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  • admin 9:54 am on August 31, 2017 Permalink
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    Five Ways Analytics and Data Science Can Add Business Value 

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  • admin 9:51 am on April 20, 2017 Permalink
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    5 Ways Cloud Vendors are Dealing with Data Privacy Concerns 

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  • admin 9:52 am on January 10, 2017 Permalink
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    Ditch the Old Ways of Product Management. Say Hello to Product Innovation. 

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  • admin 9:47 am on November 8, 2016 Permalink
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    5 Ways Data Gravity Affects Business Intelligence in the Cloud 

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  • admin 9:52 am on February 23, 2016 Permalink
    Tags: Deploy, , , ways   

    Deploy Hadoop Multiple Ways 

    The current Hadoop market is dominated by two players being Cloudera and Hortonworks. Both are built on top of open source Hadoop and are very similar in their packaging except with a few differences in applications (Impala, Ambari, Ranger, Sentry etc etc) from a software perspective and their support structures. Standing on the sidelines reminds me of watching a similar game played out over a decade ago in the linux space when you had Redhat, Suse and others all competing in the same space.

    For our customers thinking about going down the Hadoop pathway they often have different objectives in their journey and come from different angles in how to begin. Sometimes they will setup a lab environment with a small deployment of the open-source no frills Hadoop and go from there by adding packages and building out their cluster from that. The risk is when to identify that the lab is ready for the prime time in a production sense and whether they should stick with the open source version or to convert across to an Enterprise grade distro complete with support moving forward. Or they will decide to go all in and begin their journey with an Enterprise grade Hadoop from day 1. The question on their mind is which one to choose?

    I’m often asked by customers and peers which distro to go with either Cloudera or Hortonworks. My answer will often be prefaced by a range of commentary including support options, resources in the market and who else is using which and how they are going on the journey. I’m in the enviable position to offer my views and recommendations backed by a deep understanding of multiple factors. However recently I’ve been challenging those asking me the question as to why they should hedge all their bets on a single vendor? After all if the differences aren’t too great then why not go with a dual vendor strategy?

    A single data lake versus multiple data lakes

    If you’ve heard of the concept of the data lake then you know it’s the approach of landing data of all shapes and sizes onto a low cost no schema environment. The data lake is then used to refine data and serve up to multiple analytic environments such as a data warehouse, SAS or Teradata’s Advanced Analytics platform Aster. The common approach in deploying a data lake thus far has been a single data lake for the organisation. This design approach is similar to the mindset in the 90’s with data warehouses where we would build a single warehouse that would be all things to all people. In modern times we now have some customers with multiple data warehouses with the primary driver being the requirement for separation of data and workloads. Especially in government we see a need for a data warehouse to store highly classified datasets and to keep them physically separated from other datasets. Take this design and now apply it to the data lake concept. Whilst a single data lake has the merits of storing all of the data under one roof handling different workloads and different security rights the reality is that it can quickly become a data management nightmare. The driver for having multiple data lakes is not a technology driver but rather driven by corporate needs for isolating different workloads, data security requirements, country boundaries, and corporate divisions.

    Deploying your data lake 3 ways

    When it comes to a data lake deployment strategy you essentially have the choice of 3 architectures. Shared nothing, Shared management or Shared everything.

    The Shared Nothing deployment

    The shared nothing architecture you may already be familiar with if you’ve been knocking around Massively Parallel Processing (MPP) architecture for a while. This concept is based on the view that each Hadoop cluster has it’s own dedicated storage, processing and management. An example of this is depicted in the following diagram:


    The Shared Management Deployment

    Using this deployment model, you maintain the separation of clusters, however centralize the management of the clusters under a single management layer. This approach still physically keeps the data separate and meets the numerous compliance and security requirements, however reduces administrative overhead of managing multiple clusters.


    The Shared Everything deployment

    This approach is how many have deployed their data lakes using a single cluster to service multiple data types, multiple users and multiple workloads.


    How you choose to deploy Hadoop is entirely up to your data security, workload and geographical boundaries. What you have here is flexibility. Don’t think that your data lake has to be a single lake with a single management layer. If you need to build multiple lakes, don’t be afraid to.

    Ben Davis is a Senior Architect for Teradata Australia based in Canberra. With 18 years of experience in consulting, sales and technical data management roles, he has worked with some of the largest Australian organisations in developing comprehensive data management strategies. He holds a Degree in Law, A post graduate Masters in Business and Technology and is currently finishing his PhD in Information Technology with a thesis in executing large scale algorithms within cloud environments.

    The post Deploy Hadoop Multiple Ways appeared first on International Blog.

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  • admin 9:51 am on December 17, 2015 Permalink
    Tags: , , , Nurture, ways   

    5 Ways to Create and Nurture Customer Loyalty 

    by Jenne Barbour

    Customers move now at a speed that is faster than ever. That speed, coupled with the breadth of options available to them and the constant barrage of marketing messages they’re pelted with 24/7, chip away at the customer relationship—or keep it from ever getting started.

    Without properly engaging new customers or strengthening one-to-one connections with current ones, relationships may never take hold. Or if they do, they could vanish in the blink of an eye. Although it may seem like customer loyalty is eroding in the current environment, it is actually just evolving.

    Value is the Key

    More than ever, consumers are willing to create relationships with brands in exchange for tangible value. And that value can flow both ways. Rich insights are derived when customers share information, which in turn empowers brands to increase engagement and improve profitability.

    Delivering the right experience is the result of listening to the information customers provide and answering with the most relevant approach to meet their needs. This relevance—driven by individualized insights—elevates the loyalty programs of the past to the engagement of the future.

    These five tips will help you foster and maintain a strong relationship with your own customers:

    1. Keep Pace With Expectations

    As product and service options grow, consumer expectations increase. What they bought in the past may not be what they want in the future. If you do not evolve with your customers, you could soon become obsolete. Know what your customers want and need from you, then make sure what you’re delivering is on target. If you’re not giving them what they want, someone else will.

    1. Deliver Integrated Value

    It’s critical to recognize each customer individually and understand his or her relationship with your company, its products and its services. You also need to fully integrate the customer’s insights into the entirety of that person’s experience with your brand. This proves that you know him or her as an individual, and that you leverage this deep understanding to serve the person’s needs.

    1. Connect in the Mobile World

    Digital disruption isn’t just about the impact to the marketplace or how it affects the way you do business. It’s also about reaching your customers across their preferred devices. It’s commonplace for people to pull out a smartphone or tablet to find virtually anything. They can research and buy whatever they need with just a click. As consumers increasingly shift to mobile, they expect their experience to be consistent across all platforms and available everywhere they are. The individualized experience spans online and offline boundaries, often using mobile as the connection point between the two worlds.

    1. Focus on Customer Engagement

    Vast budgets can be wasted on disposable interactions that will never be seen. After all, savvy consumers have grown accustomed to ignoring the irrelevant, making some ads invisible to them. In our increasingly connected world, personalized loyalty provides the opportunity to interact with and engage the individual—as long as the interactions honor the relationship and add value to the customer.

    1. Be Transparent

    Your customers will have concerns about what you’re doing with their information. They may understand that sharing data leads to a better overall experience with your brand and more relevant offers, but they may also be cautious. It’s important to respect their concerns and nurture their trust. You can do that by being open about how their data is being used and giving them opt-out alternatives.

    Two-Way Communications

    By centralizing your customer view to include traditional behavioral data, you’ll crystallize your understanding of who your best customers are along with what they need and want from your brand. Using data-driven solutions, you can shift from merely approaching audiences to creating value exchanges on a one-to-one basis. That experience will touch every point of interaction with your customers: online and offline, wherever they are and whenever they’re ready to interact.

    A two-way exchange with customers establishes longer lasting relationships, fuels engagement and allows you to increase your wallet share. Plus you get the opportunity to find out what they are thinking—and what they think about you.

    Jenne Barbour leads marketing strategy for Teradata Marketing Applications. She works to transform an individual’s customer experience into a beneficial bond with the brand.

    This article originally appeared in the Q4 2015 issue of Teradata Magazine. For more tips on strengthening customer loyalty in today’s constantly changing marketplace, visit TeradataMagazine.com.



    The post 5 Ways to Create and Nurture Customer Loyalty appeared first on Magazine Blog.

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  • admin 9:51 am on December 5, 2015 Permalink
    Tags: , , Adapt, , , , ways   

    Four Ways Customers Changed In 2015 And What You Can Do To Adapt In 2016 

    Change Email Service ProviderMarketing trends that began a few years ago can no longer be ignored. Looking back over the past several months, it’s become increasingly clear that:

    1. The customer is in charge. You can manage the customer relationship, but don’t waste resources trying to control it. Done right, marketing creates interactions that foster a greater understanding of the uniqueness of each individual’s needs and desires. That means your job is to captivate, not control.

    2.“Traditional” customers are just as demanding as millennials. As I mentioned last month, my mom is a grandparent who, at times, shops like a millennial. She’s not 100% brick and mortar. She’s not 100% digital. But regardless of where or how she shops, she expects individualized customer service. My mom is a great reminder that consumers across all demographics are now more demanding. If you don’t engage, you’ll lose – and not just millennials, but more traditional shoppers, too.

    3. Marketers need to choreograph a conversation across multiple diverse channels. Let’s say you send customers an initial piece of collateral by direct mail. When one of those customers comes into the store, you need to pick up that same conversation. You need to know what offers have been presented, what drove them into the store. Then, after the sale, you need to have follow-up dialogue – maybe by email or social media – to keep the conversation going.  The marketing applications you’re using have to enable the brand to carry on a seamless conversation across multiple touchpoints and channels.

    4. Engagement must be appropriately paced at the rate of the individual. As I’ve outlined in #1-3 above, marketing success requires engagement, but that engagement must be delivered when it’s most meaningful. You can’t just blast out a message and expect all of your customers to be at the same place on the experience curve. Instead, you need to be responsive to your customers as individuals. Each one wants to be part of the dialogue at their own pace.

    How can you best respond to changes like these and create the kind of marketing campaigns that drive revenue? I suggest you observe what I call “the four essential truths of real-time customer engagement.” Namely:

    1. If you don’t know who you’re talking to, it’s almost impossible to be relevant.
    2. If you’re not managing the journey your customer is on, someone else is.
    3. If you can’t decide which of your offers are most appropriate, you can’t expect your customer to do it for you.
    4. If you can’t get your message delivered at the right time, it doesn’t really matter how good it is.

    More to help me remember them than anything else, I made up a relatively simple mnemonic. In short, you need to:

    …and if you follow those links, you’ll find a blog post that goes into detail about each one.

    It’s December, the season for thinking ahead and planning for the New Year. If your company is prepared, you’ll be able to withstand – and even embrace – all that’s evolving, while maintaining your competitive edge. For more about how you can adapt to changing consumer behaviors, check out the interview we recently streamed over Periscope.

    The post Four Ways Customers Changed In 2015 And What You Can Do To Adapt In 2016 appeared first on Teradata Applications.

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  • admin 9:55 am on October 31, 2015 Permalink
    Tags: , , BrickandMortar, Demise, Exaggerated, , Greatly, , , ways   

    The Demise Of Brick-and-Mortar Retail Is Greatly Exaggerated: Four Ways Advanced Analytics Can Help 

    E-commerce may be the darling of retail of late, but that doesn’t spell the end for brick and mortar stores. Far from it, 93% of total retail sales worldwide still take place in a store. And with consumers willing to pay 50% more for items that they can touch and see, it’s obvious that physical stores still represent a significant opportunity for any retailer.


    So what can retailers do to maximize this opportunity? Here are four ways that advanced analytics can transform the profitability of any physical retail store:

    Have the Right Stock in the Right Place at the Right Time

    Most retail chains develop assortment plans using high-level historical sales with some rules-based customization across store clusters. Unfortunately, this often results in poor inventory performance and customer dissatisfaction due to stock-outs. Why? Because the plans don’t account for micro-variations in demand.


    Consider these scenarios:

    A) Stocking the sizes that match your customer demographics. Let’s look at an example. In Store B (Figure 1), there is less demand for small and medium sizes, but since stock is not adjusted to reflect this demand, these garments have to be marked down at the end of the season, while store A has the same issues with regard to larger sizes. Analysis of SKUs that sell only during mark-downs, can help the retailer get the right sizes into the right shops. This approach worked for the retailer in this example and led to more than 5% increase in sales and around 50% reduction in mark-downs for affected products.

    B) Factoring in the impact of competition.
    This is especially so for stores in shopping center locations. Retailers will need to consider whether other retail chains in the same mall carry the same products at a different price point.

    C) Forecasting based on weather. For one retailer, sales of hosiery in winter made up 31% of inner wear sales for those stores in cold climes. But these stores received roughly the same amount of stock as the stores in the tropical latitudes, increasing the likelihood of stock-outs and dissatisfied customers (Figure 2).


    Price it Right

    Despite the availability of granular data around price and sales, many pricing decisions in retail are still based on gut feel, past experience and simplistic Excel analysis of summarized data.

    The result? Retailers tend to apply proportional price increases in response to manufacturing cost increases which could lead to a substantial volume decline. If retailers use price sensitivity analysis on their granular data, they could be far more precise about which prices should go up, by how much and at what time. A totally different approach to pricing that could yield incremental revenue for the company.

    Maximize the Impact of Promotions

    Analysis of promotions for a single clothing brand in a number of national retail chains over 2 years showed that only a third of the promotions for this brand yielded more than 20% uplift over average and 41% didn’t yield any uplift at all.

    Decision tree analysis (like the example in Figure 3) can tell us why. If promotions are not aligned with special events, such as Christmas, then they might not work as there is no additional foot traffic at that time. Just as surprising – 33% of promotions don’t yield the results expected because of insufficient stock. And what about your competitors? If a deal similar to that advertised can still be found cheaper at a competitor, the promotion is not going to take off. Retailers should also be able to find out if an unexpected effect of a promotion was a “halo effect”, increasing sales across the range.


    Get Value for Money on Marketing Spend

    One retailer I worked with had a markedly different spend on marketing in two years. However, the sales across the two years indicated that the extra marketing spend had not translated to additional sales. Clearly, this is a case where understanding the factors that contribute to the success of individual marketing efforts can optimize the overall spend.

    Some factors that impact on the ROI?

    • Brand equity. Is the brand a category leader or a new entrant?
    • Channel mix. Customers may receive marketing promotions from different channels and campaigns, but which one(s) had an impact with the customer?
    • Contractual obligations. Which retail marketing efforts are as the result of the need to support suppliers or the need to consume media during specific times?
    • External factors. Economic cycles, competitor advertising and special events could all affect the return on marketing investment.

    Retailers now have access to increasingly granular operational data such as point of sales (POS), inventory. When combined with other data, such as catalogue promotions and media consumption, and viewed through the lens of the right analytics, retailers can understand customer buying behavior at stores better than ever before.

    Never want to miss another opportunity? Then find out how you can overcome the barriers to analytics adoption in your business today.

    This post first appeared on Forbes TeradataVoice on 22/10/2015.

    The post The Demise Of Brick-and-Mortar Retail Is Greatly Exaggerated: Four Ways Advanced Analytics Can Help appeared first on International Blog.

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  • admin 9:51 am on August 2, 2015 Permalink
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    Six Ways To Get The Most Out Of Your Industry 4.0 Data 

    The fourth industrial revolution, or Industry 4.0 for short is changing the way the industrial sector looks at data and analytics. Many industrial businesses feel overwhelmed by this data, a recent survey  reveals, with many finding that “they cannot use them in an integrated manner or access them at company level”. Industry 4.0 relies on data as its beating heart – in fact, 90% of those surveyed were “convinced that the ability to efficiently analyse and effectively use large data volumes will be of vital importance for the future success of their business model.”

    Those taking on the Industry 4.0 challenge need an ecosystem that will allow them transform “big data into smart data”, one of our customers describes it.


    For businesses that are getting started on this data journey, here are 6 areas to consider:

    1. Focus on your data and users, not your applications. Technological advancements and enhancements are the norm. Applications that are here today, could be gone tomorrow. If you make an industrial or analytical application the centre of your data and analytics ecosystem, and that application has to be retired, you’ll need to spend time and money re-architecting your ecosystem. By building your analytics ecosystem around your data and how your users could utilise that data to make better decisions, you would not have to reinvent the wheel, every time an application is retired and a new one brought online.
    2. Be open to change. Today’s industrial businesses have a bevy of operating standards such as OPC UA, SCADA, and more recently, JSON. You couldchoose to embed pre-defined connectors within your ecosystem to speed up data integration, but that could stymie the ability to fully integrate all available data, regardless of format or standard. An open set up allows you to efficiently and effectively integrate all kinds of standards, present and future, without being limited.
    3. Context gives data its meaning. Assets change over time. So do factory lines, production processes and products. Specific data points are meaningless unless they are seen in their current and historic context. Take for instance, a sensor reading. To truly understand what the reading means, and whether remedial action is needed, we need to know the surrounding conditions at the time of the reading.
    4. Don’t leave anything out. Industrial processes are complex – sub-processes, assets, and systems (planning, operational and control systems, such as, ERP, MES, SCM, PLM) all churn out data sources of interest to the business. To build a complete picture of industrial processes you have to bring together and analyse all available data sources so that their interdependencies can be fully understood.
    5. Prepare to go large. There are various predictions and debate around the data tsunami that threatens to overwhelm businesses as more industrial Internet of Things come online, and whether that matters. Predictions are just that – they do not replace the need to prepare for the future. Choosing to build on an architecture that is based on linear and virtually unlimited scalability is the practical approach for industrial companies to take today.
    6. Different approaches needed for different types of data. Businesses have to contend with new data, different data, faster data, all of which require different solutions. From an IT perspective, that might mean implementing Hadoop, or a data lake, or re-thinking the data warehouse. Before you make any decisions, know that a “one size fits all” approach to data won’t work, especially in the complex world of industry 4.0.

    I’ll end with an inspirational message from a survey conducted by The Economist Intelligence Unit. This survey found that companies that get the groundwork right, find that their employees will understand the value of data to their work, use it effectively, and keep coming back for more – in what becomes a “virtuous circle of data” that underpins commercial and financial success.

    This blog first appeared on Forbes TeradataVoice on 05/21/2015.

    The post Six Ways To Get The Most Out Of Your Industry 4.0 Data appeared first on International Blog.

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  • admin 9:51 am on June 7, 2015 Permalink
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    4 Ways Businesses Can Get More From Their Data 

    perspectiveby Claudia Imhoff

    BI has now existed for more than two decades, yet few organizations have progressed much beyond fundamental reporting and basic multi-dimensional analysis. Simple descriptive analytics—relating what happened and what is happening—serves as a good starting point for dabbling in BI, but this information has very little value.

    Organizations can get more value with diagnostic analytics that can answer “Why?” questions. For example: Why are sales going down in certain regions? Why are campaigns lagging for certain products or channels?

    The next questions are: Will these actions continue? Where will we be in six months in terms of costs? Is this trend good or bad? Gaining that kind of insight into the future and deciding whether to promote the trends or modify them to create more acceptable results requires predictive analytics.

    This leads to the ultimate question: What should I do? This can be answered by prescriptive or optimization analytics. If the trend is a good one, how do we make sure it will continue? If it is not good, what do we need to do to change its trajectory? The ability to create “what if” scenarios, complete with optimized results, gives an organization a clear path of action, resulting in perhaps the most value the company can get from its data.

    These four suggestions can help businesses get more—much more—value from their data by building upon their current analytics:

    1. Recognize two distinct employee populations: information producers and information consumers.

    Producers are relatively techno-savvy. They can identify the data and sources they need for analyses and perform many integration or “data wrangling” processes with minimal IT support. Producers can also build models, mine the data, perform statistical operations and do other tasks. 

    Consumers may not have the time, experience or desire to produce the analytical results they need but are ready, able and enthusiastic about using better information and analyses in their day-to-day decision making.

    Examine your analytical environment to determine if it properly supports both audiences with the right data, analytical technology, visualizations, dashboards, data preparations and other requirements. If found lacking, add the appropriate technological and educational support to your arsenal of analytical prowess.

    2. Embed analytics in business workflows and make analytics usable through BI or other services.

    Information consumers can get increased value from analytics simply by having it available at the appropriate points in their day-to-day activities. By studying these workflows, producers or IT resources can ensure that analytical results are readily available to operational personnel in a fashion that is compatible with the consumers’ familiar user interface. For example, making a risk assessment analytic available to a loan officer at the appropriate point ensures the entire process is seamless and far more accurate.

    3. Allow multiple sources of data to be easily combined for more complete information.

    Modern analytical architectures have multiple sources of data and analytics, including data warehouses, experimental “sandboxes,” spreadsheets and data marts. Information consumers may need access to combinations of these sources to get accurate and quality analytic results.

    Virtualizing or blending data from different sources can be simply handled with the data management and analytical technologies available today. The business users get a view of the data that allows them to perform analytics for a much deeper understanding of an unfolding situation, events being influenced by internal and external factors, or a broader knowledge of entities such as customers or suppliers.

    4. Create an environment that performs optimally for all analytics.  

    The environment must support simple, run-of-the-mill queries as well as the highly complex and often unplanned analytical ones. It must support traditional structured data and multi-structured big data for analyses. The environment must also return results rapidly for all analytics. And it must present the results in a cohesive and comprehensible fashion to the business community. These are tall orders for most analytical environments.

    Meeting those requirements often leads to an architecture that embraces production BI/analytics in a traditional enterprise data warehouse environment while supporting the experimentation of big data in an investigative computing area. The extended data warehouse architecture is an example of one such solution. It allows seemingly conflicting processes to work in harmony, perhaps using different technologies to handle the various forms of data and queries while still ensuring compatibility across the various platforms. 

    Set the Stage

    While these suggestions may be helpful, perhaps the first step toward optimizing the value of your data is to confirm that your organization advocates the idea that data is a fundamental asset—one that must be used to improve every aspect of the enterprise’s decision-making process. This foundational vision sets the stage to truly get the most from your data as you advance from descriptive to prescriptive analytics. 

    This article originally appeared in the Q2 2015 issue of Teradata Magazine.

    Claudia Imhoff, president of Intelligent Solutions and Founder of the Boulder BI Brain Trust (BBBT), is a writer, speaker and expert on data warehousing, BI and analytics.

    The post 4 Ways Businesses Can Get More From Their Data appeared first on Magazine Blog.

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