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Technology Office challenges especially better use of information within a banking environment. Provided tangible examples of transactional analysis, text sentiment analysis, power consumption analysis
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Fst auckland presentation
- 2. Context • • • • • 800,000+ customers 280+ branches 300 ATMs 1,100 staff $14.4bn assets • 2 data centres • 350 production servers (50% virtual) • 0.5PB storage (10% structured)
- 3. My Technology Office Challenges Product / Marketing / Operations Technology Delivery Architecture Narrative 1 – what, why… 2 Make the Right Decisions 3 Capability Development 4 Derive Value from Data
- 4. 2. Making the right decision Forces for control • Processes and tools • Comprehensive documentation • Plans Forces for creation • Individuals and Interactions • What works beats theory • Change is everywhere
- 5. Sound familiar? … it’s the Agile Manifesto
- 6. My observations Our best work has happened when 3 things were true: 1. We give it a go 2. With a supportive business team that’s not scared to understand the little details 3. And a committed and passionate technical lead 4. That ensures people work together.
- 7. (Collaborative) 3. Capability Development ^ Architecture Briefing Forum Analytics Forum (soon!) Enterprise Information Forum
- 8. Architecture Briefing Forum • • • • • • • • • • • Distributed configuration across Internet Banking servers Federated Identity Anti-Money Laundering Operations Management with System Center Web Service Gateways Application Build and Deployment Storage Pre-Production Update NoSQL logging design comparing MongoDB and RavenDB Crypto 101 …
- 9. 4.Gaining value from information Key insight has been that multiple groups are required to gain full benefit. Why? Analytics platform Enterprise Information platform Differing skills and knowledge. Data Platform IT has a role across all
- 10. Our Data Platforms Storage Database Hitachi Virtualised Storage ½ PB of data = 7 years of HD-TV 1/5th is database storage High growth rate • Microsoft SQL Server • NoSQL: Universe, RavenDB, MongoDB 800 600 Shared Storage (TB) Staging • 65GB exposed to systems every day • >1000 data jobs / day 400 200 0 2000 2005 2010 2015
- 11. NoSQL?????
- 12. Our Enterprise Information Platform • Key strategy is to unlock information assets • Make data available into Analyst Playpens • Through improved data acquisition / transformation / delivery • Currently in progress…
- 13. Our Analytics Platform Microsoft Excel (+ VBA/.NET) Tibco S+ Microsoft SQL Server (DMX) Data Sungard ALM Analyst’s Playpen(s) All different. All with strengths. All suited to different users.
- 14. And Analytics Means What Exactly? 1. Optimisation Optimal decisions under competing constraints. 2. Understanding connectivity Connectivity between attributes leading to a decision ie “market basket analysis”; or understanding clustering of outcomes. 3. Predicting the future Predicting future outcomes based upon history.
- 15. A rapidly changing landscape
- 16. Analytics = $++ It’s not just the licensing… …it’s the infrastructure.
- 17. So go cloud, but take care…
- 19. How is this useful? Classic bank examples are • Risk analysis – Market risk – Credit risk – Operational risk • Proactive optimised marketing – Market basket analysis – Prioritisation of campaigns • Portfolio optimisation • Customer needs analysis
- 20. Bit of fun: Twitter sentiment @ASBBank @BNZBank @KiwibankNZ @ANZ_NZ @WestpacNZ
- 21. Bit of fun: ORM sentiment Online Relationship Sentiment
- 22. Understanding content Strictly speaking latent semantic analysis Relate terms to documents: Term Document Frequency Matrix Uncover frequent terms: Uncover associations:
- 23. Wordcloud…
- 24. Connectivity
- 25. Online buyer behaviour Is online purchasing more or less important in the regions versus the cities? My hypothesis is yes High Low Can’t disprove it…
- 28. How does your energy bill stack up?
- 29. What do we see? Large variances across companies… Minor regional differences… Colours = Companies $0 $50 $100 Key data is power company avg bill… Optimisation possibility of about $15/month or $180/year $80 $90 $100 $110 $120 $130 $140 $150 $160 $170
- 30. Metrics and Analytics for IT • Summer of Tech project • To build digital signage that displays metrics across operations, delivery, and the business. http://www.summeroftech.co.nz/ • Focused on the IT consumer
- 31. Summary We build narratives to explain what and why. We work with all of IT to grow capability and ensure we’re collectively making the right decisions. We support analysts by ensuring they have the platforms, tools, and skills to succeed.
Editor's Notes
- There are 4 technology leadership challenges I face at kiwibank of which the most important is the Architecture narrative.The single most important one is creating understanding through narrative. It’s about explaining what we have, why we have and what we’ve got to do. Without this we’d end up with disjoint, disconnected strategies and you see this time and again in companies and it’s easy to identify.Replicated systemsDiversity of technology without good reasonA collection of ‘point in time’ solutions.I don’t want this to happen at Kiwibank; it’s critical we keep moving ahead with the context of what and why flowing through everything we do.The second challenge I face is meeting the knowledge gap.My GM of IT is constantly remarking on the increasing complexity of the technologies we deal with. And I have to ask myself is it true? Is IT more complex now than it was in the 90s?I remember the 90s quite clearly…We wrote web applications in .asp vbscript/jscriptWe used local disk a lot and didn’t worry about SANs or storage virtualisationWe racked servers that looked like PCs and they were largely self containedToday it is different.Infrastructure is all about virtualization, all about adding another layer of abstraction to move away from discrete computing boxes and move to private cloudsOperating systems change and we have very good examples affecting us all today in the client and server space with Windows, iOS and Android.Software is about buses, events, asynchronous messaging and distribution across cores, CPUs and parallel computingDevelopment has become more refined. Concepts that were only talked about in math and computer science departments 20 years ago are now the stuff of every day practice.Functional programming is in along with concepts like Monads, combinators and higher order functionsThe role of the relational database is now better understood so we have regained appreciation of the many other types of databases out there. Each of these has a niche within which they makes sense and it’s the reason for the nosql movement.So keeping up with recent developments is a big task and we can’t avoid it or fear it – being good at learning and applying these technologies is key to being competitive.Unfortunately, the old model of training courses doesn’t really seem so useful to me. The content is not what I think we need. So for me it’s about building this knowledge within the bank. It’s about creating networks that bring new technologies in and finding ways to foster the spread within our IT function.
- We’re lucky at Kiwibank, nearly all the technologists are located on one floor. We’re not split across multiple buildings. The most important thing for us is to work together.And there’s two fundamental approaches: eitherpush down control through processes, tools and enforced planning and documentation, or work with the individuals creating the future to ensure it’s right.This language shouldn’t seem strange. It should sound familiar.
- Because it’s straight out of the Agile Manifesto.With our co-location we’re set up for an agile approach. We don’t have staff spread across different countries, different timezones, different languages. We’re all within walking distance of each other so it’s important we capitalize on it.The key technique that Agile uses in software to make sure you’re on track is to prefer working software over theory. In the software development world this is all about unit testing and iron out bugs early with short release cycles.When it comes to technology governance you can take the same approach.
- Why are we doing xyz? Recognise the missing gap of the context – which is what we need to strengthenThese are my observations.We’re we’ve done best is when 4 things have happened.We’ve given something a go quickly – not over a year, not in a big programme, but instead in a brief experiment.With the backing of a business team that aren’t scared to get into the nitty gritty detail (ie not focused on vendor packages)In association with a committed, capable technical leadThat ensures we all work together.We have examples: Our approach to AIRB Our approach to refactoring InTouchWe’ve recently done some analysis on a system that we use within our sales & service function. It consists of a lot of code and it’s been running for a long time. This is an example of a lesson learnt – it’s not all rosy. We deployed quickly and got early benefits but over the years we’ve transitioned the lead development role across many people and what we’ve found is that context for technical decisions had been lost, business driven change had then resulted in lots of poor implementation decisions and the code had degraded.To address it we’re doing two things. We’re spending some money on refactoring but the key thing for me is that we’ve really identified that the lead developer needs our support. She has to have the mandate to make deeply technical decisions and to ensure that we do it right in the future. I don’t want to have to transition to another technical lead and undergo the same discovery process all over again.And respect the importance of individualsAny examples?
- Next problem is capability development.Wehave 3 technical forums at Kiwibank.The Architecture Briefing Forum is the longest running and most attended. The Enterprise Information Forum is still clarifying it’s role but it’s become apparent its main focus is on data management. The Analytics Forum is new. It’s become clear that the topics the analysts want to discuss aren’t really applicable to the others and there’s enough content and interest to make something of it in isolation of the others.As a case study the ABF is fascinating. It’s on Friday mornings – so one of the most relaxed times of the week. Topics are varied – let’s look at a few recently.
- Participation occurs across all groups, but realistically there is a much higher participation from the infrastructure delivery team.Personally I like the developer focussed material but I have to admit it doesn’t tend to keep the audience so I can see another spin off in the developer space happening as we grow.The clear message is that the forum provides a fantastic opportunity to socialise ideas, technologies, new ways of working.
- Which brings us to the last challenge.Delivering value from data.We’ve all got large business intelligence teams right?We’ve all got large sources of corporate data?And you’ve heard of big data, and business analytics, but you’re wondering what it’s about and how to get there?Well me too.I’m fascinated by data. My background in science was a while ago but I think this part of it must be in my psyche. I feel happy manipulating information to test ideas or discover relationships.What’s clear to me is that deriving value from analytics requires investment in our underlying data infrastructure.Data PlatformInfrastructure heavy skill baseHigh cost to manage over timeKey challenges today are distributed data management, de-duplication, backup, archiving, IO, tieringEnterprise Information PlatformThis is a big problem area. Classic BI/MIS/EI – it’s all about data extraction, transformation, aggregation, management into playpens and reporting. Big problems today are volumes, lineage, impact analysis, master data and metadata management.Analytics PlatformThis is about catering to analysts – don’t get in the way, instead help them to do good work. Recognising that algorithms are diverse and implementation technologies suited to those algorithms are also diverse. Classic tool sets like SAS are now supplemented by tools like R and Hadoop. In this realm code is ever present – you can’t do analytics if you’re not going to accept having and doing code.The question is… what’s IT’s role in this environment?
- Unstructured business data.Clearly IT has a major role in storage.Virtualised storage layer that underlies our server infrastructure.Critical component of our Private Cloud infrastructure as it underlies our server virtualisation environment.It’s a lot of shared storage.http://mozy.com/blog/misc/how-much-is-a-petabyte/1PB is 13.3 years of HD-TV
- What’s the story?What’s all this exotic technology about.Everyone will presumably be familiar with the big database brands like DB2, Oracle and SQL Server but have you heard about these?The thing is this stuff is new and it’s what’s driving the internet scale application.Facebook, Pinterest, Instagram, Twitter – these apps don’t use big brand databases.Instagram – EC2Twitter – FlockDB (distributed fault tolerant graph database)Facebook – Memcache (9000 instances), MySQL atomic storage (4000 shards) according to http://gigaom.com/cloud/facebook-trapped-in-mysql-fate-worse-than-death/Pinterest – EC2So, OK these are obviously useful technologies when operating at internet scale. But do they have a place within a bank in the NZ retail market?Actually, yes. There are plenty of sensible deployment scenarios where these technologies can supplement our predominantly SQL Server database environment with features that address edge cases.Here’s one: distributed configuration management for which we use RavenDB.Here’s another: message auditing within our service infrastructure.And yet one more we’re investigating for the future: a customer communications document store. The current solution based on a relational database has worked well up till now but the immense size of our customer comms now, and obvious size of the infrastructure means we’re now looking for a new approach. Document stores are one of the most attractive use cases for nosql – you have the potential to get a high performing, resiliant database built on commodity hardware.
- And clearly supporting enterprise information and all required databases and tools.
- But with analytics it’s not quite so obvious. The analytics platform is characterised by a greater diversity of technology and tooling than any other functional area in IT.We have S+ for model development.We have SAS for model implementation and reporting.We have Excel and VBA.We have .NET apps.We have R, the opensource version of S+, favoured tool of the NY Times infographics department and 500 google statisticians (http://blog.revolutionanalytics.com/2012/07/another-r-mention-in-the-nyt.html, http://blog.revolutionanalytics.com/2012/07/applications-of-r-at-google.html)We have SQL Server including the data mining extensions embedded in SQL.We have Predixion Insight in use by the marketing analysts which provides an easy to use interface to SQL Server data mining via Excel.This is complicated stuff!And it get’s much more complex as data volumes increase and we have to split calculations across many machines. The classic example of this being Hadoop.It’s not easy to reduce that diversity. It’s there because invariably you can’t get everything you need from one tool. There are fundamental reasons for this. Data storage is a big one. Excel, R, S+, any RDBMS – they’re all optimised by design for smaller data sets. For larger data you typically need a different approach. SAS gets you part way and then you’re into the world of big data for which vendors are now producing packaged tooling.
- OptimisationSometimes called prescriptive – defining the best course of action for the future; always associated with a need to maximise or minimise an outcome; often time or profit.Classic optimisation problems are:Portfolio management to maximise returnScheduling to minimise costUnderstandingThe discovery of connections, the uncovering of relationships. What attributes influence an outcome. Attributes that often associate together (aka market basket analysis). Clusters of results.PredictionPredicting future outcomes based upon history. When x and y equal these values then the result is always this value.
- A lot of technologies. A lot to learn. These systems all have domains within which they excel so you can’t just rely on one tool.
- Most of the analytics software is pretty much free.But when you start processing larger quantities of data, especially when the number of potential variables goes up then resource requirements can grow enormously. The great promise of big data is associated with the potential for great cost and great infrastructure.Let’s make it tangible: text mining email communication. My email amounted to 1.1GB last year (2011). Assuming 10kb/msg that’s roughly 115,000 messages. To start mining this I need to construct a matrix with 115,000 columns and the row count will be whatever number of distinct terms there are – commonly over a 100,000 for a corpus of documents this size. This is too big for a workstation. A fairly powerful workstation can process maybe 10,000 short documents in a practical amount of time. So you need to split the task up across many machines.Hadoop is the archetypal example: an opensource framework for splitting aggregation type tasks across many machines but it’s complex to implement and it’s not easy to use.Vendors like IBM, SAS (and soon Microsoft) are making this much easier but there’s nothing out there that makes these tasks magic. You need math grads to make sense of it.And for the infrastructure you really have to turn to the cloud.
- Hmmm, not sure when I changed that setting but I soon discovered it.
- This is the result of me running through a text analysis problem on a cloud service from Microsoft called Cloud Numerics. It was for a text analysis problem.What I did wrong here was I left it running. User error – decommission the cloud infrastructure you assign or else you’ll get charged.In fact, in this case if I’d de-provisioned the infrastructure once the job had come to an end the cost would’ve been $0.So firstly, be careful using cloud infrastructure but secondly, if you use it right it makes a lot of financial sense for analytics workloads.And actually, for these problems it’s very secure – if you do it right, most of these problems can be set up such that you’re not exposing any potentially sensitive data and the data governance issues over cloud ownership can be avoided.
- A few weeks ago during a vendor presentation one of the business team mentioned sentiment while we talking about that vendor’s data products. Sentiment’s a hot topic on the internet at the moment, if you search you’ll find many examples and the approach goes basically like this: score your text based upon a list of known words or phrases with previously derived sentiment scores.To get the previously derived sentiment scores you have a few options. You’ll find freely available word lists online, or you can construct your own. You can score the terms from a –ve number to +ve number or you can just create two lists: one of negative words; and one of positive words. One way to get this yourself in more automated fashion is to leverage customer product reviews, again these are also available freely online from a variety of sources. Keep in mind that regional phrases/terms will have a bearing on the analysis so in our case I accounted for a few common NZ terms.Applying my list of positive and negative sentiment terms against Kiwibank’s online relationship manager messages gave me a reasonable response straight away and we could clearly correlate low points and high points. I can’t show that here but I can show an equivalent exercise performed for public tweets off Twitter that reference @ASBBank, @BNZBank and @KiwibankNZ.
- A few weeks ago during a vendor presentation one of the business team mentioned sentiment while we talking about that vendor’s data products. Sentiment’s a hot topic on the internet at the moment, if you search you’ll find many examples and the approach goes basically like this: score your text based upon a list of known words or phrases with previously derived sentiment scores.To get the previously derived sentiment scores you have a few options. You’ll find freely available word lists online, or you can construct your own. You can score the terms from a –ve number to +ve number or you can just create two lists: one of negative words; and one of positive words. One way to get this yourself in more automated fashion is to leverage customer product reviews, again these are also available freely online from a variety of sources. Keep in mind that regional phrases/terms will have a bearing on the analysis so in our case I accounted for a few common NZ terms.Applying my list of positive and negative sentiment terms against Kiwibank’s online relationship manager messages gave me a reasonable response straight away and we could clearly correlate low points and high points. I can’t show that here but I can show an equivalent exercise performed for public tweets off Twitter that reference @ASBBank, @BNZBank and @KiwibankNZ.
- Text mining is the common term for the a process of uncovering word relationships to create an understanding of topics. The more technical term would be semantic analysis. The even more technical one is Latent Semantic Analysis.The basic idea is to count the frequency of terms in documents and understand what terms associate together. Hopefully the associated terms will correlate to an obvious topic area and you could imagine a banking topic to be maybe ‘customer analysis of home loan deals’, or perhaps ‘a customer has a problem’ and so on…This is a hot topic itself so when you start searching on the net you’ll find a number of articles. Again R examples abound – particularly from the R text mining package ‘tm’ but also from products like SAS which has a text mining module.As a process the steps are roughly: Collect the textRemove stopwords – the words of no valueDeal with any synonymsTransform related word forms to a common format egrunning and run mean much the sameRun the algorithms that decompose the topics.The algorithm side is both complex (singular value decomposition) and a numerically intensive task. This is classic big data territory where splitting the work across machines becomes useful. However, we can make slow progress on a single workstation if we keep to a few thousand small documents. Let’s use Twitter again.Let’s now look at Tweet topic
- Note that on my screen these connectivity plots are interactive so I can drag words around and re-organize as seems sensible.Next steps could be … Classify/predict eg emotional state: happy | sad | angry
- Maybe look at online purchasing usage in regions versus cities? We can see these transactions by virtue of attributes like reference numbers, card merchant names etc.Let’s normalize for regional population.What do we find? Online buying is much more important for rural regions especially the south island’s west coast and southland. As for Malborough… I have no idea.
- A random sampling of our transactions from a day earlier this year colour coded to indicate the value of the payment and plotted across great circle arcs. This is a clever little trick that comes from the R community and was famously applied in the facebook IPO.
- And if you don’t want to put the effort into figuring this out yourself – you can get others to do it for you.What you see here is the homepage for kaggle – a site that supports competition driven analytics. You can see an example at the bottom of the page; $100,000 up for grabs to develop a scoring algorithm for student examination with 151 teams competing for the prize.
- Strongly advise going to whatsmynumber.org.nz and following the Electricity Authorities advise.They predict average annual benefits of $165 but it seems to me that many of our customers could gain $15/month or $180 annual benefit.