Tagged: Learn Toggle Comment Threads | Keyboard Shortcuts

  • admin 9:55 am on July 28, 2017 Permalink
    Tags: Bean, BusinessGrowth, Counter, Enabler, , Learn   

    Bean Counter Or Business-Growth Enabler? What Can The CIO Learn From The CFO? 

    Latest imported feed items on Analytics Matters

  • admin 9:55 am on July 11, 2017 Permalink
    Tags: , , Learn, , Maybe’, ,   

    Maybe You Can’t Machine Learn Everything – But does that mean you shouldn’t try? 

    Latest imported feed items on Analytics Matters

  • admin 9:55 am on September 1, 2016 Permalink
    Tags: , , , , Learn, , Wikipedia   

    What Enterprise Information Management Can Learn From Facebook And Wikipedia 

    Latest imported feed items on Analytics Matters

  • admin 9:51 am on July 20, 2016 Permalink
    Tags: Amount, , , Insane, Learn, , Pokemon, Traffic   

    Pokemon Go Marketing Campaigns Can Drive You An Insane Amount of Traffic – Learn How 

    Latest imported feed items on Analytics Matters

  • admin 9:52 am on February 13, 2016 Permalink
    Tags: Captain, , Kirk, Learn, , ,   

    Want To Get More Value From IoT? Learn From Captain Kirk 

    Just as Captain Kirk could always depend on his steadfast engineer, Scotty to manipulate the warp drives to deliver power to the shields of the starship Enterprise in evasive maneuver situations, so too will companies need to depend on their IT Operations team if they are to navigate their way through the Internet of Things.

    The explosion in the number of applications for Internet of Things (IoT) within the B2B space means that sophisticated sensors can now be found in all kinds of assets from wind turbines, to jet engines. These sensors generate huge amounts of data, and in variable formats.

    Take a Boeing 787 for example. These aircraft generate almost half a Terabyte of data every single flight. Multiply this by the number of flights and aircraft operated by an airline and that will give you some idea of the challenge at hand. This is the new frontier for IT operations.

    Where to start? How about with an infrastructure that enables enterprise readiness? It is not enough to only look at the data lake, or the data warehouse in isolation. These days, it is generally acknowledged that what’s needed is an ecosystem, but that requires more management and input from an IT perspective.

    If we are going to plug the data from Internet of Things into various analytical tools so that we can gain insights from it that can inform mission critical decisions, then it has to be available, reliable, authentic and correct. Of course, it has to make sense cost wise as well. And IT will need to find a way to securely transfer, process, store, and archive it.

    Talking costs – open source on paper would seem a good fit cost wise. But will the IT Operations team be ready to manage the amount and frequency of updates of open source products, compared to enterprise grade software products? Specific update strategies would need to be put in place. With an open source stack, IT Operations need to be prepared to manage new and complex interfaces, and maybe even provide second and third level support themselves. These are the sorts of headaches that just wouldn’t come into the picture with enterprise grade vendors.

    Then we have to consider that many IoT infrastructures will be operated as shared services within large companies. Large shared services and shared IT environments usually require multi-tenancy capabilities. Several business units or divisions will want to make use of the infrastructure, while restricting access to the data to particular groups of users. What sounds trivial might become very tricky in reality. Authentication, access management and so on could be challenging in complex analytical ecosystems or in a cloud-based environment.

    Then we need to talk network. Network admins will have to be ready to make sure their infrastructure is able to ingest the data, transfer it into the repository and manage the peaks of traffic when users generate their Monday morning reports. This is where cloud-based solutions start making sense, for the sake of easing the strain on corporate networks. But wait – what does IT Security say?

    IT Security will be looking at these new kinds of data and assessing whether the data needs to be kept under lock and key to protect the privacy of users and other confidentiality clauses. And while IT Security Officers have well established best practices for the handling of ERP, financial, HR data, that is simply not the case when it comes to sensor data. Why? Judging whether sensor data may or may not carry a significant insight on customers’ processes and even revenue streams (think output data of gas turbines), is far more nuanced.

    In balance, while it is hard to argue that IoT will bring great (and positive) change to an organisation – the reality is that IT operations will not always love this brave new world. A recent study found that most companies are currently only getting benefit from a tiny fraction of the data that’s available from IoT. There is a huge potential for uplift in competitiveness and productivity still to come from IoT – but companies will need to ensure that IT is involved in decisions made from the start before engaging their warp drives.

    Interested in discovering how IoT data can generate more value for your company when combined with business operations and human behavioral data? Read on.

    This post first appeared on Forbes TeradataVoice on 04/12/2015.

    The post Want To Get More Value From IoT? Learn From Captain Kirk appeared first on International Blog.

    Teradata Blogs Feed

  • admin 9:53 am on January 27, 2016 Permalink
    Tags: , , , Learn, ,   

    What Marketers Can Learn From The Evolution Of Super Bowl Ads 

    Tablet computer with handThe Super Bowl is the priciest commercial time on TV. Over the past decade, the average rate for a 30-second ad during the Super Bowl game has increased by 76%, and this year, a 30-second spot will cost almost $ 5 million, according to Kantar Media. 

    But the price isn’t the only thing that’s trending upward. Kantar Media’s research has also found that Super Bowl ads are increasingly:

    • Prevalent. The past six Super Bowls have been the most ad-saturated in history. Each has included more than 47 minutes of commercial time. During the big game on February 7, ads will take up nearly 50 minutes of the broadcast.
    • Long-form. Many Super Bowl advertisers now opt for long-form commercials. Kantar Media says that’s part of an effort to tell a deeper story and further engage viewers. In the past two games, 37% (2015) and 40% (2014) of brand ads were 60 seconds or longer, the highest shares since at least 1984. By comparison, the normal proportion of long-form ads on broadcast networks is about 6%.
    • Part of a larger social media engagement strategy. Kantar Media’s research shows that hashtags are now the most popular call-to-action mechanism. Last year, 57% of non-promo ads (34 of 60) contained a hashtag, while less than one-half had a URL. Only 5 percent of ads mentioned Facebook.

    What can marketers learn from these trends?

    • Data helps you determine which investments are worthwhile. Super Bowl placement is no guarantee of success, and no one invests $ 5M+ on a whim. Your marketing campaigns need to be guided by data driven solutions. That’s the only way you can tell with certainty what works, and what doesn’t. Some brands have bowed out of Super Bowl 50. For instance, Nissan and Ford are focusing their resources elsewhere. On the other end of the spectrum, LG will be appearing for the first time, and Taco Bell will be returning after an absence of three years.
    • Engagement is key. Today’s consumers are flooded by marketing, and it’s impossible to stand out unless you can make relevant, meaningful connections. Without relevance, relationships are short, attention wanders and marketing campaigns fall flat.
    • Multi-platform use is on the rise. The evidence is mounting that people want to consume media using their own personal digital ecosystems. They’re seeking out an individualized experience. They want to control what they’re seeing, as well as when and how they’re seeing it.

    Underlying ALL of this is the undeniable trend toward Individualized Marketing.  In a global survey released around the time of last year’s Super Bowl, Teradata found that nine out of ten marketers see Individualized Marketing as the future.  So it is clear:  To optimize your $ 5 –  $ 10 million television advertising investment, and extend both its real-time and long-tail reach into loyal customers and new prospects alike, your integrated marketing strategy must include digital / social marketing, insightful data management, and sophisticated analytics so you can show the return on this hefty marketing investment.  Doing so will also help you set the stage for confidently discussing your Super Bowl 2017 marketing budget. 

    What are you plans for Super Bowl 50?  Share your ideas below!  

    The post What Marketers Can Learn From The Evolution Of Super Bowl Ads appeared first on Teradata Applications.

    Teradata Blogs Feed

  • admin 9:53 am on December 6, 2015 Permalink
    Tags: , , Learn, , ,   

    Data Modeling Requires Detailed Mapping — Learn Why 

    Mapping is an important step to understanding your data and where the data resides in your ecosystem. Mapping takes us from the known to the unknown and is effectively accomplished by using mapping tools, adopting best practices, and having a common understanding of how the mappings will be used. But mapping does take a considerable amount of time and requires a person with extensive knowledge in the source or target system or both.

    Our team maps from an industry logical data model (core model) to access path building blocks — then to semantic structures such as dimensions, or lower to higher level facts.

    Access path building blocks (APBBs) are designed to help the semantic modeler develop dimensions and facts (which are specific and denormalized) for the semantic data model from the core data model (which is normalized and generalized). APBBs bridge the gap between the dimensional and normalized logical data models. To help the modeler in using APPBs, construction maps are included in the SMBBs to illustrate how a dimension can be built from the core data model (the appropriate Teradata industry data model) using the access path building blocks. The following picture is an example of an APBB construction map. The green boxes represent tables in the core data model (the Teradata Financial Services Data Model, in this example), the orange boxes are the APBBs, and the white boxes are the resulting dimensions.

    In this example the construction map visually layouts the data needed to identify a person who is insured by a policy (join path) and the diagram can also be understood by business analysts that may not model or write SQL.
    Performing mappings helps our team identify gaps in both models. In one case, core data may need to be added to the semantic data model in the other, the core data model may need to be expanded with data required by BI reports. The gap analysis identifies the missing pieces of data need to address the business requirements.

    Papierniak picture for blog Nov 30Initially, our group performed detailed attribute-to-attribute mapping, but then switched to higher-level, entity-to-entity mappings — to save time. The time saved on detailed attribute-to-attribute mappings, many of which are obvious, was instead focused on describing the purpose of the mappings thru filter and join notes. In the picture above Gender Type is an entity while Gender Type Description (in Gender Type) is an attribute.

    We found that we could save a considerable amount of time by reusing the mappings from areas such address or product in the same industry or across industries. And using a tool to perform the mappings makes them easily reusable, adaptable, and assists in standardizing around best practices.
    Mappings help with many things, including:
    • Focusing on specific areas in an ecosystem
    • Finding and resolving information gaps as well as design gaps
    • Creating the core layer views for the semantic layer
    • Establishing a reusable base for common content such as location or product
    • Supporting a more precise way of communicating and refining details during design and implementation

    Our team finds mapping to be useful, reusable, and educational — and is a worthwhile investment of our time.
    For mapping we use Teradata Mapping Manager (TMM) found on Teradata Developer Exchange. Information on our Industry Data Models (iDMs) and Solution Modeling Building Blocks is on http://www.teradata.com.

    Karen Papierniak cropKaren Papierniak is a Product Manager responsible for development of Teradata’s Industry Solution Modeling Building Blocks and Data Integration Roadmap portfolio — that spans eight major industries and used by customers worldwide. Karen’s roles at Teradata have been in software development, systems architecture, and visual modeling while working in a variety of industries including retail and communications.

    The post Data Modeling Requires Detailed Mapping — Learn Why appeared first on Data Points.

    Teradata Blogs Feed

  • admin 9:51 am on September 8, 2015 Permalink
    Tags: , Empowerment, Learn, ,   

    Big Data Success Starts With Empowerment: Learn Why and How 

    As my colleague Bill Franks recently pointed out on his blog, there is often the perception that being data-driven is all about technology. While technology is indeed important, being data-driven actually spans a lot of different areas, including people, big data processes, access, a data-driven culture and more. In order to be successful with big data and analytics, companies need to fundamentally embed it into their DNA.

    To be blunt, that level of commitment simply must stem from the top rungs of any organization. This was evident when Teradata recently surveyed 316 senior data and IT executives. The commitment to big data was far more apparent at companies where CEOs personally focus on big data initiatives, as over half of those respondents indicated it as the single most important way to gain a competitive advantage.

    Big Data Success Starts With Empowerment, Chris Twogood, Data Points, TeradataIndeed, industries with the most competitive environments are the ones leading the analytics push. These companies simply must find improvements, even if the needle is only being moved in the single digits with regards to things like operational costs and revenue.

    Those improvements don’t happen without proper leadership, especially since a data-driven focus impacts just about all facets of the business — from experimentation to decision-making to rewarding employees. Employees must have access to big data, feel empowered with regards to applying it and be confident in their data-driven decisions.

    In organizations where being data-driven isn’t embedded in the DNA, someone may go make a decision and attempt to leverage a little data. But, if they don’t feel empowered by the data’s prospects and aren’t confident in the data, they will spend a lot of cycles seeking validation. A lot of time will be spent simply attempting to ensure they have the right data, the accurate data, that they are actually making the right decision based on it and that they will be backed up once that decision is made.

    There is a lot of nuance with regards to being data-driven, of course. While all data has value, there are lots of levels to that value – the challenge generally lies in recognizing the values and extracting it. Our survey confirmed, for instance, just how hot location data is right now, as organization work to understand the navigation of their customers in order to deliver relevant communication.

    Other applications of data, according to the survey, include the creation of new business models, the discovery of new product offers, and the monetization of data to external companies. But that’s just the tip of the iceberg. Healthcare, for example, is an up-and-coming industry with regards to data usage. An example is better understanding path to surgery — breaking down the four or five steps most important to achieving a better patient outcome.

    But whether you’re working in a hospital or a hot startup, and working to carve out more market share or improve outcomes for patients, the fundamentals we’ve been discussing here remain the same. Users must be empowered and confident in order to truly be data-driven — and they’re not going to feel that way unless those at the top are leading the way.


    The post Big Data Success Starts With Empowerment: Learn Why and How appeared first on Data Points.

    Teradata Blogs Feed

  • admin 9:55 am on February 7, 2015 Permalink
    Tags: , , , Learn   

    What You Can Learn From Google Glass 

    logo_420_color_2xWhen Google Glass started selling prototypes of its hi-tech headset in May 2013, the response to the new technology was decidedly mixed.

    Critics cited privacy concerns, given that Glass users – or “Glassholes,” according to some – could easily record those around them without anyone’s consent.  Others wondered if Glass would end up alongside the likes of the Segway, in the pantheon of technology that doesn’t quite live up to its hype.

    Now just over a year-and-a-half later, Google Glass is no longer being sold to consumers, the team has left Google X (Google’s development arm), and new leadership is taking over. Is it actually the end for the embattled device, or will Glass rise again in new and improved form?

    I’ve heard a variety of different answers to that question.

    For those who found Google Glass problematic, this new development confirms everything they thought was wrong – with the product, and with the way Google launched and marketed it.

    For those who believe augmented reality is very much on the rise, Glass still has plenty of life left.

    Either way, the continuing evolution of Glass offers some important lessons about marketing, customer experience management and technology. For example, Glass shows us that:

    • New technology creates new issues. Privacy concerns will continue to plague Glass – along with any other device that allows people to capture images and video of those around them without their knowledge. The reality is that any new technology is bound to come with a new set of concerns or objections, problems we didn’t have before, simply because there was nothing to cause them! It’s up to those who introduce new technology to help people get comfortable with what’s being offered – or to adjust the function of the technology to put people at ease.
    • Sometimes you have to fail first. It’s rare that a great idea is great from the very beginning. If Google Glass becomes a massive success after this rumored reboot, the year-and-a-half of sometimes-negative press will largely be forgotten.Failures teach us what doesn’t work, and they enable us to let go and move on. Failures give us the chance to improve and iterate – and to catch other mistakes before they turn into full-blown issues. Failures make us push to do what we do better. If we see them as hurdles, not black holes, we can keep leaping over them until we hit the victory line.
    • Try, try again. When a project fails to perform, you have a few different options: wait a little longer to see if it picks up steam; close up shop for good; or work on improving the product to see if a little evolution does the trick. What Google always does, whichever option they choose, is try again. Google seems to always have new products, new advances and new ways to connect its ideas with the people who will find them valuable. Google Glass may end up being a bust of an idea (unlikely, but possibly), but Google isn’t going anywhere.

    Of course, most of us don’t have millions of dollars to put into potential success or potential failures. Then again, most of us don’t lose millions when our risks don’t pay off.

    What we can learn from Google’s example, whatever industry we’re in and whatever financial bracket we occupy, is that our success depends on our willingness to stay in motion: to adapt, to get back up after we fall and to try a new way to move forward.

    From the smallest marketing campaign to the biggest tech launch, the only way to win is to never let the last idea be your last idea.

    The post What You Can Learn From Google Glass appeared first on Teradata Applications.

    Teradata Blogs Feed

Compose new post
Next post/Next comment
Previous post/Previous comment
Show/Hide comments
Go to top
Go to login
Show/Hide help
shift + esc