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A-Team DMS Awards 2014 - Xenomorph on the Cloud

A-Team’s DMS Data Management Awards close on the 26th of September so if you haven't already, please vote for Xenomorph!

Xenomorph on the Cloud - First of a few lookbacks at what we have been doing over the past year - firstly with a short animation about one of our major initiatives this year, cloud provision of data management and a new venture into cloud-based data publishing with the TimeScape MarketPlace

So it would be fantastic if you could support Xenomorph by voting here

Thank you!

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Posted by Brian Sentance | 11 September 2014 | 7:21 pm

A-Team DMS Data Management Awards 2014

Very pleased to announce that we have been nominated again this year in the A-Team’s DMS Data Management Awards. The categories we’ve been selected for are: 

  • Best Sell-Side Enterprise Data Management Platform
  • Best Buy-Side EDM Platform
  • Best EDM Platform (Portfolio Pricing & Valuations)
  • Best Risk Data Aggregation Platform
  • Best Analytics Platform.

Last year we were delighted to win the Best Risk Data Management/Analytics Platform award – even more so as the awards are voted for by our clients and industry peers.

So if you would like to support us again this year the voting is open now:


and runs through to the 26th September. The award winners will be announced at A-Team’s Data Management Summit, at the America Square Conference Centre in London on October 8th.

Posted by Kerry Johnson | 5 August 2014 | 12:09 pm

NoSQL Document Database - Manhattan MarkLogic

Bit late in posting this up, but given I did something about RainStor I thought I should write up my attendance at a MarkLogic event day in downtown Manhattan from several weeks back - their NoSQL database is used to serve up content on the bbc web site if you wanted some context. They are unusual for the NoSQL “movement” in that they are a proprietary vendor in a space that is dominated by open source databases and the companies that offer support for them. The database they most seem to compete with in the NoSQL space seems to be MongoDB, where both have origins as “document databases” (- managing millions of documents is one of the most popular uses for big data technology at the moment, though not so much publicized as more fashionable things like swallowing a twitter feed for sentiment analysis for example).

In order to cope with the workloads needing to be applied to data, MarkLogic argue that data has escaped from the data centre in terms of need separate data warehouses and ETL processes aligned with each silo of the business. They put forward the marketing message that MarkLogic allows the data to come back into the data center given it can be a single platform for where all data lives and all workloads applied to it. As such it is easy to apply proper data governance if the data is in one place rather than distributed across different databases, systems and tools.

Apparently MarkLogic started out with the aims of offering enterprise search of corporate data content but has evolved much beyond just document management. Gary Bloom, their CEO, described the MarkLogic platform as the combination of:

• Database
• Search Engine
• Application Services

He said that the platform is not just the database but particularly search and database together, aligned with the aim of not just storing data and documents but with the aim of getting insights out of the data. Gary also mentioned the increasing importance of elastic compute and MarkLogic has been designed to offer this capability to spin up and down with usage, integrating with and using the latest in cloud, Hadoop and Intel processors.

Apparently one of the large European investment banks is trying to integrate all of their systems for post-trade analysis and regulatory reporting. The bank apparently tried doing this by adopting a standard relational data model but faced two problems in that 1) the relational databases were not standard and 2) that it was difficult to get to and manage an overarching relational schema. On the schema side of things, the main problem they were alluding to seemed to be one schema changing and having to propagate that through the whole architecture. The bank seems now to be having more success now that they have switched to MarkLogic for doing this post-trade analysis – from a later presentation seems like things like trades are taken directly from the Enterprise Service Bus so saving the data in the message as is (schema-less).

One thing that came up time and time again was their pitch that MarkLogic is “the only Enterprise NoSQL database” with high availability, transactional support (ACID) and security built in. He criticized other NoSQL databases for offering “eventual consistency” and said that they aspire to something better than that (to put it mildly). I thought it was interesting over a lunch chat that one of MarkLogic guys said that "MongoDB does a lot of great pre-sales for MarkLogic" meaning I guess that MongoDB is the marketing "poster child" of NoSQL document databases so they get the early leads, but as the client widens the search they find that only MarkLogic is "enterprise" capable. You can bet that the MongoDB team disagree (and indeed they do...).

On the consistency side, Gary talked about “ObamaCare” aka HealthCare.gov that MarkLogic were involved in. First came some performance figures of how they were handling 50,000 transactions/sec with 4-5ms response time for 150,000 concurrent users. This project suffered from a lot of technical problems which really came down to problems of running the system based on a fragile infrastructure with weaknesses in network, servers and storage. Gary said that the government technologists were expecting data consistency problems when things like the network went down, but the MarkLogic database is ACID and all that was needed was to restart the servers once the infrastructure was ready. Gary also mentioned that he spent 14 years working at Oracle (as a lot of the MarkLogic folks seem to have) but it was only really until Oracle 7 that they could really say they offered data consistency.

On security, again there was more criticism of other NoSQL database for offering access to either all of the data or none of it. The analogy used was one of going to an ATM and being offered access to everyone’s money and having to trust each client to only take their own. Continuing the NoSQL criticism, Gary said that he did not like the premise put around that “NoSQL is defined by Open Source” – his argument was that MarkLogic generates more revenue than all the other NoSQL databases on the market. Gary said that one client said that they hosted a “lake of data” in Hadoop but said that Hadoop was a great distributed file system but still needs a database to go with it.

Gary then talked about some of the features of MarkLogic 7, their current release. In particular that MarkLogic 7 offered scale out elasticity but with full ACID support (apparently achieving one should make it not possible to achieve the other), high performance and a flexible schema-less architecture. Gary implied that the marketing emphasis had changed recently from “big data” pitch of a few years back to include both unstructured and structured data but within one platform, so dealing with heterogeneous data which is a core capability of MarkLogic. Other features mentioned were support for XML, JSON and access through a Rest API. Usage of MarkLogic as a semantic database (a triple store) and support for the semantic query language Sparql. Gary mentioned that semantic technology was a big area of growth for them. He also mentioned support for tiered stored on HDFS.

The conversation them moved on to what’s next with version 8 of Mark Logic. The main thing is “Ease of Use” for the next release with the following features:

• MarkLogic Developer – freely downloadable version
• MarkLogic Essential Enterprise – try it for 99c/hour on AWS
• MarkLogic Global Enterprise – 33% less (decided to spend less time on the sales cycle)
• Training for free – all classes sold out – instructor led online

Along this ease of use theme, MarkLogic acknowledged that using their systems needs to be easier and that in addition to XML/XQuery programming they will be adding native support for JavaScript, greatly expanding the number of people who could program with MarkLogic. In terms of storage formats, then in addition to XML they will be adding full JSON support. On the semantics side they will offer full support for RDF, Sparql 1.1. and inferencing. Bi-temporal support will also be added with a view to answering the kind of regulatory driven questions such as “what did they know and when did they know it?”.

Joe Pasqua, SVP of Product Strategy, then took over from Gary for a more technical introduction to the MarkLogic platform. He started by saying that MarkLogic is a schema-less database with a hierarchical data model that is very document-centric, and can be used for both structured and unstructured data. Data is stored in compressed trees with the system. Joe then explained how the system is indexed explaining the “Universal Index” which lists where to find the following kinds of data as in most good search engines:

• Words
• Phrases
• Stemmed words and phrasing
• Structure (this is indexed too as new documents come in)
• Words and phrases in the context of structure
• Values
• Collections
• Security Permissions

Joe also mentioned that a “range index” is used to speed up comparisons, apparently in a similar way to column store. Geospacial indices are like 2D range indices for how near things are to a point. The system also supports semantic indices, indexing on triples of subject-predicate-object.

He showed how the system has failover replication within a database cluster for high availability but also full replication for disaster recover purposes. There were continual side references to Oracle as a “legacy database”.

On database consistency and the ACID capability Joe talked about MVCC (Multi Version Concurrency Control). Each “document” record in MarkLogic seems to have a start and end time for how current it is, and these values are used when updating data to avoid any reduction in read availability. When a document is updated a copy of it is taken but made hidden until ready – the existing document remains available until the update is ready, and then the document “end time” in the old record is marked and the “start time” marked on the new record. So effectively always doing append in serial form not seeking on disk, and the start and end time for the record enables bitemporal functionality to be implemented. Whilst the new record is being created it is already being indexed so there is zero latency searching once the new document is live.

One of the index types mentioned by Joe was a “Reverse Index” where queries are indexed and as a new document comes in it is passed over these queries (sounds like the same story from the complex event processing folks) and can trigger alerts based on what documents fit each query.

In summary, the event was a good one and MarkLogic seems interesting technology and there seems to be a variety of folks using it in financial markets with the post trade analysis example (bit like RainStor I think though, as an archive) and others using it more in the reference data space. Not sure how much MarkLogic is real-time capable – seems to be a lot of emphasis on post trade. Also brought home to me the importance of search and database together which seems to be a big strength of their technology. 

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Posted by Brian Sentance | 11 July 2014 | 9:36 pm

Cloud, data and analytics in London - thanks for coming along!

We had over 60 folks along to our event our the Merchant Taylors' Hall last week in London. Thanks to all who attended, all who helped with the organization of the event and sorry to miss those of you that couldn't come along this time.

Some photos from the event are below starting with Brad Sevenko of Microsoft (Director, Capital Markets Technology Strategy) in the foreground with a few of the speakers doing some last minute adjustments at the front of the room before the guests arrived:



Rupesh Khendry of Microsoft (Head of World-Wide Capital Markets Solutions) started off the presentations at the event, introducing Microsoft's capital markets technology strategy to a packed audience:



After a presentation by Virginie O'Shea of Aite Group on Cloud adoption in capital markets, Antonio Zurlo (below) of Microsoft (Senior Program Manager) gave a quick introduction to the services available through the Microsoft Azure cloud and then moved on to more detail around Microsoft Power BI:



After Antonio, then yours truly (Brian Sentance, CEO, Xenomorph) gave a presentation on what we have been building with Microsoft over the past 18 months, the TimeScape MarketPlace. At this point in the presentation I was giving some introductory background on the challenges of regulatory compliance and the pros and cons between point solutions and having a more general data framework in place:



The event ended with some networking and further discussions. Big thanks to those who came forward to speak with me afterwards, great to get some early feedback.



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Posted by Brian Sentance | 30 June 2014 | 8:05 pm

Xenomorph Participates in American Cancer Society Financial Services Cares Gala for Second Year

As one person I cannot change the world, but I can change the world of one person – Paul Shane Spear. As a mantra, not many come better to live by.

According to cdc.gov, each year about 8 million people die from cancer and 14 million people are diagnosed with it. To put things into perspective, twice as many people die from cancer than AIDS, malaria and tuberculosis combined. The American Cancer Society (ACS) is a voluntary health organization dedicated to the elimination of cancer. Anything that we as individuals, groups or institutions can do to assist in this regard is valued as every effort helps to aid in research and clinical trials that continue the hopes of cancer cures being found.

Into its ninth year, the annual American Cancer Society Financial Services Cares Gala will take place on Tuesday, June 24, 2014 at Cipriani 42nd Street in New York City to bring together leaders in the financial services industry who are invested in the fight against cancer. Xenomorph is proud in its association with the ACS and to be donating a custom men’s suit for the second year running to be auctioned at the event.

Visit the ACS Financial Services Cares Gala page to learn more about the event and how you can get involved.

Posted by Naj Alavi | 26 June 2014 | 5:25 pm

Cloud, data and analytics in London. Tomorrow Wednesday 25th June.

One day to go until our TimeScape MarketPlace breakfast briefing "Financial Markets Data and Analytics. Everywhere You Need Them" at Merchant Taylor's Hall tomorrow, Wednesday June 25th. With over ninety people registered so far it should be a great event, but if you can make it please register and come along, it would be great to see you there.

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Posted by Brian Sentance | 24 June 2014 | 11:25 am

Cloud, data and analytics in London. Next Wednesday June 25th.

Less than one week to go until our TimeScape MarketPlace breakfast briefing "Financial Markets Data and Analytics. Everywhere You Need Them" at Merchant Taylor's Hall on Wednesday June 25th. 

Come and join Xenomorph, Aite Group and Microsoft for breakfast and hear Virginie O'Shea of the analyst firm Aite Group offering some great insights from financial institutions into their adoption of cloud technology, applying it to address risk management, data management and regulatory reporting challenges.

Microsoft will be showing how their new Power BI can radically change and accelerate the integration of data for business and IT staff alike, regardless of what kind of data it is, what format it is stored in or where it is located.

And Xenomorph will be demonstrating the TimeScape MarketPlace, our new cloud-based data mashup service for publishing and consuming financial markets data and analytics. 

In the meantime, please take a look at the event and register if you can come along, it would be great to see you there.

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Posted by Brian Sentance | 19 June 2014 | 4:34 pm

New Client - Mizuho Securities USA

Very pleased to announce that Mizuho Securities USA has completed a successful implementation of TimeScape, you can see the press release here and more detail is available in this article on Inside Reference Data. Big thank you to all those involved in making this happen, both at Mizuho and on the Xenomorph team.

Posted by Brian Sentance | 18 June 2014 | 11:12 am

Financial Markets Data and Analytics. Everywhere London Needs Them.

Pleased to announce that our TimeScape MarketPlace event "Financial Markets Data and Analytics. Everywhere You Need Them" is coming to London, at Merchant Taylor's Hall on Wednesday June 25th. 

Come and join Xenomorph, Aite Group and Microsoft for breakfast and hear Virginie O'Shea of the analyst firm Aite Group offering some great insights from financial institutions into their adoption of cloud technology, applying it to address risk management, data management and regulatory reporting challenges.

Microsoft will be showing how their new Power BI can radically change and accelerate the integration of data for business and IT staff alike, regardless of what kind of data it is, what format it is stored in or where it is located.

And Xenomorph will be demonstrating the TimeScape MarketPlace, our new cloud-based data mashup service for publishing and consuming financial markets data and analytics. 

In the meantime, please take a look at the event and register if you can come along, it would be great to see you there.

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Posted by Brian Sentance | 11 June 2014 | 8:53 pm

Clients and Partners. Everywhere You Need Them.

Quick thank you to the clients and partners who took some time out of their working day to attend our breakfast briefing, "Financial Markets Data and Analytics. Everywhere You Need Them." at Microsoft's Times Square offices last Friday morning. Not particularly great weather on here in Manhattan so it was great to see around 60 folks turn up...

Photo 1

Rupesh Khendry of Microsoft (Head of World-Wide Capital Markets Solutions) started the event and set out the agenda for the morning. Rupesh described the expense of data within financial markets, and the difficulties experienced by risk managers in pulling together all the data and analytics they need...  Photo 2
 ...and following Rupesh was Antonio Zurlo (below) of Microsoft (Senior Program Manager) who explained the fundamentals of Microsoft Azure and what services and infrastructure it offers, including public cloud, virtual private cloud and hybrid cloud architectures. Antonio also described a key usage pattern for HPC/grid on Azure being used to "burst to the cloud" when on-premise infrasture needs to be extended for end/intra-day risk calcs...
Photo 3
Sang Lee (below) of Aite Group (Managing Partner) then delivered his presentation "Floating in the Capital Markets Cloud: Moving Beyond Data Storage". Sang's main findings from the survey of 20 financial institutions were that concerns about security and SLAs relating to cloud usage remain, but even those that were concerned about this also said they were planning to start a cloud project within the next 24 months. Cloud technology seems to becoming more acceptable of late, and Sang said this seems to be due to regulation, cost pressures and the desire to offer better services to clients. Sang confirmed that HPC/Grid with "burst to the cloud" is a common usage pattern and that "Data as a Service" is becoming more popular... 
Photo 4
Fred Veasley (below) of Microsoft (Tech Solutions Professional) to introduce Microsoft Power BI and Office 365. Fred explained how Power BI extended the capabilities of Excel with data search (finding and retrieving publicized data sources both within an organization and over the web), its integration capabilities with standard databases, NoSQL databases, data standards such as OData and new APIs/sources of data such as Facebook. Once downloaded, the data can be shaped and merged with other datasets (for instance combining data from positions databases/systems with analytics and data from the cloud), and kept up to date automatically. In addition to Power BI, Power View enables great visualizations and interactive dashboards to be created, and once finalized these can be deployed centrally via web pages down to end users...
Photo 5
After Fred, Brian Sentance (below), CEO of Xenomorph explained the origins of the TimeScape MarketPlace. Based on some discussions with Microsoft about 18 months back, the idea was effectively to firstly to get TimeScape running in the Microsoft Azure cloud, secondly to turn the data management capabilities of TimeScape "upside-down" by using it as a means to upload and publish data to the cloud and thirdly to provide one-to-many access to multiple sources of data via web interfaces and key delivery tools such as Microsoft Power BI. Put another way, without any local software or hardware infrastructure both business users and IT staff can access multiple data sources in the same format and using the same data model wherever the data is needed. In addition to .NET and Java interfaces to the TimeScape MarketPlace via OData, web API delivery into F#, Python, R and MATLAB are all in development...
Photo 1 - Copy
...and in addition to downloading data via Power BI, Brian also demonstrated how you could build on the data using "Power View" to create powerful analytical dashboard functionality that could be built and tested in Excel, then deployed centrally within a browser for access by users outside of Excel. He added that partners was one of the key aspects for the platform, and introduced the TimeScape MarketPlace Partner Program for the platform to get data, analytics, model vendors, software and service vendors involved and building on the platform. Andrew Tognela (below) of Microsoft (Worldwide Managing Director) closed the presentations...
Photo 4 - Copy

Posted by Brian Sentance | 14 May 2014 | 9:51 pm

S&P Capital IQ Risk Event #1 - Managed Services

Attended a good event at S&P Capital IQ's offices on Tuesday morning last week in London, built around the BCBS 239 document on risk aggregation and reporting (see earlier PRMIA event on this topic too). A partner vendor of S&P CIQ, Tech Mahindra, started the morning with Richard Burtsal's presentation on "Delivering an Enterprise Data Strategy". Tech Mahindra recently acquired a data management platform from UBS Asset Management and are offering a managed service data management offering based on this (see A-Team article).

Richard said that he wasn't going to "sell" in his presentation (always a worrying admission from one of us data management vendors, it usually means entirely the opposite). That small criticism aside, Richard gave a solid update on the state of the industry and obviously on what Tech Mahindra are offering, and added that:

  • For every $1 spent directly on market data, the total cost of that data goes up by a factor of 6 by the time the data is actually used 
  • 33% of rejected trades are caused by incorrect reference data
  • 60% of staff manipulate, report on or support data on a daily basis (I wonder what the other 40% actually do then? Be good to get the Tower Group report this came from to find out maybe?)
  • 25% of reference data management is wasted due to duplication and inefficiences
  • In their work with UBS Asset Management they had jointly shown that the cost of data management were reduced by 25-30% using a managed service (sounds worth verifying what the "before" situation was I guess, but interesting/impressive).
  • Clients were pushing for much faster instrument setup and a reduction in time from the 1-2 weeks setup in some systems.

There were a few questions from the audience during Richard's talk, the first asked about the differences in doing data management with the buy-side and data management on the sell-side. Richard said that his experience was that the buy-side managed less instruments (<500,000) but with greater depth of data, and sell-side held more instruments (10M+) but with less depth of data (not sure that completely reflects my experience, but sounds worth a survey maybe). 

The second question was why is the utility model for data management going to succeed right now, when previous attempts over the past 10 years had failed? Richard responded that he thought Tech Mahindra would succeed due to:

  • Tech Mahindra are data-vendor agnostic (I assume aimed at Markit-Cadis and Bloomberg-PolarLake)
  • Tech Mahindra own all their own IP (hmm, not really so sure this is a good reason or even a differentiator, but a I guess aimed at managed services that are not run by the firm that develops the data management system?)

I think the answers to this second question need thinking through more clearly, to be fair Richard had stated the 25% cost reduction already as one benefit, and various folks have said that the technology is ripe for these kinds of offerings now, but all the same the response need to be more fully developed to convince many I think (I remain undecided personally, it would be good to have some more evidence to back this up). One of the S&P CIQ added that what he thinks clients want is "Utility of Delivery" and not "Utility of Content" which I thought was a sensible comment and one that I will be revisiting in the coming months. 

On a related note to why managed services just now, another audience member asked how client specific data was managed within a utility or managed service model, and Richard said that client specific data was often managed at the client but that they can upload and integrate client generated data into the managed service offering. I think this is a very key issue within the debate about managed services and utilities, I mean I get the point the data utility proponents make that certain datasets are simple "facts" as such are either write or wrong and hence commoditisable, but much of the data is subjective and all of the data needs validating together in the context of its intended use in my view. I guess I kind of loose myself in looping arguments about why data utility vendors aren't ultimately wanting to be the next Thomson Reuters or Bloomberg (not that that is not a laudible aim but it is not going to change the world or indeed financial markets data provision very much).



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Posted by Brian Sentance | 7 May 2014 | 1:22 am

7 days to go - Financial Markets Data and Analytics. Everywhere You Need Them.

Quick reminder that there are just 7 days left to register for Xenomorph's breakfast briefing event at Microsoft's Times Square offices on Friday May 9th, "Financial Markets Data and Analytics. Everywhere You Need Them."

With 90 registrants so far it looks to be a great event with presentations from Sang Lee of Aite Group on the adoption of cloud technology in financial markets, Microsoft showing the self-service (aka easy!) data integration capabilities of Microsoft Power BI for Excel, and introducing the TimeScape MarketPlace, Xenomorph's new cloud-based data mashup service for publishing and consuming financial markets data and analytics.

Hope to see you there and have a great weekend!


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Posted by Brian Sentance | 2 May 2014 | 7:34 pm

Xenomorph Releases TimeScape Data Validation Dashboard

Very pleased to announce general availability of TimeScape Data Validation Dashboard which we announced this morning. You can see find out more here. Big thank you to all the staff and the clients involved, who have helped us to put this together over the past year. 

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Posted by Brian Sentance | 30 April 2014 | 4:30 pm

Regulatory, Compliance, and Risk Data Technology Challenges - PRMIA

The New York Chapter of PRMIA hosted "Regulatory, Compliance, and Risk Data Technology Challenges" at Credit Suisse's offices in New York, last Thursday 10th April. Abraham Thomas introduce the panelists, and Don Wesnofske started off by setting the scene for the evening's event.

Don outlined how in reaction to the 2008 Crisis the regulators now require data retention for up to 10 years or more. Don cited one particular example where data must be reconstructed within 24 to 48 hours for any date up to 7 years back, and said that this kind of "forensic" investigation capability was an important consideration for many financial institutions. He took us through a good presentation slide of his view on data management/risk architecture, and outlined how operational risk is comprised of people, process, technology and events. Don ended his presentation by taking us through Wikipedia's definition of "Big Data", and in particular talked about how data has a life cycle going through:

  • Production
  • Retention
  • Archive
  • Purged

Don handed then handed over to Luigi Mercone of Credit Suisse who is a Director of Engineering Strategy & Architecture at Credit Suisse. Luigi started by saying that to the business at CS, he is technical support which involves asking "What is on fire today? And whats going to be on fire tomorrow?" Luigi described how some time back CS had regulatory enquiry around their equities business which required them to reconstruct data from 2 years back.

The project to do this took around 4-5 months of database adminstrators time to reconstruct the world as at that point in time (I guess because tape storage was being used, and this needed restoring to disk/database). This was for an equity order management system that had doubled in size every year for the past 17 years, and at that point CS was only retaining data going back 2 years. Luigi said that it was then thought that with new regulations requiring the ability to produce forensice evidence at any point in time would potentially swamp CS's resources unless it was addressed head on and strategically. 

Luigi described the original architecture that they were using being based on an in-memory database for intraday workloads, then standard Sybase (probably ASE I guess) and then Sybase IQ for longer term archiving, taking advantage of the column-store capabilities of Sybase IQ and the resulting data compression possible. He added that the data storage requirements of the system had grown from 150TB to 1.2PB in 4 years.

Luigi then offered a comparison of this original architecture with what he found by implementing RainStor, in the original architecture the Sybase IQ database compressed data down into 160TB, whereas this was improved by a further factor of 10 down to 14TB using RainStor. He said that the RainStor was self-service providing a standard SQL interface, eliminated the need for tape storage, reduced the system "footprint" by 90% at CS, was 1/5 of the cost and the performance was good. (I guess here I would like to caveat that I know nothing of the original architecture other than the summary Luigi provided, and as such it is hard to judge whether the original architecture was optimal for the data growth experienced, and hence whether this was overall an objective comparison of Sybase IQ's capabilities with RainStor.) Luigi closed by saying that whilst RainStor was a great archive database, its original origins were in in-memory databases and he would encourage RainStor to re-enter that market too, given his experience so far. 

John Bantleman CEO of RainStor took over and described how RainStor had been designed specifically for the needs of data archiving (I guess talking more about what it does now rather than its origins outlined by Luigi above). He said that RainStor offers a 20-40x storage footprint reduction over traditional database technology and operates efficiently even at the PetaByte (PB) scale, based around RainStor proprietary database technology making use of columnar storage and being capable of storing data in both relational-style tabular format and also in more "document" style using XML and JSON formats using Key-Value access. John mention that in terms of being able to store data that not only could RainStor retrieve data at a point in time, but it could retrieve the schema being used at that point in time for a more complete view of the state of the world at that point. This echos a couple of past articles that I have penned, one for IRD and one for Wilmott Magazine on bitemporal regulatory requirements.

John said that regulation was driving the need for data archiving capabilities, with 1400 regulations added since 2008 (not sure of source, but believable) and the comment from a Chief Data Officer (CDO) at one financial markets client that if a project wasn't driven by regulatory compliance then the project isn't going to get done (certainly sounds like regulatory overload). John's opening remarks were really around how regulatory cost, complexity and compliance were driving forces behind the growth of RainStor in financial services technology, and whilst regulation is the driver, firms should look at archiving of data as an opportunity too, in order to create value from corporate memory, and to be proactive in addressing future reporting and analysis needs.

John illustrated the regulatory need for data archiving through the Consolidated Audit Trail (CAT) regulation with data retention over 7 years will generate 100PB of data. He also mentioned SEC Rule 17a-4 for broker dealers as another example of "data retention" regulation, with particular reference to storage of records in on-rewriteable, non-erasable format. John termed this WORM storage, meaning Write Once, Read Many. John seemed to imply that both the software (RainStor) and the hardware it runs on (e.g. EMC or Teradata etc) need to be WORM compliant. One of the audience members asked John about BCBS 239, to which John said that he didn't know that particular regulation (fair enough that John didn't know in my opinion, RainStor's tech is general about "data" and is applicable across many industries, whereas BCBS 239 is obviously about banks specifically and is more about data aggregation and reporting than data retention/archiving to my understanding, and this seems to be confirmed with a quick doc scan for "archive" or "retention".)

To finish off the main part of the event (before the drinks and food began) there was a panel discussion. Luigi said that it was best to "prepare for all time, not just specifics" with respect to data retention and that there were dangers in rolling up data (effectively aggregating and loosing granularity to reduce storage needs). John added that his definition of "Big Data" was "All information, for ever". Luigi added that implementing RainStor had allowed CS to spend more time on interesting questions rather than on database restoration. John proposed that version 1 of Big Data involved the retention of web data, and as such loosing a data point here and their didn't matter. Version 2 of Big Data is concerned more with enterprise data where all data has value and needs to be retained i.e. lots of high value data. He added that this was an opportunity for risk and compliance to become an asset. 


Abraham (second from left), Don (center) and John (second from right)

Overall it was a good event which I found very interesting (but I have to admit to a certain geeky interest in this kind of tech). The event would have benefitted from say another competitive or complementary technology vendor involved maybe, plus maybe an academic to give a different slant on data retention and on what the regulators hope to gain from this kind of mandated data retention. Not that the regulators have been that good at managing data themselves recently.


 Networking afterwards courtesy of Credit Suisse and RainStor








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Posted by Brian Sentance | 17 April 2014 | 3:06 pm

Financial Markets Data and Analytics. Everywhere You Need Them.

Very pleased to announce that Xenomorph will be hosting an event, "Financial Markets Data and Analytics. Everywhere You Need Them.", at Microsoft's Times Square New York offices on May 9th.

This breakfast briefing includes Sang Lee of the analyst firm Aite Group offering some great insights from financial institutions into their adoption of cloud technology, applying it to address risk management, data management and regulatory reporting challenges.

Microsoft will be showing how their new Power BI can radically change and accelerate the integration of data for business and IT staff alike, regardless of what kind of data it is, what format it is stored in or where it is located.

And Xenomorph will be introducing the TimeScape MarketPlace, our new cloud-based data mashup service for publishing and consuming financial markets data and analytics. More background and updates on MarketPlace in coming weeks.

In the meantime, please take a look at the event and register if you can come along, it would be great to see you there.

Posted by Brian Sentance | 15 April 2014 | 3:57 pm

When Big Data is not Big Understanding

Good article from Tim Harford (he of the enjoyable "Undercover Economist" books) in the FT last week called "Big data: are we making a big mistake". Tim injects some healthy realism into the hype of Big Data without dismissing its importance and potential benefits. The article talks about the four claims often made when talking about Big Data:

  1. Data analysis often produces uncannily accurate results
  2. Make statistical samplying obsolete by capturing all the data
  3. Statistical correlation is all you need - no need to understand causation
  4. Enough data means that scientific or statistical models aren't needed

Now models can have their own problems, but I can see where he is coming from, for instance 3. and 4. above seem to be in direct contradiction. I particularly like the comment later in the article that "causality won't be discarded, but it is being knocked off its pedestal as the primary fountain of meaning."

Also I liked the definition by one of the academics mentioned of a big data set being one where "N = All", and that you have "all" the data is an incorrect assumption behind some Big Data analysis put forward. Large data sets can mean that sample error is low, but sample bias is still a potentially big problem - for example everyone on Twitter is probably not representative of the population of the human race in general.

So I will now press save on this blog post, publish in Twitter and help re-enforce the impression that Big Data is a hot topic...which it is, but not for everyone I guess is the point.



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Posted by Brian Sentance | 8 April 2014 | 10:21 pm

Innovations in Liquidity Risk Management - PRMIA

PRMIA put on an event at MSCI on Wednesday, called "Innovations in Liquidity Risk Management".



Melissa Sexton of Morgan Stanley introduced the agenda, saying that the evening would focus on three aspects of liquidity risk management:

  • methodology
  • industry practice
  • regulation

LiquidityMetrics by MSCI - Carlo Acerbi of MSCI then took over with his presentation on "LiquidityMetrics". Carlo said that he was pleased to be involved with MSCI (and RiskMetrics, aquired by MSCI) in that it had helped to establish and define standards for risk management that were used across the industry. He said that liquidity risk management was difficult because:

  • Clarity of Definition - Carlo suggest that if he asked the audience to define liquidity risk he would receive 70 differing definitions. Put another way, he suggested that liquidity risk was "a strange animal with many faces".
  • Data Availability - Carlo said that there were aspects of the market that we unobservable and hence data was scarce/non-existent and as such this was a limit on the validity of the models that could be applied to liquidity risk.

Carlo went on to clarify that liquidity risk was different depending upon the organization type/context being considered, with banks obviously focusing on funding. He said that LiquidityMetrics was focused on asset liquidity risk, and as such was more applicable to the needs of asset managers and hedge funds given recent regulation such as UCITS/AIFMD/FormPF. The methodology is aimed at bringing traditional equity market impact models out from the trading floor across into risk management and across other asset classes. 

Liquidity Surfaces - LiquidityMetrics measures the expected price impact for an order of a given size, and as such has dimensions in:

  • order size
  • liquidity time horizon
  • transaction costs

The representation shown by Carlo was of a "liquidity surface" with x dimension of order size (both bid and ask around 0), y dimension of time horizon for liquidation and z (vertical) dimension of transaction cost. The surface shown had a U-shaped cross section around zero order size, at which the transaction cost was half the bid-ask spread (this link illustrates my attempt at verbal visualization). The U-shape cross section indicates "Market Impact", its shape over time "Market Elasticity" and the limits for what it is observable "Market Depth". 

Carlo then moved to consider a portfolio of instruments, and how obligations on an investment fund (a portfolio) can be translated into the estimated transaction costs of meeting this obligations, so as to quantify the hidden costs of redemption in a fund. He mentioned that LiquidityMetrics could be used to quantify the costs of regulations such as UCITS/AIFMD/FormPF. There was some audience questioning about portfolios of foreign assets, such as holding Russian Bonds (maybe currently topical for an audience member maybe?). Carlo said that you would use both the liquidity surfaces for both the bond itself and the FX transaction (and in FX, there is much data available). He was however keen to emphasize that LiquidityMetrics was not intended to be used to predict "regime change" i.e. it is concerned with transaction costs under normal market conditions). 

Model Calibration - In terms of model calibration, then Carlo said that the established equity market impact models (see this link for some background for instance) have observable market data to work with. In equity markets, traditionally there was a "lit" central trading venue (i.e. an exchange) with a star network of participants fanning out from it. In OTC markets such as bonds, there is no star network but rather many to many linkages establised between all market participants, where each participant may have a network of connections of different size. As such there has not been enough data around to calibrate traditional market impact models for OTC markets. As a result, Carlo said that MSCI had implemented some simple models with a relatively small number of parameters. 

Two characteristics of standard market impact models are:

  1. Permanent Effects - this is where the fair price is impacted by a large order and the order book is dragged along to follow this.
  2. Temporary Effects - this is where the order book is emptied but then liquidity regenerates

Carlo said that the effects were obviously related to the behavioural aspects of market participants. He said that the bright side for bonds (and OTC markets) was given that the trades are private there was no public information, and price movements were often constrained by theoretical pricing, therefore permanent effects could be ignored and the fair price is insenstive to trading (again under "normal" market conditions). Carlo then moved on to talk about some of the research his team was doing looking at the shape of the order book and the time needed to regenerate it. He talked of "Perfectly Elastic" markets that digest orders immediately and "Perfectly Plastic" markets that never regenerate, and how "Relaxation Time" measures in days how long the market takes to regenerate the order book. 


Liquidity Observatory - Carlo described how the data was gathered from market participants on a monthly basis using a spreadsheet to categorize the bond/asset class type, and again using simple parameters from active "expert" traders. Take a look at this link and sign up if this is you. (This sounded to me a lot like another "market consensus" data gathering exercise which are proving increasingly popular, such as one the first I had heard of many years back in Totem - we are not quite fully ready for "crowdsourcing" in financial markets maybe, but more people are seeing sense in sharing data.). 

Panel Debate - Ron Papenek of MSCI was moderator of the panel, and asked Karen Cassidy of Morgan Stanley about her experiences in liquidity risk management.

Liqudity Risk Management at Banks - Karen started by saying that in liquidity management at Morgan Stanley they look at:

  • Funding
  • Operating Capital
  • Client Behaviour

Since 2008, Karen said that liquidity management had become a lot more rigorous and formalized, being rule based and using a categorisation of assets held from highly liquid to highly illiquid. She said that Morgan Stanley undertake stress testing by market and also by idiosyncratic risk over time frames of 1 month and 1 year. As part of this they are assessing the minimum operating liquidity needed based on working capital needs. 

Karen added that Morgan Stanley are expending a lot of effect currently on data collection and modelling given that their data is specific to a retail broker-dealer unit, unlike many other firms. They are also looking at metrics around financial advisors, and how many clients follow the financial advisor when he or she decides to switch firms. 

Business or Regulation Driving Liquidity Risk Management - Ron asked Karen what were the drivers of their processes at Morgan Stanley. Karen said that in 2008 the focus was on fundability of assets, saying that the FED was monitoring this on a daily basis. She made the side comment that this monitoring was not unusual since "Regulators live with us anyway". Karen said that it was the responsibility of firms to come up with the controls and best practice needed to manage liquidity risk, and that is what Morgan Stanley do anyway.

Karen added that in her view the industry was over-funding and funding too long in response to regulation, and that funding would be at lower but still pragmatic levels in the absence of regulatory pressure. Like many in the industry, Karen thought the regulation had swung too far in response to the 2008 crisis and would eventually swing back to more normal levels. 

Carlo added that he had written an unintentionally prescient academic paper on liquidity management in 2008 just prior to the crisis hitting, and he thought the regulators certainly arrived "after" the crisis rather than anticipating it in any way. He thought that the banks have anticipated the regulators very well with measures such as LCR and SFR already in place. 

In contrast, Carlo said that the regulators were lost in dealing with liquidity risk management for asset managers and hedge funds, with regulation such as UCITS being very vague on this topic and regulators themselves seeking guidance from the industry. He recounted a meeting he had with BaFin in 2009 where he told them that certain of their regulations made no sense and he said they acknowledge this and said the asset management industry needed to tell them what to implement (sounds like the German regulator is using the same card as the UK regulators in keeping regulations vague when they are uncertain, waiting for regulated firms to implement them to see what the regulation really becomes...). 

What Have We Learnt Since 2008 - Karen said that back in 2008 liquidity was not managed to term, funding basis was not rigorous and relied heavily on unsecured debt. She said that since then Morgan Stanley had been actively involved in shaping the requirements of better liquidity risk management with more rigorous analysis of counterparties and funding capacity. Karen said that stronger governance was a foundation for the creation of better policy and process. She said that regulators were receptive to new ideas and had been working with them closely.

What will be the effect of CCPs on OTC markets? Carlo said that when executing a large order, you have the choice between executing 1) multiple small orders with multiple counterparties or 2) a single large block order with one counterparty. In this regard, the equity and bond markets are very different. In lit equity venues, the best approach is 1), but in the bond markets approach 2) is taken since the trade information is not transparent to the market.

Obviously equity markets have become more fragmented, and this has resulted in improve market quality since it is harder to get all market information and hence the market is less resonant to big events/orders. Carlo added that with the increased transparency proposed for OTC markets with CCPs etc will this improve them? His answer was that this was likely to improve the counterparty risk inherent in the market but due to increased transaparency is likely to have a negative effect on transaction costs (I guess another example of the law of unintended consequencies for the regulators).

Audience Questions - there then followed some audience questions:

LiqidityMetrics extrapolation - one audience member asked about transaction cost extrapolation in Carlo's modelling. Carlo said that MSCI do not extrapolate and the liquidity surface terminates where the market terminates its liquidity. There was some extrapolation used along the time dimension however particularly in relation to the time-relaxation parameter. 

LiquidityMetrics "Cross-Impact" - looking at applying LiquidityMetrics to a portfolio, one audience member wondering if an order for one asset distorted the liquidity surface for other potentially related assets. Carlo said this was a very interesting area with little research done so far. He said that this "cross-impact" had not been detected in equity markets but that they were looking at it in other markets such as fixed income where effective two assets might be proxies for duration related trading. Carlo put forward a simple model of where the two assets are analogous to two species of animal feeding from the same source of food.

Long and short position liquidity modelling - one audience member asked Carlo what the effects would be of being long or short and that in a crisis you would prefer to be short (maybe obviously?) given the sell off by those with long positions. Carlo clarified that being "short" was not merely taking the negative number on a liquidity surface for a particular asset but rather a "short" is a borrowing position with an obligation to deliver a security at some defined point, and as such is a different asset with its own liquidity surface.  

Changing markets, changing participants - final question of the evening was from one member of the audience who asked if the general move out of fixed income trading by the banks over recent years was visible in Carlo's data? Carlo said that MSCI only have around two years of data so far and as such this was not yet visible but his team are looking for effects like this amongst others. He added that the August 2011 weak banks - weak sovereigns in Europe was visible with signals present in the data.


Good food and good (really good I thought) wine put on by MSCI at the event reception. Great view of Manhattan from the 48th floor of World Trade Centre 7 too.







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Posted by Brian Sentance | 31 March 2014 | 2:40 pm

#DMSLondon - The Hobgoblin of Little Minds: Risk and Regulation as Drivers

The second panel of the day was "Regulation and Risk as Data Management Drivers" - you can find the A-Team's write up here. Some of my thoughts/notes can be found below:

  • Ian Webster of Axioma responded to a question about whether consistency was the Holy Grail of data management said that there isn't consistent view possible for data used in risk and regulation - there are many regulations with many different requirements and so unnecessary data consistency is "the hobgoblin of little minds" in delaying progress and achieving goals in data management.
  • James of Lombard Risk suggest that firms should seek competitive advantage from regulatory compliance rather than just compliance alone - seeking the carrot and not just avoiding the stick.
  • Ian said he thought too many firms dealt with regulatory compliance in a tactical manner and asked if regulation and risk were truly related? He suggested that risk levels might remain unchanged even if regulation demanded a great deal more reporting.
  • Marcelle von Wendland said she thought that regulation added cost only, and that firms must focus on risk management and margin.
  • James said that "regulatory risk" was a category of risk all in itself alongside its mainstream comtempories.
  • Ian added that risk and finance think about risk differently and this didn't help in promoting consistency of ideas in discussions about risk management.
  • James said that the legacy of systems in financial markets was a hindrince in complying with new regulation and mentioned the example of the relatively young energy industry where STP was much easier to implement.
  • Laurent of Bloomberg said that young, emerging markets like energy were greenfield and as such easier to implement systems but that they did not have any experience or culture around data governance.
  • Marcelle said that the G20 initiatives around trade reporting at least promoted some consistency and allowed issues to be identified at last.
  • Ian said in response that was unconvinced about politically driven regulation, questioning its effectiveness and motivations.
  • Ian raised the issues of the assumptions behind VaR and said that the current stress tests were overdone.
  • Marcelle agreed that a single number for VaR or some other measure meant that other useful information has potentially been ignored/thrown away.
  • General consensus across the panel that fines were not enough and that restricting business activities might be a more effective stick for the regulators.
  • James reference the risk data aggregation paper from the Basel Committee and suggested that data should be capture once, cleaned once and used many times.
  • Ian disagreed with James in that he thought clean once, capture once and use many times was not practically possible and this goal was one of the main causes of failure within the data management industry over the past 10 years. 
  • The panel ended with Ian saying that we not just solve for the last crisis, but the underlying causes of crises were similar and mostly around asset price bubbles so in order to recuce risk in the system 1) lets make data more transparent and 2) do what we can to avoid bubbles with better indices and risk measures.

3 Regulation panel


Posted by Brian Sentance | 24 March 2014 | 6:09 pm

Risk Management in Securities Financing and Money Market Funds - PRMIA

I went along to this PRMIA event on Thursday evening hosted by Credit Suisse and sponsored by Acacia Capital. Viktoria Baklanova introduced the panel with Joseph Tenaga as MC for the panel and very quickly got a plug in for her about to be released book written with Joe on money market funds. For those of us who don't know so much about money market funds, then these are a form of interest-bearing fund that invests in short term debt securities. The funds attempt to maintain a stable Net Asset Value (NAV) but to quote Wikipedia they "are widely (though not necessarily accurately) regarded as being as safe as bank deposits yet providing a higher yield." Their role in the 2008 financial crisis echos on strongly through to the present day, with controversy of their supposedly stable NAV (typically $1 in the US) and the associated phrase "Breaking the Buck".

Joe Tenaga started the panel with an (unnecessary in my view) justification of academia, asking the rhetorical question "What is the point of academia?" to which Joe answered that "knowledge is what makes the impossible possible" and he added that knowledge drives us to make things better. Joe introduced the next panelist, Matthew Fink of Oppenheimer Mutual Funds. Matt said that we would be prepared to wager that he had worked in the money market funds area the longest of anyone in the room, having started his involvement in the industry in April of 1971. Matt gave a picture of the mutual funds industry at the time, with around $60B AUM in the US with 95% invested in equities. At that time the mutual funds industry was going through a very bad time, as the economy and markets were falling and fund redemptions were rising to such an extent that they had fallen to $30B over the next few years. At the time, if redemptions had continued at this rate the industry would have vanished.


Against this background for the mutual funds industry, interest rates in the US were very high rising from 6% in 1969 to around 12% in 1974. So many people were paying very high rates on mortgage obligations whilst being limited to receiving only 4-5% on savings due to "Regulation Q". For wealthy individuals, it was possible to get around these savings limits, but only if you had $100,000 to put in a Commercial Deposit or $10,000 into a T-Bill. Ironically it was the regulation to remove one risk (it had been thought that competition on deposit rates had contributed to the bank failures of the Great Depression) that had sparked the drive to innovate to find higher returns and create the money market funds industry as a result, with the first fund being "The Reserve Fund" in 1971. (side comment - if regulation from the 1930's via the 1970 can cause problems in 2014, then I would have to defer to which ever Deity you worsphip to advise on what the longer-term consequencies will be of the current round of complexity being implemented...). 

The banks saw the money pouring into money market funds such as those from Fidelity and Dreyfus, and understandably wanted to be part of the party too. Some of the worries about money market funds were firstly what if a fund got into trouble? Secondly, the bank regulators were angry that funds were flowing into this new industry and were concerned that it would increase bank failures. 1979 saw a certain Paul Volcker (ever heard of him?) complaining that money market funds were acting like checking accounts. Matt said that he spoke with Volcker and said that this was not the case, to which Volcker replied that it was true since his wife's company was paying staff wages out on checks written against money market funds. 

Henry Shilling of Moody's took over from Matt and showed a few slides, firstly showing the number of funds with AAA (AAAmmf, Aaamf, AAAmf) from Fitch (49), Moody's (130) and S&P Ratings (156). Henry described how regulators have wanted to reduce the risk of funds by shortening the maturity of the debt held from 90 to 60 days, and having one and seven day liquidity windows. He showed that there is a high degree of concentration risk in the industry with the top 10 firms have 74% AUM and the top 20 covering 94% of the AUM for the industry. Similarly, looking at the assets invested in the funds, 80% are from financial institutions.

Igor Axenov of Barclays Capital then showed his slides, illustrating the composition of the funds by asset type prior to the crisis:

  • ABS related - 34%
  • Bank products - 23%
  • Repos - 15%
  • Corporate - 11%
  • Unsecured - 8%
  • Other - 6%

He said that the largest exposure then was to securitized products, with implicit indirect exposure to banks. Igor said that CDO issuance was rising at a rate of $300B per year through 2005/6/7 and that much of the structuring was done to ensure that the ABS products fitted the needs and regulations of money market funds. Detailing the ABS asset composition, Igor showed:

  • Asset Backed (AB) commercial paper - 50%
  • AB medium term notes - 24%
  • Extendible AB commercial paper - 17%
  • ABS Bonds - 5%

Igor said the asset backed commercial paper market (largely funded through money market funds) had grown to $1.2Trln by 2007, and has fallen precipitously since then down to around $200M now.

Looking at the current money market fund portfolio, it looks like:

  • Bank products - 41%
  • Repos - 18%
  • CP - 15%
  • US Govt + Agency debt - 10%
  • Asset backed CP - 9%
  • Other corporate - 4%

Terence Ma added that the Money Market Fund industry sat at 4Trln in 2008 and was now around $2.7Trln in 2014. Matthew Fink said that given his involvement in regulation that he had "never met the face of the enemy before" in Igor was the start of some lively but well-intended banter between the ex-regulator and structurer.  

Terence Ma of South Street Securities described his business, which exclusively involves repurchase agreements "Repos". Terry said that in the 1990's Citi were very disciplined on balanced sheet management and in his opinion, then adhead of the market in this regard. He that the Repo business earns small spreads and as a result needs a big balance sheet. When John Read took over Citi, he decided that he did not like the Repo business since its ROE could not compete with some of the products in retail and other parts of the business. So Terry and his partners wondered whether the Repo business could be managed off balance sheet, so they formed a broker-dealer business and when Citi merged with Salomen Brothers they span off. This was December of 2003 but by 2008 they were left "sucking wind" by the crisis.

Terry was quite explicit that his firm is not part of the "shadow banking system" but are subject to the SEC. He then described a few more things about his business, starting with his definition of a Repo as "an agreement to sell and repurchase a security at a fixed date in the future", with the objectives of providing cash inventory, leverage and short cover. All borrowings are lent out, unlike Lehman Brothers in 2008. They do not finance again structured products unless guaranteed, and only accept collateral from Fannie, Freddie and the US Government. 

Joe Tenaga then open out questions to the audience. Someone asked who the first MMF was (I think they missed the first part of the talk) and Matt said that the Keystone MMF filed first but the first was Reserve MMF (which got into trouble in 2008). Matt said that it was interesting that the same people like Paul Volcker were stilled involved with the same concerns about the industry many years on. 

The next question was how did early MMFs keep their NAV at $1? Henry said that the "Break the Buck" definition is when there is a mark to market fall of 50bp or more. He said that historically that fund sponsors had addressed any issues with breaking the buck with purchases of the fund at par or direct equity investment in the fund - they did this since the effect on their funds and the industry would be too great to comtemplate. Hence an MMF is not a perfect product but (up until Lehmans in 2008 with a 50% NAV loss) has a near-perfect record. He added that the first funds to break the buck were from Salomon's and First Chicago.

Matt added some further history saying the need to maintain the $1 NAV was initially due to the needs of some of the early investors in the industry, who could not invest in products unless they had fixed NAV. He mentioned that one of the companies, Federated, had a long running battle with the SEC over Money Market Funds, filing for exemptions to avoid some of the restrictions that the SEC was trying to impose since the SEC regarded the MMF industry as damaging the mutual funds industry. He mentioned Rule 2-a7 which defines the accountancy procedures for keeping the NAV at $1, and some of the battles around amortization and penny-rounding policies to facilitate this. To later questions, Matt said that the SEC wants a floating NAV for institutional MMFs but currently wants to leave retail alone (seems somewhat arbitrary choice i.e. lets only change what has been problematic before, ignore anything else and not contemplate what could happen if only we understood things better). He said that the SEC was weak and FSOC is driving the SEC to change (and FSOC itself is a pawn of the Federal Reserve). 

Overall an interesting panel, particularly when you have characters such as Matt Fink who know the history and stories within the industry so well. 






Posted by Brian Sentance | 24 March 2014 | 12:01 pm

#DMS London - Building a Flexible Enterprise Architecture

You can find A-Team's view on "Building a Flexible Enterprise Architecture" here. Some additional notes/thoughts:

  • I thought Neil van Lint of GoldenSource's comment about "putting lipstick on a pig" with reference to legacy architectures was pretty funny and apt.
  • The old Irish joke about asking for directions and receiving the response "Well I wouldn't start from here" is also amusing but too true with our industry and most large organisations.
  • "Schema on read, not on write" is getting my award for phrase of the month from NoSQL proponents (quote Amir from Mark Logic).
  • Agree that ETL is problematic/a big resource drain but unless starting from a greenfield site it is currently unavoidable.
  • I like the idea of FIBO (and decoupling data meaning from data structure) but still left unsure what it actually (practically) covers so far and how much it is used, despite the references to it by Peter of Nordea. I guess it is all a matter of semantics.
  • I knew little of TOGAF mentioned by Rupert but maybe that is because I am a techie no more (if I ever was).
  • Rupert came back to his "where are we?" and data map questions and asked the audience how many of them had a good handle on where data was used in what systems - unsurprisingly not many with a Morgan Stanley guy saying that there monitoring systems were linked to the operational systems for a full inventory of data.
  • I agree that the regulators need to push standards directly on the industry - Amir ended the panel suggesting the regulators need to say things like "Thou shalt use FIBO".

2 First panel

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Posted by Brian Sentance | 21 March 2014 | 7:44 pm

#DMSLondon - Creating a Data Map of the Financial Enterprise

Rupert Brown of UBS did the keynote at this Spring's A-Team Data Management Summit (DMS). Rupert's talk was about understanding what data there is within a financial institution and understanding where it comes from and where it goes to. Rupert started by asking the question "Where are we?" illustrating it with a map of systems and data flows for an institution - to my recollection I think he said it stretched to 7 metres in length and did not look that accessible or easy to understand. He asked what dimensions it should have as a "map" of data, wondering what dimensions are analogous to latitude, longitude, altitude and orientation? Maybe things like function, product, process, accounting or legal entity as potential candidates.

1 Rupert start of day where are we

Briefly Rupert took a bit of a detour into his love of trains with a little history on the London Underground Map. He started by mentioning the role of George Dow who illustrated maps for train routes in a single line, showing just dependency and lineage (what stations are next etc) and ignoring geography and distance. This was built upon by another gentleman, Harry Beck, who took these ideas a stage further with the early ancestors of the current Undergroud map, showing both routes but interweaving all the lines together into a map that additionally was topologically sufficient (indicating broad direction - NESW).

Continuing on with this analogy of Underground to maps of data and data management, Rupert then mentioned Frank Pick who created the Underground brand. Through creating such an identifiable brand, effectively Frank got people to believe and refer to the map, and that people in data governance need and could benefit from taking a similar approach to data governance with data management. I guess it is easy to take maps we see every day for granted and particularly some of the thought that went into them, maybe ideas that initially were not intuitive (or at least not directly representative of physical reality) but that greatly improved understand and comprehension. Put another way, representing reality one for one does not necessarily get you to something that is easy to understand (sounds like a "model" to me). 

Rupert then described some of his efforts using Open Street Map to map data, making use of the concepts of nodes, ways and areas. Apparently he had implemented this using a NoSQL database (Mark Logic) for performance reasons (doesn't sound like a really "big data" sized problem with several hundred apps and several thousand data transports but nevertheless he said it was needed, maybe as a result of its graph like nature?). He said that the data was crowdsourced to refine the data, with a wiki for annotations. He said he was interested in the bitemporality of data, i.e. how the map changes over time. He advised that every application should also be thought of as its own "databus" in addition to any de facto databuses might be present in the architecture. 

In summary the talk was interesting, but it was demonstrable from what Rupert showed that we have long way to go in representing clearly and easily where data came from, where it goes to and how it is used. I think Rupert acknowledges this and has some academic partnerships trying to develop better ways of representing and visualizing data. Certainly data lineage and audit trail on everything is a hot topic for many of our clients currently, and something that deserves more attention. You can download Rupert's presentation here and the A-Team's take on his talk can be found here.

Posted by Brian Sentance | 20 March 2014 | 5:49 pm

S&P Capital IQ Risk Event #2 - Enterprise or Risk Data Strategy?

Christian Nilsson of S&P CIQ followed up Richard Burtsal's talk with a presentation on data management for risk, containing many interesting questions for those considering data for risk management needs. Christian started his talk by taking a time machine back to 2006, and asking what were the issues then in Enterprise Data Management:

  1. There is no current crisis - we have other priorities (we now know what happened there)
  2. The business case is still too fuzzy (regulation took care of this issue)
  3. Dealing with the politics of implementation (silos are still around, but cost and regulation are weakening politics as a defence?)
  4. Understanding data dependencies (understanding this throughout the value chain, but still not clear today?)
  5. The risk of doing it wrong (there are risk you will do data management wrong given all the external parties and sources involved, but what is the risk of not doing it?)

Christian then moved on to say the current regulatory focus is on clearer roadmaps for financial institutions, citing Basel II/III, Dodd Frank/Volker Rule in the US, challenges in valuation from IASB and IFRS, fund management challenges with UCITS, AIFMD, EMIR, MiFID and MiFIR, and Solvency II in the Insurance industry. He coined the phrase that "Regulation Goes Hollywood" with multiple versions of regulation like UCITS I, II, III, IV, V, VII for example having more versions than a set of Rocky movies. 

He then touched upon some of the main motivations behind the BCBS 239 document and said that regulation had three main themes at the moment:

  1. Higher Capital and Liquidity Ratios
  2. Restrictions on Trading Activities
  3. Structural Changes ("ring fence" retail, global operations move to being capitalized local subsidiaries)

Some further observations were on what will be the implications of the effective "loss" of globablization within financial markets, and also what now can be considered as risk free assets (do such things now exist?). Christian then gave some stats on risk as a driver of data and technology spend with over $20-50B being spent over the next 2-3 years (seems a wide range, nothing like a consensus from analysts I guess!). 

The talk then moved on to what role data and data management plays within regulatory compliance, with for example:

  • LEI - Legal Entity Identifiers play out throughout most regulation, as a means to enable automated processing and as a way to understand and aggregate exposures.
  • Dodd-Frank - Data management plays within OTC processing and STP in general.
  • Solvency II - This regulation for insurers places emphasis on data quality/data lineage and within capital reserve requirements.
  • Basel III - Risk aggregation and counterparty credit risk are two areas of key focus.

Christian outlined the small budget of the regulators relative to the biggest banks (a topic discussed in previous posts, how society wants stronger, more effective regulation but then isn't prepared to pay for it directly - although I would add we all pay for it indirectly but that is another story, in part illustrated in the document this post talks about).

In addtion to the well-known term "regulatory arbitrage" dealing with different regulations in different jurisdictions, Christian also mentioned the increasingly used term "subsituted compliance" where a global company tries to optimise which jurisdictions it and its subsidiaries comply within, with the aim of avoiding compliance in more difficult regimes through compliance within others.

I think Christian outlined the "data management dichotomy" within financial markets very well :

  1. Regulation requires data that is complete, accurate and appropriate
  2. Industry standards of data management and data are poorly regulated, and there is weak industry leadership in this area.

(not sure if it was quite at this point, but certainly some of the audience questions were about whether the data vendors themselves should be regulated which was entertaining).

He also outlined the opportunity from regulation in that it could be used as a catalyst for efficiency, STP and cost base reduction.

Obviously "Big Data" (I keep telling myself to drop the quotes, but old habits die hard) is hard to avoid, and Christian mentioned that IBM say that 90% of the world's data has been created in the last 2 years. He described the opportunities of the "3 V's" of Volume, Variety, Velocity and "Dark Data" (exploiting underused data with new technology - "Dark" and "Deep" are getting more and more use of late). No mention directly in his presentation but throughout there was the implied extension of the "3 V's" to "5 V's" with Veracity (aka quality) and Value (aka we could do this, but is it worth it?). Related to the "Value" point Christian brought out the debate about what data do you capture, analyse, store but also what do you deliberately discard which is point worth more consideration that it gets (e.g. one major data vendor I know did not store its real-time tick data and now buys its tick data history from an institution who thought it would be a good idea to store the data long before the data vendor thought of it).

I will close this post taking a couple of summary lists directly from his presentation, the first being the top areas of focus for risk managers:

  • Counterparty Risk
  • Integrating risk into the Pre-trade process
  • Risk Aggregation across the firm
  • Risk Transparency
  • Cross Asset Risk Reporting
  • Cost Management/displacement

The second list outlines the main challenges:

  • Getting complete view of risk from multiple systems
  • Lack of front to back integration of systems
  • Data Mapping
  • Data availability of history
  • Lack of Instrument coverage
  • Inability to source from single vendor
  • Growing volumes of data

Christian's presentation then put forward a lot of practical ideas about how best to meet these challenges (I particularly liked the risk data warehouse parts, but I am unsurprisingly biassed). In summary if you get the chance then see or take a read of Christian's presentation, I thought it was a very thoughtful document with some interesting ideas and advice put forward.








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Posted by Brian Sentance | 11 March 2014 | 7:26 pm

See you at the A-Team Data Management Summit this week!

Xenomorph is sponsoring the networking reception at the A-Team DMS event in London this week, and if you are attending then I wanted to extend a cordial invite to you to attend the drinks and networking reception at the end of day at 5:30pm on Thursday.

In preparation for Thursday’s Agenda then the blog links below are a quick reminder of some of the main highlights from last September’s DMS:

I will also be speaking on the 2pm panel “Reporting for the C-Suite: Data Management for Enterprise & Risk Analytics”. So if you like what you have heard during the day, come along to the drinks and firm up your understanding with further discussion with like-minded individuals. Alternatively, if you find your brain is so full by then of enterprise data architecture, managed services, analytics, risk and regulation that you can hardly speak, come along and allow your cerebellum to relax and make sense of it all with your favourite beverage in hand. Either way your you will leave the event more informed then when you went in...well that’s my excuse and I am sticking with it!

Hope to see you there!

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Posted by Brian Sentance | 3 March 2014 | 6:33 pm

Aqumin visual landscapes for TimeScape

Very pleased that our partnering with Aqumin and their AlphaVision visual landscapes has been announced this week (see press release from Monday). Further background and visuals can be found at the following link and for those of you that like instant gratification please find a sample visual below showing some analysis of the S&P500.


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Posted by Brian Sentance | 31 January 2014 | 7:04 pm

F# in Finance New York Style

Quick plug for the New York version of F# in Finance event taking place next Wednesday December 11th, following on from the recent event in London. Don Syme of Microsoft Research will be demonstrating access to market data using F# and TimeScape. Hope to see you there!

Posted by Brian Sentance | 6 December 2013 | 7:49 am

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