Day: February 8, 2018

This event was yesterday and here are some quick insights, similar to the analysis of SAP Inside Track Istanbul. I used a combination of Lumira and SAP Analytics Cloud.  What would the top topic be?  Top tweet?  See below:

So top counts include the SAP Inside Track twitter account and the SAP Mentors, along with community members.  Good to see Jakob Flaman who organizes SAP Inside Track Bern, make this list.

The text analysis shows the social media topics were tweeted the most, unlike SAP Inside Track Istanbul where it was twitter ID’s.

It is no surprise that the top topic was different versions of #sitWDF, the event hashtag.  Some attendees Periscoped the session, and interesting that ABAP was a topic topic (“ABAP is not dead”, so they say)

Top mentions in social media text analysis were SAP Mentors, SAP Inside Track, Jitendra Kumar Kansal Katarina Fischer and the SAP Community

Tag cloud shows a mix of twitter ID’s and topics, including Sven Denecken

The top language in tweets was English, with German second, and Turkish third.

Top tweeted time zone was Berlin (no surprise).  London is the timezone for SAP Inside Track twitter account, and Pacific is the SAP Mentor’s twitter account.  Bern is likely from Jakob Flaman.  Last year Amsterdam was in the top but not this year.

Yesterday, the day of the event, had the most tweets (no surprise).  Yesterday’s count at 809 is slightly lower than last year’s 875.

This one is always interesting; I haven’t heard of “Flamingo for Android”

For fun, I also used SAP Analytics Cloud – it gave a preview on topics/IDs from the Text Analysis

I like the feature “build my story” and it “knows” what I want to see.

This is hard to see but the Smart Discovery feature analyzes “retweets”.

On the iPad, the images are crisp using SAP Analytics Cloud.

Above is the image on the iPhone for SAP Analytics Cloud – the image is very easy to read.

The top tweet, at 20 retweets, is from Hendrik Neumann :

Usually the top tweet is the group picture, but there was a special announcement yesterday:

On the left, SAP’s Thomas Grassl with Malin Liden (video by Katarina Fischer  – I recommend SAP Community members watch this.

Top “favorited” tweet, with 47, is from Svea Becker 

What did I miss?

Upcoming Items of Interest

You can learn more Lumira here:

BI2018

BI+Analytics Conference

Upcoming ASUG BI Webcasts

and more about SAP Inside Tracks here

  http://bit.ly/2BeeqH7 #SAP #SAPCloud #AI

Pre-configured SAP and IBM industry accelerators for SAP S/4HANA pave the way to digital transformation.

IBM and SAP have been working together to drive the modernization of clients’ systems and processes to thrive in today’s global economy. Since our digital transformation partnership announcement in 2016, our companies have been working together to offer businesses of all sizes a clear roadmap to innovate new business models, products, and processes to improve outcomes.

As we collaborate closely with our customers, we have seen many of the challenges posed by their existing systems and IT infrastructures that weren’t built for the constantly changing requirements of a data-driven business environment. As customers are seeking to implement next-generation platforms capable of meeting the demands of modern-day business, the need for additional tools has become clear.

To help businesses keep up with increasingly rapid change, and accelerate the pace of their digital transformations, IBM and SAP are collaborating to expand SAP Model Company offerings with IBM’s Industry Impact solutions. With IBM as the first SAP Partner to leverage the SAP Model Company framework, we are working together to offer the market a complementary set of capabilities helping to simplify and speed outcomes for clients that want to become AI-enabled enterprises.

The breadth and depth of the SAP and IBM industry accelerators, combined with the strength of SAP and IBM’s consulting practices, gives customers a major head start on their SAP S/4HANA projects and provide clients the ability to realize value from their deployments with even greater speed and agility as they pave the way to digital transformation.

Early adopters of this combined approach are already seeing significant benefits, including exponentially greater process coverage and, according to IBM research estimates, from 20 to 30% increases in implementation speed from the use of pre-configured industry solutions.*

By offering combined complementary sets of capabilities and solutions, customers can expect greater value through enhanced customer and user experiences and industry-specific functionality— all enabled with SAP S/4HANA, available on premise and in the cloud.

For example, this summer, our companies jointly announced the first innovative offering for the retail and consumer packaged goods industries aimed to help businesses increase profitability and improve the consumer experience by responding with speed, precision and confidence leveraging hyper-local data and AI. This capability is integrated into IBM’s Impact Industry solution and will now be a part of our complementary Food and Beverage solution.

IBM currently delivers accelerators for industries including retail, consumer products, food and beverage, auto, life sciences, industrial products (IM&C), high-tech/electronics, wholesale distribution, utilities, A&D, oil and gas, chemicals and public sector. On average, the IBM Impact solutions extend SAP Model Company’s strong foundation and rich industry content with more than 100 additional pre-configured processes to each industry accelerator. In addition, IBM offers a deeply enriched set of data conversion, testing, and training accelerators to compliment the embedded processes. That means more industries making speedier transformations, and more businesses empowered to embrace the new data-driven world.

For more information on this partnership, visit www.ibm-sap.com/digital_transformation. Let us show you how IBM and SAP industry solutions can help jump start your S/4HANA journey.

Sign up today for an SAP S/4HANA Impact Assessment.

Harald Martin is senior vice president of Partner Strategy & Business Development, Digital Business Services, SAP
Keith Costello is global vice president and general manager, SAP Global Business Services Leader, IBM

*Based on analysis of project estimates and actuals by IBM’s Impact Project Estimation Team http://bit.ly/2sjgJ93 #SAP #SAPCloud #AI

Labels are how humans define and categorise different concepts. There’s lots of evolutionary psychology, neuroscience and linguistics behind this, but without going into that, without labels human (and other animal) intelligence would not be possible. Labels are the algebra of everyday life.

But what’s that got to do with Data Science? As it happens, quite a lot. When we want to understand what people believe or perceive, we do it by analysing their communication either written or spoken. Let’s say we’re wanting to analyse voice of customer text data.

The classical way to approach this is text mining based on keywords and rules to drive topic analysis e.g. using TFIDF or some other kind of ‘vectorization’, and sentiment analysis of the opinion terms.

There are issues here. Firstly, what are we supposed to do with all the topics? If we build a word cloud how useful is that? If they use synonyms which aren’t in a dictionary, do we group these together in advance? We are essentially trying to second-guess and group terms, which might not match the intentions of the customers, or be different for different situations. Things for sentiment are even more dissonant and we haven’t begun to explore the technical challenges with sarcasm, context, comparators and double negatives which all perform very poorly in such analyses.

So how else are we meant to analyse text data, apart from painfully compiling dictionaries and constant manual checking? Well, say hello to the wonderful world of labels. The labels being referred here are generated from machine learning i.e. by replicating human judgment based on a training sample of manually labelled data. The machine doesn’t need to be told keywords, it figures out common patterns which might be a lot more than single keywords, and might include where they are in the sentence and whether they are nouns or verbs, just as a human might.

So, if labels are so great, why isn’t everyone using them? Well the short answer is that it’s expensive. It’s expensive in terms of time because someone with the requisite domain knowledge needs to generate the labels, and it’s even more expensive because a data scientist needs to use those labels to try to generate a signal using various techniques without resorting to ‘data torture’ (i.e. the phenomenon of eventually getting out of a dataset what you wanted, even if not scientifically justifiable). The problem and approach need to be carefully defined, the data cleansed, parsed and filtered to suit the approach, and frankly a great deal of trial and error. Even if a predictive model is generated, it needs to be tuned, tested for stability and then checked and curated carefully over time in case the data and performance changes (and they always do in anything interesting!). This explains why labelling from a machine learning point of view is precious and only used sparingly for the highest-value use cases.

Thankfully this no longer needs to be the case thanks to the latest technologies. Imagine a world where AI-based labelling is cheap and plentiful, where data scientists are not required to tune and drive models.

The basic premise is that the labelling machine judges its own uncertainty and invites user intervention to label things manually that it needs to maximise its performance for the minimum human intervention. The human intervention just needs to be someone with domain knowledge and doesn’t need to be a data scientist, and the labelling required is ‘just enough’ to achieve the requisite business performance. No data artistry needed. Also, because it invites human intervention when there’s uncertainty, it can spot new topics i.e. ‘early warning’ of new signals, and keep the models maintained to their requisite performance. If there are differences in labelling, labels can be merged or moved around in hierarchies. If the performance at the granular level isn’t high then it will chose the coarser level just as a human might.

To spell out the potential savings, suppose a business wants to automate its complaint handling system by building a predictive model of categories for queries (i.e. labels). There might be hundreds of categories and, as a data scientist, you might ask for an estimate of the initial labelling set which could take many man-weeks, with the possibility of not actually finding a signal. Then there’s feature engineering, in itself an iterative activity with no guarantees. If all this takes 6 weeks of labelling, then the latest technology might be typically 2% of that i.e. just over half a day to achieve the same performance. Any time spent in feature engineering is also massively reduced as you rapidly test and tune with more certainty and a much quicker feedback loop. Furthermore, the time spent curating models disappears from the data science team and is instead replaced by the minimal amount of labelling when new or ambiguous signals appear. This might be a day or so per year rather than a heavy overhead. You can quickly see that a model that might cost many hundreds of thousands per year of human input might literally only cost a few thousand instead, and be more flexible and powerful in terms of early warning and adaptability.

So now you can see that labels are indeed very powerful in a machine learning context. They move text analytics to the next level and now there are technologies which lower the time, cost and technical skill levels to deploying them. What will you label? https://goo.gl/zppNVh #DataScience #Cloud