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Here is an interview with Jason Kuo who works with SAP Analytics as Group Solutions Marketing Manager. Jason answers questions on SAP Analytics and it’s increasing involvement with R statistical language.
Ajay- What made you choose R as the language to tie important parts of your technology platform like HANA and SAP Predictive Analysis. Did you consider other languages like Julia or Python.
Jason- It’s the most popular. Over 50% of the statisticians and data analysts use R. With 3,500+ algorithms its arguably the most comprehensive statistical analysis language. That said,we are not closing the door on others.
Ajay- When did you first start getting interested in R as an analytics platform?
Jason- SAP has been tracking R for 5+ years. With R’s explosive growth over the last year or two, it made sense for us to dramatically increase our investment in R.
Ajay- Can we expect SAP to give back to the R community like Google and Revolution Analytics does- by sponsoring Package development or sponsoring user meets and conferences?
Will we see SAP’s R HANA package in this year’s R conference User 2012 in Nashville
Jason- Yes. We plan to provide a specific driver for HANA tables for input of the data to native R. This planned for end of 2012. We’ll then review our event strategy. SAP has been a sponsor of Predictive Analytics World for several years and was indeed a founding sponsor. We may be attending the year’s R conference in Nashville.
Ajay- What has been some of the initial customer feedback to your analytics expansion and offerings.
Jason- We have completed two very successful Pilots of the R Integration for HANA with two of SAP’s largest customers.
Jason has over 15 years of BI and Data Warehousing industry experience. Having worked at Oracle, Business Objects, and now SAP, Jason has been involved in numerous technical marketing roles involving performance management dashboards, information management, text analysis, predictive analytics, and now big data. He has a bachelor’s of science in operations research from the University of Michigan.
Here is a brief interview with Alvaro Tejada Galindo aka Blag who is a developer working with SAP Hana and R at SAP Labs, Montreal. SAP Hana is SAP’s latest offering in BI , it’s also a database and a computing environment , and using R and HANA together on the cloud can give major productivity gains in terms of both speed and analytical ability, as per preliminary use cases.
Ajay- What made the R language a fit for SAP HANA. Did you consider other languages? What is your view on Julia/Python/SPSS/SAS/Matlab languages
Blag- I think “R” is a must for SAP HANA. As the fastest database in the market, we needed a language that could help us shape the data in the best possible way. “R” filled that purpose very well. Right now, “R” is not the only language as “L” can be used as well (
) …not forgetting “SQLScript” which is our own version of SQL (
) . I have to admit that I tried Julia, but couldn’t manage to make it work. Regarding Python, it’s an interesting question as I’m going to blog about Python and SAP HANA soon. About Matlab, SPSS and SAS I haven’t used them, so I got nothing to say there.
Ajay- What is your view on some of the limitations of R that can be overcome with using it with SAP HANA.
Blag- I think mostly the ability of SAP HANA to work with big data. Again, SAP HANA and “R” can work very nicely together and achieve things that weren’t possible before.
Ajay- Have you considered other vendors of R including working with RStudio, Revolution Analytics, and even Oracle R Enterprise.
Blag- I’m not really part of the SAP HANA or the R groups inside SAP, so I can’t really comment on that. I can only say that I use RStudio every time I need to do something with R. Regarding Oracle…I don’t think so…but they can use any of our products whenever they want.
Ajay- Do you have a case study on an actual usage of R with SAP HANA that led to great results.
Blag- Right now the use of “R” and SAP HANA is very preliminary, I don’t think many people has start working on it…but as an example that it works, you can check this awesome blog entry from my friend Jitender Aswani “Big Data, R and HANA: Analyze 200 Million Data Points and Later Visualize Using Google Maps “ (
Ajay- Does your group in SAP plan to give to the R ecosystem by attending conferences like UseR 2012, sponsoring meets, or package development etc
Blag- My group is in charge of everything developers, so sure, we’re planning to get more in touch with R developers and their ecosystem. Not sure how we’re going to deal with it, but at least I’m going to get myself involved in the Montreal R Group.
|Name:||Alvaro Tejada Galindo|
|Company:||SAP Canada Labs-Montreal|
|Instant Messaging Type:|
|Instant Messaging ID:||Blag|
|Professional Blog URL:||
|My Relation to SAP:||employee|
|Short Bio:||Development Expert for the Technology Innovation and Developer Experience team.Used to be an ABAP Consultant for the last 11 years. Addicted to programming since 1997.|
SAP HANA is SAP AG’s implementation of in-memory database technology. There are four components within the software group:
- SAP HANA DB (or HANA DB) refers to the database technology itself,
- SAP HANA Studio refers to the suite of tools provided by SAP for modeling,
- SAP HANA Appliance refers to HANA DB as delivered on partner certified hardware (see below) as anappliance. It also includes the modeling tools from HANA Studio as well replication and data transformation tools to move data into HANA DB,
- SAP HANA Application Cloud refers to the cloud based infrastructure for delivery of applications (typically existing SAP applications rewritten to run on HANA).
R is integrated in HANA DB via TCP/IP. HANA uses SQL-SHM, a shared memory-based data exchange to incorporate R’s vertical data structure. HANA also introduces R scripts equivalent to native database operations like join or aggregation. HANA developers can write R scripts in SQL and the types are automatically converted in HANA. R scripts can be invoked with HANA tables as both input and output in the SQLScript. R environments need to be deployed to use R within SQLScript
More blog posts on using SAP and R togetherDealing with R and HANA
HANA meets R
When SAP HANA met R – First kiss
Using RODBC with SAP HANA DB-
SAP HANA: My experiences on using SAP HANA with R
and of course the blog that started it all-
When I was doing my MBA (a decade ago), one of the principal theories on why corporations exist was 1) Shareholder Value creation (grow wealth for investors) and a notable second was 2) Stakeholder Value creation- creating jobs for societies, providing tax to countries, providing employees with stable employment and incentives, and of course creating monetary value for shareholders.
There were two ways you could raise money- debt or equity. Debt had the advantage of interest payments being tax deductible. Debt payments had to be met regularly. Equity had the advantage that equity holders were the last ones to be paid in case of closing the company down, which justified that rate of return on equity is generally higher than cost of debt. Dividend payouts to stockholders could be deferred in a low revenue year or due to planning reasons.
Or in plain English, over the long term borrowing money from share holders in lieu of stocks was more expensive than selling bonds or borrowing from the banks.
Hybrid combinations of debt and equity were warrants and debentures that started off as one form of instrument and over a period of time gave much more flexibility and risk safety nets to both issuers and subscribers of capital. Another hybrid was stock options (now considered as a default option of rewarding employees in technology companies, but this was not always the case).
The use of call and put options in debentures, and the idea of vesting period in stock options was to promote lone term stability and minimize fluctuations in stock prices, employee attrition, besides of course to minimize the weighted average cost of capital. Venture capital was another class of capital known for both huge rates of return and risk taking (?)
But in today’s world where a Google has three classes of shares, companies trade shares before IPOs, and valuations of technology companies sink and rise by huge % over weeks (especially as they near IPO dates)- I wonder if traditional theories in finance need a much stronger overhaul.
or do markets need a regulatory overhaul, that would enable stock exchanges to have once more the credibility they had as the primary sources of raising capital.
Who will guard the guardians? Their conscience- the regulators or the news media?
There are ways of raising money that are not evil.
But they are not perfectly fair as well.
Here is an interview with Hjálmar Gíslason, CEO of Datamarket.com . DataMarket is an active marketplace for structured data and statistics. Through powerful search and visual data exploration, DataMarket connects data seekers with data providers.
HG- DataMarket is my fourth tech start-up since at age 20 in 1996. The previous ones have been in gaming, mobile and web search. I come from a technical background but have been moving more and more to the business side over the years. I can still prototype, but I hope there isn’t a single line of my code in production!
Funny you should ask about the 10 things that have surprised me the most on this journey, as I gave a presentation – literally yesterday – titled: “9 things nobody told me about the start-up business”
* Do NOT generalize – especially not to begin with
* Prioritize – and ﬁnd a work-ﬂow that works for you
* Meet people – face to face
* You are a sales person – whether you like it or not
* Technology is not a product – it’s the entire experience
* Sell the current version – no matter how amazing the next one is
* Learn from mistakes – preferably others’
* Pick the right people – good people is not enough
* Tell a good story – but don’t make them up
I obviously elaborate on each of these points in the talk, but the points illustrate roughly some of the things I believe I’ve learned … so far
Both Amazon and Google have entered the public datasets space. Infochimps has 14,000+ public datasets. The US has http://www.data.gov/
So clearly the space is both competitive and yet the demand for public data repositories is clearly under served still.
How does DataMarket intend to address this market in a unique way to differentiate itself from others.
HG- DataMarket is about delivering business data to decision makers. We help data seekers find the data they need for planning and informed decision making, and data publishers reaching this audience. DataMarket.com is the meeting point, where data seekers can come to find the best available data, and data publishers can make their data available whether for free or for a fee. We’ve populated the site with a wealth of data from public sources such as the UN, Eurostat, World Bank, IMF and others, but there is also premium data that is only available to those that subscribe to and pay for the access. For example we resell the entire data offering from the EIU (Economist Intelligence Unit) (link:
DataMarket.com allows all this data to be searched, visualized, compared and downloaded in a single place in a standard, unified manner.
We see many of these efforts not as competition, but as valuable potential sources of data for our offering, while others may be competing with parts of our proposition, such as easy access to the public data sets.
Ajay- What are your views on data confidentiality and access to data owned by Governments funded by tax payer money.
HG- My views are very simple: Any data that is gathered or created for taxpayers’ money should be open and free of charge unless higher priorities such as privacy or national security indicate otherwise.
Reflecting that, any data that is originally open and free of charge is still open and free of charge on DataMarket.com, just easier to find and work with.
HG- The scene is quite vibrant, given the small community. Good teams with promising concepts have been able to get the funding they need to get started and test their footing internationally. When the rapid growth phase is reached outside funding may still be needed.
There are positive and negative things about any location. Among the good things about Iceland from the stand point of a technology start-up are highly skilled tech people and a relatively simple corporate environment. Among the bad things are a tiny local market, lack of skills in international sales and marketing and capital controls that were put in place after the crash of the Icelandic economy in 2008.
I’ve jokingly said that if a company is hot in the eyes of VCs it would get funding even if it was located in the jungles of Congo, while if they’re only lukewarm towards you, they will be looking for any excuse not to invest. Location can certainly be one of them, and in that case being close to the investor communities – physically – can be very important.
We’re opening up our sales and marketing offices in Boston as we speak. Not to be close to investors though, but to be close to our market and current customers.
Ajay- Describe your hobbies when you are not founding amazing tech startups.
HG- Most of my time is spent working – which happens to by my number one hobby.
It is still important to step away from it all every now and then to see things in perspective and come back with a clear mind.
I *love* traveling to exotic places. Me and my wife have done quite a lot of traveling in Africa and S-America: safari, scuba diving, skiing, enjoying nature. When at home I try to do some sports activities 3-4 times a week at least, and – recently – play with my now 8 month old son as much as I can.
Hjálmar Gíslason, Founder and CEO: Hjalmar is a successful entrepreneur, founder of three startups in the gaming, mobile and web sectors since 1996. Prior to launching DataMarket, Hjalmar worked on new media and business development for companies in the Skipti Group (owners of Iceland Telecom) after their acquisition of his search startup – Spurl. Hjalmar offers a mix of business, strategy and technical expertise. DataMarket is based largely on his vision of the need for a global exchange for structured data.
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