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« Customized Voice of The Customer Analytics using SAP HANA |

Posted by Timo Elliott on Tuesday, May 21, 2013 · Leave a Comment 

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It was a hectic SAPPHIRE NOW event in Orlando last week! In this series of posts, I’d like to give you a flavor of what it’s like to attend the event from an analytics point of view — and I’ve included links to some of the great analytics content that came out of the show.

Here’s part one: the run up to the show…

I was a “social media ambassador” for analytics for the conference – my job was to keep track of everything about business intelligence, analytics, and big data at the event. After entertaining myself with my introductory animated gif above, I prepared a pre-show reading list of the useful blogs and links.

I set about preparing my SAPPHIRE NOW survival gear – mostly lots of camera gear and electronics but also my “HANA HANA HANA…” t-shirt that I thought might come in handy…

Unfortunately, just before I was due to leave, I managed smash the screen of my iPhone screen – no phone, email or instagram during the show? Gulp…  Luckily, I managed to find a place that offered an (expensively) same-day screen replacement service, and I was back in action (phone shown here with an appropriate “cloud” reflection):

On the Friday before the conference, SAP announced the new SAP Lumira Cloud product (in beta), and had me busy explaining the renaming of SAP Visual Intelligence.

Early on Monday morning, I started the 15+ hours of travel it took to get to Orlando – hey! what’s that view from my airline window?!

The travel was wonderfully uneventful, for once, and I just had time for a quick shower before heading off to the Human Face of Big Data networking evening event sponsored by Intel.

The inimitable Steve Lucas (now in charge of both big data and analytics at SAP) opened up the evening with his “ah-hana” moment: some personal thoughts on how big data affects his life as a type 1 diabetic.

And author Rick Smolen gave some great examples of how big data will affect all of our lives in the future.

And Intel were on hand to explain their vision and the SAP partnership around Intel’s Hadoop distribution.

Lucky attendees walked away with a signed copy of Rick’s book, which is suitably… big! I strongly recommend it for anybody who wants to get a feel for what is becoming possible with analytics (or get the iPad app: it’s a selection of the better stories, and it’s much cheaper and easier to lift!)

Here’s a Vine recording of some of the pages:

In between networking and catching up with old friends, I had the opportunity to interview several attendees on camera to get their thoughts on what big data means to them (video in preparation!):

Time to head back to the hotel through the walkways that snake around the conference center and the closest hotels:

…and that was the end of Day 0! More soon.

Share and Enjoy

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Filed under All, Featured · Tagged with #bigdata, 2013, Analytics, Big Data, BusinessObjects, Orlando, Photos, SAP, SAPPHIRE, SAPPHIRE NOW, SAPPHIRENOW

Article source: http://timoelliott.com/blog/2013/05/photo-essay-part-1-analytics-at-sapphire-now-pre-show.html

Dashboards have become, in the minds of many, the most useful new form of information display that has emerged in the last decade or so. Since its publication in 2006, my book Information Dashboard Design has consistently been a best seller in the field of data visualization and the unchallenged authority on the visual design of dashboards, but it is in need of an update. In late July or early August, this problem will be solved with the publication of Information Dashboard Design: Displaying data for at-a-glance monitoring, Second Edition, in hardback from Analytics Press.

New chapters have been added that focus on the following topics:

• Fundamental considerations while assessing requirements
• In-depth instruction in the design of bullet graphs
• In-depth instruction in the design of sparklines
• Critical steps that you should take during the design process

Examples of graphics and dashboards have been updated throughout the book and many new examples have been added, including a few more well-designed dashboards. In total, approximately 30% more content has been added to the book.

To give you a sense of this new edition, here’s the preface:

When I finished writing the first edition of this book in early 2006, I could not find a single example of a well-designed dashboard to illustrate the principles and practices that I advocate. Prior to this book, no specific guidelines for dashboard design existed. Not only did no good examples exist at the time, but no software could easily produce them. For example, in early 2006 no products supported data visualization expert Edward Tufte’s sparklines, which often work ideally on dashboards to provide an abbreviated view of history. No products supported bullet graphs either; shortly before this book was first published, I had introduced the bullet graph as a better alternative to typical dashboard gauges. I took a risk by writing a book that urged people to do what exceeded the capabilities of existing technology at the time. The risk paid off in that dashboard software has come a long way since then (although it still has a long way to go). However, had I worked within the boundaries of existing products at the time, the book would have not been worthwhile.

One complaint that I received about the first edition of this book was that it didn’t include enough examples of well-designed dashboards. Given the technological limitations that I’ve just described, I had to create, using Adobe Illustrator, the few good examples that appeared in the final chapter of the first edition. One of the main reasons that I’ve now written this second edition is to respond to this legitimate but unavoidable complaint by adding several more good examples, most of which were created by others.

In the years since 2006, another minor gap has developed between the book that I initially wrote and this second edition: the dashboard examples are somewhat dated. What’s surprising, however, is the fact that most of the dashboards that people create today using the latest technology are no better than their early predecessors. Almost every software vendor that claims to support dashboards features a hall of shameful examples on its website. I had a wealth of poorly designed dashboards to choose from vendor websites; I’ve included those examples throughout this new edition to illustrate bad but typical design practices.

Now that copies of the first edition are no longer available for purchase, Amazon.com is now accepting pre-orders for the new edition. I hope you find it useful. It has certainly been a labor of love.

Take care,

Article source: http://www.perceptualedge.com/blog/?p=1656

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« SAPPHIRE NOW: The Human Face of Big Data |

Posted by Timo Elliott on Monday, May 20, 2013 · Leave a Comment 

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At SAPPHIRE NOW and ASUG 2013,  Mantis Technology Group CEO Doug Turner and Senior Consultant Jim Egan gave a presentation explaining how they provide their customers with sophisticated, customizable “voice of the customer” solutions, based on SAP’s powerful text analysis technology.

Their product, Mantis Pulse Analytics, uses a combination of SAP BusinessObjects technologies to gather information from social feeds, analyze sentiment, and combine it with other corporate metrics to make the data actionable.

Voice of the Customer Analysis

There are lots of different sentiment analysis tools on the market, but many of them provide only shallow classification based on the presence of words like “bad” or “good” in a tweet.

Others, like SAP Social Media Analytics by Netbase, are very powerful and easy to use, but are not primarily designed to flexibly combine information from both internal and external data sources. Mantis uses SAP’s text analysis functionality to combine sentiment data with other information from the web, or from internal systems, to help make the data more actionable.

Originally acquired from a company called Inxight, SAP’s out-of-the box “entity extraction” technology breaks documents down into sentences, then phrases, then concepts, and then sentiment analysis is applied to these concepts.

Here’s an example analysis on customer survey data from a cruise ship company:

Different social sources present a spectrum of different opportunities. Emoticon and acronym-filled teenage tweets may produce little useful sentiment information for a brand, while Facebook posts or entries on a customer feedback page are more likely to provide useful information, and a full customer survey (“please tell us what else we can do…)” will likely result in highly qualified, very clean information. Individual tweets can be processed very quickly, while entire documents naturally take much longer.

Using Sentiment Data to Improve Business

The number one questions Mantis is asked is “what do I do with sentiment data?” — and the answer, of course, depends on the organization. It could be improving brand loyalty, customer service perception, or increase sales – the goal is to improve the metrics organizations currently use and take better advantage of information that is currently being ignored.

Customers are now starting to expect the same level of response from a tweet or a post to a Facebook page as a call to your customer service center. If the tweet is ignored, it’s like not picking up the call, with negative consequences to your brand image. The good news is that the most important signals are likely to be the clearest. If, as a consumer, I decide to purposely leverage social media to get the attention of a brand, then I’m likely to use direct, easily understood language.

Once the relevant information has been processed, it can be presented to customer service representatives in priority order:

The Mantis Pulse Analytics system lets users reply directly on social media, or integrate it into existing email-based service ticket systems:

Over time, analysis has become more sophisticated and customized to individual needs. Data is collected from more channels including custom sources such as web surveys and information about particularly influential social media users. Existing CRM customer records can be expanded to include social media profile information.

Time-based correlation analysis allows the comparison of sentiment with things like sales promotions. This helps make the information more actionable. The example below shows sentiment combined with Google Analytics web metrics to show the correlation and success of a marketing campaign by different social channels.

Other examples of Mantis Pulse reporting:

The Challenges of Sentiment Analysis

Examples of problems and issues encountered by Mantis (if you’re interested in the subject, you should read articles by Seth Grimes of Alta Plana, and see presentations from the Sentiment Analysis Symposium he organizes each year)

• If somebody says “I hated the install of the new version, but loved the new features,” the sentiments don’t cancel each other out, but have to be separated and ranked separately for the top positive and negative lists.
• A long list of problems followed by a single “resolved” may badly underestimate the real sentiment
• A consumer might wax poetically and enthusiastically about chocolate in general for five sentences, but then say “but I hate MMs” in two different ways. In this case, it would probably be more useful to calculate the ratio of positive to negative sentiment as 1 to 1 rather than 5 to 2.
• Many words can be interpreted as either a verb or a noun, and quality will be negatively effected by poor sentence structure, punctuation, or run-on sentences that combine several different unrelated concepts.
• Not all clear brand sentiment is very actionable. For example, one of Mantis’ customers had predictable spikes of drunken “I love Bud beer!” tweets on Friday and Saturday nights.
• The band “Big Bad Voodoo Daddy” generated negative sentiment, as did the tag line of a clothing campaign “dress irresponsibly”, so those are “tokenized” to take them out of the equation. In addition, specific fields and language  may be required for certain industries – a cruise ship is interested in recording the name of a ship, the cabin number, etc.
• Phrases had to be added that weren’t recognized such as “abend” (from “abnormal end” or crash)
• Some expletives have to be classed as positive sentiment, in phrases such as “just bought a guitar, it’s f****g awesome”
• Call centers exist in order to deal with problems, so rather than just classifying “problem” as negative, it’s important to distinguish between phrases such as “you solved my problem” and “I have a new problem.”

Mantis found that tuning the system for different customer meant adding specific custom dictionaries to the system. Rather than modifying the underlying text analysis “CGUL” files – which are the same for all the Mantis Pulse Analytics customers, and can generate unwanted side effects, since rules are based on other rules — Mantis used SAP Data Services to make appropriate upfront changes to the data.

In general, it’s important to realize that sentiment analysis can never be an exact science, given the complexity of human language – and much language is fundamentally ambiguous, with different people classifying sentiment in different ways. Manual evaluation of data samples by Mantis and their customers indicates an accuracy rate of around 80% compared to human interpretation of sentiment.

In particular, humans have the benefit of a greater amount of “context knowledge” that can be discerned just from the text itself  – which means there will always be false positives and negatives. This means that the results are much, much better than ignoring the data entirely, and can be used in aggregate to detect important trends, but have to be used with caution when deciding, for example, whether a customer service representative should keep his or her job.

The Underlying Technology

Mantis Pulse Analytics has existed for several years, in both on-premise and Amazon-cloud-based versions. Custom tools are used to collect the data, using standard social APIs and code to crawl specialized forms and blogs. Once the data is collected, it was put into a MySQL holding area, then the sentiment was extracted using SAP BusinessObjects Data Services Text Data Processing to create a data mart, with reporting using SAP BusinessObjects.

Mantis Pulse Analytics was rumored one of the largest BusinessObjects text analytics installations ever, with a Text Analytics Farm of 20-25 concurrent versions of the text analytics engine.

Customers wanted ever-more powerful analysis, ever faster, and Mantis turned to SAP HANA One running in the Amazon cloud, which now provides the same text analysis engines, but built directly into the platform, and processed in-memory.  The result is much faster, with fewer moving parts.

The data is collected directly in a SAP HANA table, and a full text index created, without any need for rollups or aggregate tables. Reporting is unchanged – except that sentiment analysis against million of documents now running in seconds.

Currently, all customer data is processed using the same configuration – in the future, the company may move to using multiple tables, each with their own customizable table index.

The SAP HANA-based solution currently provides a little less visibility into the text processing process than the previous solution, but this is more than made up for by the dramatic advantages when it comes to the reporting and analysis – “It’s coming to the state where the processing is at the speed of thought” says Doug Turner. “Our customers are trying to get to the point where they can respond to a tweet before a customer leaves a store”

If you’d like to know more about the Mantis Pulse Analytics solution, you can follow @mantispulse on twitter, or visit the web site.

There’s also a version of the solution designed specifically for sports organizations called Mantis Pulse Athletics, designed to “monitor, measure, and visualize social media and to reduce the risk of regulatory and organizational compliance issues”

Share and Enjoy

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Filed under All, Best practice, BI 2.0, Featured · Tagged with Analytics, BusinessObjects, Dashboards, HANA, SAP, SAP HANA, SAPHANA, Sentiment, Sentiment Analysis, Social, Social Analytics, Text, Text Analysis, Text Analytics, text processing, textanalytics, VOC, Voice of the customer

Article source: http://timoelliott.com/blog/2013/05/customized-voice-of-the-customer-analytics-using-sap-hana.html

Why Tableau Should Add a Gauge to Version 9

We’re wrapping up the Tableau 8 Roadshow (having now been shut out of two cities, including our own hometown of Denver, by airport weather cancellations). Tableau 8 is available and is enjoying rave reviews. And, while I was just getting around to fully digesting the old Tableau controversy about removing WikiLeaks visualizations, I just now heard about the new one that erupted when Stephen Few dissed Tableau about version 8. Despite my behind-the-time-ness, I simply must offer a contrast to Mr. Few’s thoughts.

There’s a place for visualization “experts.” Varying points of view are good. Educated opinions on visual best practices contribute to improved toolsets. But, can we all remember that there’s not any one person who knows all, or sees all, about any particular topic? My philosophy about “informed opinions,” including mine, is “Put this in your bucket of thoughts, shake or stir thoroughly, and benefit from the mix.” With this spirit of “mix of opinions” in mind, add the following to your bucket and shake it up.

Tableau should add a gauge mark type to Version 9.

We had an existing SAP BusinessObjects customer (Stephen Few’s never-ending scorn for this product is legendary) who approached us a while back inquiring about Tableau. “Our existing BI system has some issues. Some parts of it are slow and difficult to maintain. Can you give us an idea of where Tableau might improve this?” My reply was, “Sure… let me know where you have particular issues — where are your most painful areas?” But, before we could even begin to address these basic salient points, the prospect took it upon themselves to download a Tableau demo and begin to explore the product. The first follow-up was almost immediate; “How do I create a gauge in Tableau?”

I tried to move the customer back to the initial issues that had, theoretically, been the impetus for their initial inquiry. “Well, we can explore that. But, rather than just trying to mirror your current visuals, can we talk about where you have problems? Maybe there’s a better way, such as use of a bullet chart, to analyze those types of metrics. When can we talk?” The response, “Yeah, let me see when I can work a call into my schedule. But, for now, can you tell me how to create a gauge in Tableau?” I responded with a fairly extensive comparison of gauges versus bullet charts to analyze actual/goal data (this particular customer’s application of gauges). The next, final response: “Sorry, Tableau isn’t right for us.”

Yes, we can talk all day about this customer’s lack of insight, inability/unwillingness to look at anything other than “the way we’ve always done things,” and their refusal to sit down for even a basic discussion of their issues. In other words, this was a fairly normal situation (so, I’m admittedly now adopting the Stephen Few approach of “A few insults never hurt anybody”). In the final evaluation, Tableau’s lack of this ubiquitous mark type immediately prevented this (admittedly uninformed) prospect from discovering the beautiful, blazingly fast, tool that’s Tableau. Once they would have gotten over their gauge-itis, they too would have come on board.

Here’s the bottom line (expressed with an appropriate metaphor relating to New York Mayor Bloomberg’s recent come-uppance from a State Supreme Court): It’s not Tableau’s place to keep the 18 ounce sugary drink off the Mark Type Cafe menu. Visualization Seat Belts may help at the time of the Data Discovery collision, but Stephen Few simply doesn’t have the authority to mandate self-protection by all BI passengers.

Even if they choose to add the following confirmation dialog:

Article source: http://www.perceptualedge.com/blog/?p=1706

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Posted by Timo Elliott on Friday, May 17, 2013 · Leave a Comment 

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SAPPHIRE NOW in Orlando was packed with examples of how Big Data affects all our lives.

SAP Executive Steve Lucas was the host of an Intel-sponsored Big Data special interest group meeting at the conference. He kicked off the session by explaining that he considers himself to be an example of the human face of new data technologies. As a type 1 Diabetic, he wears a device that measures his blood sugar once a second, and communicates it to a hand-held device that he uses to constantly monitor his diet and take control of his health:

“For a diabetic, you literally are the living embodiment of data because you spend most of your spare time during your day checking and monitoring your blood sugar number.”

Rick Smolen, a former Time Life journalist and photographer, spoke at the event and during a panel the next day.

After working on various projects including the very successful “Day in the Life” series of books, Rick’s most recent book (and accompanying interactive iPad application) is called The Human Face of Big Data, and it’s full of compelling stories of how new data technologies are making a difference.

Rick explained that Marissa Meyer, now CEO of Yahoo, helped draw him to the project when she described technology as “helping the planet develop a nervous system.” In other words, by aggregating all of the sensors, searches and transactions in the world, it gives us a sense of what’s happening in the planet in real time.

In his forward to the book, Rick says:

“On the following pages, as you meet the men, women, and children whose lives are being transformed by this data revolution, I suspect you will come to the same conclusion that I have: Big Data may well turn out to be the most powerful tool set the human race has ever had to address the widespread challenges facing our species and our planet”

Rick believes that big data will change our lives – and not just in the business world. “Healthcare is at the top of the list” he explained. Today, as drug companies do clinical trials, they may find that it can help 99% of patients, but harm a small percentage, so could never get approved by the FDA. But the price of genome decoding is plummeting, holding out the promise of personalized medicine, prescriptions based on what is effective for your particular DNA. “Today, the cures exist, but they are sitting on the shelves” said Rick “today, antibiotics is one size fits all. The whole work is going to change thanks to medicine designed to treat the individual.”

Sensors will also result in new opportunities. “For example, 14 seconds before the recent massive earthquake in Japan, every bullet train and every factory came to a halt” said Rick. “Because the country had prepared a hardwired sensor system to detect the wave that comes before the violent one”.  That required a huge upfront capital investment. In silicon valley, a group of researchers realized that every modern laptop has sensors to protect their hard drives: if you drop the computer, it lifts the head off the platter to try to save the spinning disk. So they created the Quake Catcher Network, that allows crowd-sourced earthquake detection around the world. You download the free software to your computer and let it run in the background (it can be your default screensaver, for example). Then the software aggregates all the data and if one person’s laptop sensor is set off by, for example, a heavy truck going by, nothing happens, but if all the sensors in a 30 mile radius go off, then it’s an earthquake…

Sensors are also behind a new “magic carpet”: “My mother is 90 years old, and my father passed away six years ago. Six months ago, my mother fell, and then she fell another time, and the third time, nobody found her for five hours. We asked her to move in with us, but she didn’t want to. We hired people to live with her in shifts, and she hated it. Now GE and Intel are introducing products aimed at aging at home, and one of the prototypes is a carpet filled with sensors. Over time, it creates a baseline knowledge of ‘normal behavior’ – she walks on the carpet at 9:30 am, and here’s her gait – over time it can predict muscle weakness, and changes to normal patterns and tweet me to “call mom!”

Rick believes one of the biggest opportunities is making better use of previously ignored “dark data”. “For years, meteorologists have had to filter out ‘bioclutter’ from Doppler radar weather systems – the “noise” generated by flocks of birds or bats. But when bird researchers realized they had 15 years of invaluable data on migration patterns they were delighted!”

But Rick also cautioned that there will be new challenges. For example, today, data is typically owned by governments and businesses, not individuals, and there needs to be more thinking about how to make sure that powerful data is not misused.

For more fantastic examples and stories about Big Data is affecting people’s lives, visit http://bigdata.saphana.com/

Share and Enjoy

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Posted by Timo Elliott on Thursday, May 16, 2013 · Leave a Comment 

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Just before the SAPPHIRE NOW conference, SAP announced the Release to Customer (RTC) of SAP BusinessObjects BI 4.1, Edge BI 4.1 and Crystal Server 2013.

What’s New in BI 4.1

BI 4.1 is one suite for all insight, one place for all information and on standard for enterprise BI. Here are the highlights of the new release – or you can skip to the end of the post to get a detailed technical presentation of the new features.

The features in this latest release include:

• Unified and highly personalized platform with improved interoperability and usability across the BI suite
• Powerful visualizations that empower end users with industry focused visualizations and spatial analytics
• Big Data ready with support for Amazon Elastic MapReduce and Hadoop Hive. Improved access to Oracle’s Exadata, OLAP and Essbase. BI 4.1 also includes enhanced workflow capabilities in conjunction with SAP HANA and BW
• Professional grade BI platform for any size business, any type of deployment and for any role across the organization

BI 4.1 includes features for all five areas of SAP BI innovation

One Suite for All Insight

One Place for All Information

One Standard for Enterprise BI

Three key types of information access: self-service, dashboards and apps, and reporting

New Self-Service Features

New Dashboarding Features

New Reporting Features

New Platform Features

What’s different about this release?

Summary of Key Messages for Business Objects 4.1

One strategy for enterprise BI: one suite for all insight, one place for all information, one standard for enterprise BI.

One Suite For All Insight

Unified and highly personalized

• Improved interoperability and usability across the BI suite
• Deliver engaging information to users when and where they need it
• Create custom experiences with embedded analytic extensions

Powerful visualizations

• Monitor your business with specialized charting and geospatial analytics
• Empower end users with industry focused visualizations and spatial analytics
• Detect outliers and discover areas to optimize your business

Faster more accurate decisions

• Create and consume BI content from any mobile device
• Empower teams to deliver collaborative decisions
• Simplified deployment to accelerate time to value

One Place For All Information

Big data ready

• Support for Amazon Elastic MapReduce and Hadoop Hive
• Improved access to Oracle Exadata, Oracle OLAP and Oracle Essbase
• Enhanced workflow capabilities in conjunction with SAP HANA and BW

Scale and power

• Immediate response time regardless of data volume
• Improved usability to build pervasive BI applications
• Integrate with SAP monitoring, lifecycle management, search, and user management

Speed to deliver

• Enhanced design experience with new creation wizard and integrated data federation
• Improved Web Intelligence report design performance against BW
• Automated universe deployment and lifecycle management

One Standard For Enterprise BI

Professional grade BI platform

• For any size business, small to large
• For any type of deployment, on-premise or on-demand
• For anyone across the organization, individuals, teams, or department

Quality focused

• Increased efficiency to enhance decision making capability and productivity
• Non-disruptive production delivery and information governance across the organization
• Simplified architecture for increased IT responsiveness

Superior breadth of ecosystem

• Open and agnostic suite of products that can leverage existing BI investments
• Supported by global best practices for enhanced adoption
• Enable speedy deployment with shortened time to value

Key Links

• Getting Started with SAP BusinessObjects BI Solutions
• BI4 Upgrade Resources
• How to Select the Right BI Tool for Your Environment
• Sizing and Deploying SAP BusinessObjects BI4
• eLearning product tutorials
• Road Maps on SAP Service Marketplace
• SAP’s Release Strategy for Major Releases of SAP BusinessObjects BI

Full, Detailed Technical Feature Documentation/Presentation

Here’s the official feature-level what’s new manual for BusinessObjects 4.1, and a presentation by Ty Miller, delivered at the ASUG2013 / SAPPHIRENOW Conference

Share and Enjoy

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Article source: http://timoelliott.com/blog/2013/05/sap-businessobjects-bi-4-1-release-to-customers.html

« SAP BusinessObjects BI 4.1 Release to Customers |

Posted by Timo Elliott on Thursday, May 16, 2013 · Leave a Comment 

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As announced last Friday, SAP Visual Intelligence has been renamed to SAP Lumira (you can find the announcement and full FAQ here)

Why Did We Change Another Perfectly Good BI Product Name?

We know that this decision is likely to be met by some eye-rolling from our faithful customers who are still recovering from previous name changes.

The bottom line: we know that there are downsides to such a change, and despite the inevitable disruption it may cause, we feel it’s the right choice. Here are some of the key reasons that tipped the balance:

Family Ties. As we announced earlier this week, the next version of SAP Visual Intelligence (sorry, SAP Lumira) will offer tight integration with SAP’s next-generation cloud BI platform, SAP Lumira Cloud. We feel that it’s more appropriate to use a completely new name for a completely new “network of truth” approach to business intelligence and analytics (oh – and “SAP Visual Intelligence Cloud” is clunky).

Searching and Sharing. SAP Lumira is a more human-friendly yet Google-ready name. As with SAP HANA (which trips off the tongue more easily than “SAP’s new platform for real-time analytics and applications”), a distinctive name makes it easier to find relevant materials (it’s one of the reasons we took the space out of “Business Objects” many years ago). And with community becoming increasingly important, this change should ultimately make it easier to find the information you need to deploy and use the products successfully (but alas, no, this doesn’t mean that Xcelsius is coming back, even if the logic is the same.)

Destined for Greatness. Although BI has been around for a long time, we believe that analytic users are still a minority. SAP Lumira aims to change that by making it easier than ever to get powerful, self-service business intelligence into the hands of everyone. We have big plans for the product that we can’t quite talk about yet, but we believe it is destined for greatness, and it’s less painful to make the switch now than later.

SAPLumira New Features

• Enhanced data manipulation capabilities, with better merge functionality, replay manipulation steps improved formula editor
• Extended acquisition to more data sources support different data formats during acquisition
• Empower end users to customize charts , add conditional formatting and create simple user defined views without scripting or coding
• Annotate and share your results at a click of a button on-premise or on the cloud.

Please Give Feedback and Spread the Word

If you’re reading this, you’re probably one of the people that know and use our products, and we’re very grateful. We know that changing names is disruptive, not only for you but for the business people in your organization that use our products to get insight. We’re eager to help you make the change as smooth as possible, so please let us know what we can do to help by taking part in the discussions at the SCN area. And we’d like your help to get the word out about the changes to the rest of the world (#SAPVisi is dead! Long live #SAPLumira!).

Save a Bundle

To celebrate the change, you can take advantage a special SAPPHIRE NOW offer that slashes the regular prices by 90% – SAP Lumira Personal (for spreadsheet data) is only $9 and the full version is only $99. Hurry! The offer is only valid during SAPPHIRE NOW and ASUG 2013, May 13-May 16th, while stocks last!

To take advantage of the offer, go to SAP.com/buylumira and use the promotion code “sapphire”

Data Geek Challenge 2.0

Looking to do something with your new product? At SAPPHIRE NOW, we’re launching the next round of the Data Geek Challenge  at www.sap.com/datageek. To enter, create an interesting view of data using SAP Lumira desktop and your data or data we’ve provided and share with the SAP Data Geek (datageek@sap.com) via SAP Lumira Cloud.

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Article source: http://timoelliott.com/blog/2013/05/new-sap-lumira-see-the-light.html

Posted by Timo Elliott on Thursday, April 18, 2013 · 5 Comments 

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This is the second post based on a SAP-sponsored breakfast meeting organized in Sydney earlier this year, as part of a ANZ/APJ innovation analytics tour, with speaker Donald Feinberg, Gartner VP and Distinguished Analyst explaining the “Nexus of Forces”: social, mobile, cloud and information.

After covering why in-memory is disrupting everything, and why every organization will be running in-memory in 15 to 20 years time,

In this post, Donald explains the business impacts of the new in-memory computing possibilities, and in the next post, how to create an in-memory action plan.

These comments are based on my notes taken from the speech, formatted for legibility.

Business impact of in-memory computing

What is the impact of in-memory computing on your business? It’s about running the business, growing the business, and transforming the business, and you need to look at the business impact of this technology across all of these.

Run the business

One of the biggest advantages of memory that people forget is this: right now, you have lots of applications. And today, people typically have one application per server. Let’s say you have your corporate running on ten servers today, and it’s spread out across locations, because of storage access and the speed of the processors and the speed of the applications and the database access.

If I can consolidate that down to a single server, I‘m going to save a lot of money, right off the bat. Not only power, floor space, cooling, but replacement costs every three to four years for ten or twenty servers is more than one. It’s not necessarily a single server — it may be one or two — but it’s going to be much fewer.

The people required to maintain it are going to be fewer, your maintenance costs per year are going to be less, everything is less. So the speed of these in-memory technologies on just running your business – forget about transforming for a minute – is going to be a huge savings. Because if one applications runs a hundred times faster on a server, I can get more applications on that server.

When I said you you’re going to run your whole business in-memory in 10 or 15 years, I left off the fact that it’s going to be on a single server the size of what you think of as a desktop server, plugged into the wall with no special air conditioning needs. That’s the kind of miniaturization and speed that in-memory is bringing to the table, with huge savings.

I know many of you are saying “he’s not talking about high availability or disaster recovery”. All of that is coming — and it also is miniaturized. You’re not going to run your business on one of these, you’re going to run your business on two of them, sitting next to each other, duplicating everything it does, synchronously. That’s your high availability. Then you’ll put another one somewhere else, in somebody’s home, 250 or 800 kilometers away, and that’s your disaster recovery center. You hire a disaster recovery manager in Perth, and put the disaster recovery in his house — that’s the way it will be in the future.

Transform the business

The latency with in-memory is so low that you can do things synchronously that you wouldn’t have thought to do synchronously before. It’s not only a matter of how many things you can do, and how much you can fit into this box because of the speed, but it’s also because of what the latency is going to give you.

Why is that important? Think about where information and mobile and social come together, and you need to do messaging and things like that. Because of this lower latency, I can start to do things I couldn’t even consider before, because I couldn’t get it fast enough to even think about it.

How many of you may have applications that you thought about doing, but because things took so long on your system, it’s just not reasonable to do? I’m not talking about the demonstrable ones like if you’re in the manufacturing business, your MRP run takes four to six hours overnight, now you can run it in five seconds. So you can use the application differently.

And other things that you couldn’t do at all now become possible. As we start to do sentiment analysis, looking at social networks, and building it into a planning application that I’m running in seconds, that’s huge in the way you can change your business.

Think about if somebody says to you “I want to buy 10,000 cases” and you don’t even know if you can produce that. And then he says “I want it next week.”

How long does it take your company to commit to that, and to figure out a price, that may in fact be higher because I’m going to bounce other customers off the production line in order to get this done? If you can do that with a latency in seconds, it changes the way you do business.

That now is getting into “transform the business” because an application that you view as “a forecasting package that I run overnight” is not a just a forecasting package if I can run it in five seconds or two minutes. It becomes a sales tool, changing the way I’m doing business.

The example that I like to use is this: airlines want to sell you discount tickets. Most people don’t know that airlines re-price all the tickets on all their planes every night. So your company goes and buys a full-fare ticket because you need it refundable.

The next day, that flight may have two more discount tickets because they have a yield that they need for each plane, for each flight, so they can actually go through a whole calculation that tells them how many discount tickets they can have. Now, why is this valuable to them? Well, if you get on to, say, Qantas today and say “I want to go to Singapore and I want a discount ticket” and there are none on the day of the flight that you want, most of you wait until tomorrow to see if there are any, right?” Not true – most people don’t even know that happens. Instead, what you’re going to do is switch over to Singapore Airlines and if they have a ticket, you’re going to buy it and Qantas just lost the revenue.

But if Qantas could re-price every seat on every plane every time a ticket was sold, that business wouldn’t go away. If you had an application like that, which in-memory will allow you to do, and you went to the CEO of the airline and said “we have this application, do you want it?”, how much do you think they would be willing to pay? I’ll tell you — they won’t even ask how much it costs. That’s how much it transforms their business, and changes what they do. They’ll pay whatever you want.

In-memory computing technologies

So far, we’ve been talking just about in-memory DBMS. Here are some of the other ways the technology is used.

In-memory data grids have been around a long time. If any of you do web applications, you may be using some them. Memcached is the one that comes to mind – an open-source product – where your data’s in memory, in the application, and scales across multiple computers, multiple servers. That technology’s been around a long time and enables some of the biggest web applications that you’re all using, including Amazon, including eBay, and all the spinoffs of those.

High-performance messaging infrastructure. Think about what happens if you want to send a message out to four or five thousand of your customers at a time. It’s an SMS message or whatever, in-memory’s going to be able to do that much quicker.

Wouldn’t it be nice if you’re an airline, and you’re cancelling a flight, to get those messages out quickly? Or, in retail, if you’re going to have a special pricing discount, you’re going to send out to all the customers registered on your site, and you’re a big retailer with one hundred thousand or a million customers, think about how high-performance messaging is going to happen.

Complex event processing. That’s what fraud detection is all about, especially for cloned cell phones, for trading fraud, for credit-card fraud, for anything where some analysis is taking place on streaming data coming into a computer and in real-time. I make a decision on an event that’s happening, and then do something about it.

In-memory application servers. These are necessary if you’re going to do this consolidation onto a single or double box of all your applications. Your application servers have to be in-memory, and they can’t be based on disk drives, or they’re not going to run as fast as all the other technology that is enabled with the applications running in the application server.

All of these together make up “in-memory technologies”. The providers of this technology are going to merge together and all of this is going to become an in-memory megadata platform over the next three to five years. Data grids are going to go away and just become part of the in-memory database. These two will be the first to merge, and they’re merging already with in-memory analytic applications and application servers.

That’s the future, as they merge together, which will enable you to run your whole business in memory.

Drivers of in-memory computing

So what drives all this? Well, big data. Now remember “big data” is not just about volume. When we mention big data with respect to in-memory, people think we’re crazy, because big data is a lot of data, and people say “I’m not going to put a petabyte in memory: it’s too expensive!”

“Big Data” is volume (big size) and/or velocity(how fast the data’s coming in) and/or the variety of data(unstructured data). In-memory can support velocity today, that’s one the first use case of it, high-speed data coming in through event processing, smart metering, etc. And it can support unstructured data. As the price comes down, as compression gets better, it’ll also get start to get larger and larger on volume of data.

Real-time analytics. For years, Gartner has said there is no such thing as “real-time.” Today, you are running analytics on data that is coming from a transaction system. If I have to say it that way, there’s a latency there. Some ETL or data integration process has to move data from the transaction system to the data warehouse before you can do those analytics. The only way you can do real-time analytics is if it’s being done on the transaction data when it’s completed. So that is one of the drivers for this.

24×7 with no batch windows. If you batch window drops to less than zero, you’re going to have to run things very quickly. Batch is going away. That Materials Requirement Planning batch run that takes six hours? If it starts to run in 3-4 seconds, it’s really no longer batch.

So the whole concept of batch disappears with in-memory technology. Any time you see words like “awareness” then you’re talking about in-memory. In order to make any applications aware of things it means real time, and it means you need the speed and low latency of in-memory technology to do it.

Inhibitors of in-memory computing adoption

So what’s slowing us down?

A lot of these are perceptions. So the perception that it’s a complex architecture: it doesn’t have to be.

The perception that it’s unrealistic: today, this technology is emerging, and yes, it’s disruptive, but no, you can’t do everything with it. So the expectations have to be set right. There are of course no standards, there aren’t a lot of skills and there’s not a lot of best practices yet, because this is just emerging with those. That will happen over the next few years.

So yes, there are many drivers, but at the same time there are many inhibitors, a lot of which you can change by setting expectations and perceptions correctly. So you start to think about IT looking at all this data and saying “what do I do with it all?” and the bottom line is: if your assumptions are that you can’t do anything with it, you’re not going to do anything with it.

See the first post, on why in-memory changes everything, or next post in the series: Part 3, how to create an in-memory action plan. In addition, if you’re interested in hearing Donald Feinberg talk about this, a web seminar is available (registration required)]

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Article source: http://timoelliott.com/blog/2013/04/the-business-impact-of-in-memory-computing-from-run-to-transform.html

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Posted by Timo Elliott on Friday, April 19, 2013 · Leave a Comment 

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George-Edouard Dias, head of Digital Business for L’Oréal gave a great example of digital business transformation during his interview at SAPPHIRENOW Madrid at the end of last year. By combining analytics, social, mobile, and the cloud, the company aims to create new customer experiences.

Summary

L’Oréal’s big transformation is to have moved from marketing planned in advance to real-time marketing. The company wants to simplify the life of the customer: it has lots of data, in lots of different databases (customer purchases, unstructured data, data in the cloud, twitter feeds, Nielsen market trends, etc), and it wants to make sense of it in real-time.

L’Oréal needs tools that make decisions in real-time, to help resellers do their job better, and for customers who want direct access to that information. Mobility is also very important, in order to give consumers the information they need to at the “moment of truth” when they are looking at products on the shelf, such as the product reviews of “customers like me”, or what their Facebook friends think of it.

Interview

[some minor edits for legibility]

Joining me now is George-Edouard Dias, Digital Business at L’Oréal S.A. Welcome to you, thanks for being here today to talk about this transformation you’re doing at L’Oréal.

Thanks for inviting me.

Let’s talk about this digital transformation. What are you working on there?

I’m trying to really embark L’Oréal into this digital journey. For us, it’s three-fold. It’s really rethinking the way we communicate: a change, transformation of advertising, rethinking the way we sell to people — normally e-commerce, but also transforming retail — and trying to see how digital can help us to create new forms of business we’ve never thought about and new ways of interacting with the customer, which is such an important thing in the world today, where we need to conquer our next billion customers.

Where do you start with a project like this? It sounds like it’s a pretty massive scope. 

We’re starting with the observation. I like to take the position of the customer and to start to understand how L’Oréal can be a life-changer. What type of service you can bring to make his life as a customer easier? Where you can streamline the process?

If you think about what we’ve been doing for years, we’ve made beauty life more complex, because we have generated a lot of new products and amongst all these products, it’s difficult to realize which products fit me, which products I need, what is ideal for me. Could L’Oreal simplify my life? I have my beauty goals that I want to achieve, and you are the intermediary to make this happen.

Our goal is to curate all this data, all this information, coming from trends, coming from stores, coming from what we know of the customers habits, what we know about the customer himself. Curate that information, so it makes sense to the customer and we surface it, give them the information they need to take wise decisions and to live their beauty life better.

That sounds like a lot of data points, coming in from all different angles – how do you merge those, how do you keep those all together and make that end product simpler for the end consumer?

Well I think it’s really something that’s a real journey, because we have tons of data. We have, in fact, islands of data, in a lot of different databases, and we want to make sense of this data in real-time. The big transformation, the big change that has happened to marketing these days, is that we’ve moved from a marketing  where we plan in advance – everybody was talking about two-year, three-year, even five year plans – to something which we call real-time marketing.

If marketing is about real-time decision making, so you need to have a tool to make that decision in real-time, a decision that makes sense. So you need to make sense of all these sources of data – unstructured, data on the cloud, twitter feeds, what Nielsen says about the evolution of the market, what I see in the store, the purchases of the customer, the way a product is selling well or not.

I need to sort that information out, and make it make sense, for me as an entrepreneur, as a business owner, but also for my retailer, because I can also help my retailer to do his job better, to serve the customer, and also to my customer, because the customer wants also to have a direct access to that information right now, and that’s where mobility comes to play.

That goes into my next question. Where have you seen the biggest impact when it comes to technologies, on the customer. Is it mobile, is it the web site, is it advertising?

It’s the one billion smartphones that exist in the world today. Out of six billion cellphone subscriptions worldwide, there are one billion cell phones that are really smart phones. Smart phones mean a very big difference, because it means that we’ve moved from a world where you’re calling and talking to people to a world where you’re pulling data from data sources that is to your benefit.

When you are at this point of decision for a customer, that we call the real moment of truth, where you’re in front of these big shelves and you wonder which product you need, you can pull the data out of our database, out of a retailer’s database, so you make an informed purchase. We help the customer to take that decision at the moment of truth, by really giving them the right information that he needs.

So, they see this big shelf, with all kinds of products on it, but realistically, if they’re the best customer you have, and you’re doing your job right, the one product that they need the most, they’ll find that quickly and easily.

I always say that we should find the way that product will recognize the customer, not the reverse. And if I can have a tool where I’m just using my camera in my smartphone, and I’m using some form of augmented reality where the product I need is really highlighted it so I can purchase it, that’s exactly what I need. I need technology to make my life simpler. And the way we use technology at L’Oreal is to make the beauty life of our customers simpler, and we think that mobile is certainly the best way – because one thing I’m sure my customers care about, at all times, especially when shopping, traveling, moving around, is the cell phone, because it’s a personal tool, and it carries a lot of personal information, and it can interact with the ecosystem of information which exists in the store and give me the precise information that I need to make my informed purchase.

Can we see that as a possible application for mobile from L’Oréal in the future?

We’re already been working on this. We have a lot of different applications that we’re putting to the market to try to empower the customer at this moment of decision.

The first thing that we did was, you know on each of the products there’s some sort of code that you can read, so we placed some sort of 3D code or the traditional barcode so that you can read it with your smartphone, and you can access the customer reviews. And for example, why is it important to access customer reviews? Because you know whether this product has been appreciated or not by customers, and it’s not only customer review of all customers, but it’s also you can access the customers that look like you, or have exactly the same problem as you have, the same expectations that you have — or people that are your Facebook friends. So, we’re also going in the direction where we empower the customer with more targeted information. So it’s a way to progress.

So it sounds like technology is definitely making things very exciting for you and what you’re embarking on at L’Oréal. Thank you so much for joining us.

Interested in hearing more? Come to SAPPHIRE NOW / ASUG 2013 in Orlando to see how other organizations are using information and analytics to reinvent the customer experience, with products such as SAP Precision Retailing.

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Filed under All, Featured · Tagged with Analytics, Augmented Reality, BI, Dashboards, L’Oreal, Mobile, Precision Retailing, Retail, SAP, SAPPHIRE, SAPPHIRE NOW, SAPPHIRENOW, Social

Article source: http://timoelliott.com/blog/2013/04/loreal-turning-data-into-a-customer-experience.html

Filed under All, Media · Tagged with Analytics, Cartoon, Collaboration, Data, Humor, Keynote, Mobile, Social

Article source: http://timoelliott.com/blog/2013/04/cartoon-coming-to-a-keynote-near-you.html