache Hadoop, soon to become a foundation of government big data efforts, is created.

    Not Your Dad’s Oracle (2005) Alternatives (to Oracle) that are more focused on the usability of the end-user emerge. Big Data solutions that work the way people work collaboratively, on the fly and in real-time are the gold standard.

    Taming the Big Data Explosion (2006) A solution to handle the explosion of big data from the web is more prevalent…Hadoop. Free to download, use, enhance and improve…like Java in the 80s. Hadoop is a 100% open source way of storing and processing data – that enables distributed parallel processing of huge amounts of data.

    Can I Interest You In A Flood? (2008) The BIG part of big data starts to show itself. The number of devices connected to the Internet exceeds the world’s population.

    Real questions: In 2015, will the internet be 500x larger than it is now? Will IP traffic reach one zettabyte?

    How Big is Big? (2008) The term “Big Data” begins catching on among techies. Wired magazine mentions the “data deluge.” “Petabyte” age is coined…too technical to be understood…it doesn’t matter…as it is soon replaced by bigger measures like exabytes, zettabytes and yottabytes.

    No They Didn’t (2008) Yes, they said it. Big Data computing is perhaps the biggest innovation in computing in the last decade. We have only begun to see its potential.

    Business Intelligence became a top priority for CIO’s in 2009.

    BI this….BI that (2010) Recognition and use of Business Intelligence (BI) becomes common as 35% of the rank and file enterprises began to readily employ “pervasive” business intelligence. Look at best-in-class organizations, and you find an adoption of 67% – and it’s moving to self-service.

    Moving On Up (2011) Business Intelligence matures with trends emerging in cloud computing, data visualization, predictive analytics and big data is on the horizon.

    Big Government and Big Data (2012) The Obama administration announces the Big Data Research and Development Initiative – 84 separate programs. The National Science Foundation publishes “Core Techniques and Technologies for Advancing Big Data Science & Engineering.”

    Even Better Than a Rewards Program (2013) (Big) Data as “a real business asset used to gain competitive advantage in the market” becomes accepted. The widespread drive to understand and make use of big data – to remain relevant – is well underway.

    Want to leverage big data analytics for better and more efficient business? Learn more about Teradata’s big data solutions.

     

    Teradata Blogs Feed

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

    Cyber Security in Government 


    Teradata Brochures

     
  • admin 9:51 am on January 23, 2015 Permalink
    Tags: , Cultural, , , , , Impede, ,   

    New Global Study Reveals Cultural Gaps Impede Companies’ Efforts to be Data-Driven 

    CEOs’ rosy view of data initiatives, and barriers to data access, hinder success
    Teradata News Releases

     
  • admin 9:51 am on January 23, 2015 Permalink
    Tags: , , , Meaningful,   

    The Art And Science Of Creating Meaningful Customer Interactions 

    Grocery bagIt’s time to start building something new. A new way to understand customers, a new way to interact with them and a new way to better serve them. There is nothing more important in today’s marketing environment than creating an unprecedented customer experience—one that recognizes, honors and exceeds individual customer expectations…every time.

    The modern marketing environment has morphed from a brand-driven model into a customer-driven one. Marketers who understand the importance of providing a personalized, relevant, customer-centric experience in real time also understand that collecting all the data their customers provide is essential to their organizational success.

    A data-driven marketing ecosystem—one that’s developed by both marketing and IT—is the foundational basis for creating unprecedented customer engagement that ultimately leads to increased loyalty and revenue.

    Creating an unprecedented data-driven customer experience is about getting to know and understand every customer across every channel to provide the experience he or she wants, expects and deserves.

    Customers Are in Control
    Today’s empowered customers have high expectations. They know what they want from their favorite brands—meaningful conversations, real-time communications and an experience that feels seamless and relevant from beginning to end, regardless of medium.

    Rather than having campaigns launched at them, they want to be heard, delighted and engaged with meaningful interactions. Whether it’s online or in-person, consumers want to be recognized, known and understood.

    And that’s good news for marketing and related departments.

    Why? Because the best way to meet and exceed customer expectations is through data-driven marketing. This gives organizations the unique opportunity to innovate with data and applications technology so customers get the personalized experience they expect, demand and deserve…every time they engage with a brand.

    The Marriage of Marketing and IT
    So what, exactly, is data-driven marketing? It’s the process of collecting and connecting large amounts of various types of online data with traditional offline data, rapidly analyzing it and gaining cross-channel insights about customers. And then bringing those insights to market via highly-personalized interactions tailored to the customer, at his or her point of need and in real time.

    When fully embraced, data-driven marketing enables organizations to create experiences that are built around and propelled by the data their customers have provided. The focus for marketers then shifts to developing a better customer experience—fueled by, but not focused exclusively on—customer data.

    Businesses whose leadership is savvy enough to embrace the marriage of marketing and IT to create a data-driven, customer-centric environment are the ones that are winning both market share and customer loyalty. In the “2013 Holiday Shopping Survey” by SDL, 60% of consumers revealed they’d be willing to pay more for a better customer experience. By integrating people, processes and technology, organizations can more effectively meet individual customer expectations with relevant, meaningful experiences.

    Find the Missing Pieces
    Most organizations, however, still have a long way to go to create a true data-driven experience—one that results in a single customer view across all interactions. In fact, only 18% of marketers currently report having a single view of their consumers’ activities, according to the “Teradata Data-Driven Marketing Survey 2013.”

    Discovering all the missing pieces of customer data—from location, sensor, partner, competitor, online, offline and everything in between—is the most critical component of creating meaningful customer interactions. Missing a single source of data can result in a skewed and incomplete understanding. Once marketers have all their customer data available to them, they can begin to form a complete historical picture and a model for predictive analytics.

    Contextual vs. Relevant
    Effectively merging and expertly navigating the art and science of marketing ensures marketers are well informed and equipped to initiate the right conversation with the right customer at the right time.

    But a good thing can definitely be taken too far. It’s important for marketers to understand that contextual data isn’t necessarily relevant. A complete customer view is critical to maintaining that fine balance between remaining important and crossing the line into “The Creep Factor”.

    Creating an unprecedented data-driven customer experience is not just about gathering data to power marketing initiatives. It’s about getting to know and understand every customer across every channel in order to provide the experience he or she wants, expects and deserves.

    Results Every Business Wants
    Investing in the right technology infrastructure enables marketers to listen better so they can create the personalized, relevant and real-time experience savvy customers want and expect. When marketers know more about their customers, they can do more to develop meaningful, long-term relationships.

    The result? Satisfied, loyal customers and measurable revenue growth.

    This story appeared on Forbes.com.

    Teradata Blogs Feed

     
  • admin 9:47 am on January 23, 2015 Permalink
    Tags: , , , ,   

    Whats Next for Data Driven Marketing 


    Teradata Web Casts

     
  • admin 9:44 am on January 23, 2015 Permalink
    Tags: , , , ,   

    Creating a Data Driven Advantage in Healthcare 


    Teradata Brochures

     
c
Compose new post
j
Next post/Next comment
k
Previous post/Previous comment
r
Reply
e
Edit
o
Show/Hide comments
t
Go to top
l
Go to login
h
Show/Hide help
shift + esc
Cancel