To roadmap Wall Street priorities for 2013, we have been having an interesting set of meetings recently with MDs and leading architects in global banks and investment services firms. It is clear that banks are devoting more resources to Anti-Money Laundering (AML) and Office of Foreign Assets Control (OFAC) compliance.
Got the scoop on analytics projects they are investing in — Anti-Money Laundering (AML) monitoring, Risk Management, trade surveillance and Know Your Customer (KYC) analytics.
To enable AML and KYC initiatives…the big foundational investments in 2013 are around:
1) Strengthening the Golden Sources – Security Master, Account Master and Customer Master.
2) Various enterprise data management initiatives – Data Quality, Data Lineage, Data Lifecycle Management, Data Maturity and Enterprise Architecture procedures.
3) Regulatory reporting improvements via next generation Enterprise Datawarehouses (EDW) — Reporting on top of EDW addresses the core problems faced by Finance, Risk and Compliance when these functions extract their own feeds of data from the product systems through which the business is conducted and use differing platforms of associated reference data in support of their reporting processes. Lot of current investments are in the areas of Finance EDW which delivers common pool of contracts, positions and balances, organized on an enterprise wide basis and completed by anointed “gold” sources of reference data which ensure consistency and integration of information.
Crawl, walk, Run seems to be the execution game-plan as the data complexity is pretty horrendous. Take for instance, Citi alone….has approximately 200 million accounts and business in 160+ countries and jurisdictions.
The type of data challenges banks like Citi are wrestling with include: Read more
Big Data emphasizes the exponential growth of data volumes worldwide (collectively, >2.5 Exabytes/ day).
Big Data incorporate the following key tenets: diversification, low latency, and ubiquity. In parallel, the emerging field of data science introduces new terms including, predictive modeling, machine learning, parallelized and in-database algorithms, Map Reduce, and data monetization.
A variety of infographics have been published around Big Data, Data Scientists. Here is a compendium of some very interesting ones.
The Real World of Big Data (Click image to see a larger version and article)
|Big Data Big Opportunity||A Data Scientist Study|
“Through 2015, more than 85 percent of
Fortune 500 organizations will fail to effectively exploit big data for competitive advantage” - Gartner BI Summit.
It doesn’t take genius to recognize that there is an increasing demand for information to improve shareholder value and gain competitive advantage by leveraging information, data and analytics as a strategic enterprise asset. The question is no longer about the importance of data but when, how, and where to leverage the asset. Read more
The bleeding edge of insight innovation is around next generation digital consumer experience. Consumer behaviors are rapidly evolving….always connected, always sharing, always aware. Obviously new technology like Big Data drives and transforms consumer behavior and empowerment.
With the influx of money, attention and entrepreneurial energy, there is a massive amount of innovation taking place to solve data centric problems (such as the high cost of collecting, cleaning, curating, analyzing, maintaining, predicting) in new ways.
There are two distinct patterns in data-centric innovation:
- Disruptive innovation like predictive search which brings a very different value proposition to tasks like discover, engage, explore and buy and/or creates new markets!!
- Sustaining innovation like mobile dashboards, visualization or data supply chain management which improves self service and performance of existing products and services.
With either pattern the managerial challenge is moving from big picture strategy to day-to-day execution. Execution of big data or data-driven decision making requires a multi-year evolving roadmap around toolset, skillset, dataset, and mindset.
Airline loyalty programs are a great example of multi-year evolving competitive roadmaps. Let’s look at BA’s Know Me project.
British Airways “Know Me” Project
British Airways (BA) like Delta, United and others has focused on achieving competitive through customer insight. It had accumulated substantial customer information from its Executive Club loyalty program and its website, and has incorporated the data into a customer data warehouse for analysis. BA decided to put customer big data to work in its Know Me program. The goal of the program is to understand customers better than any other airline, and to put the customer knowledge accumulated across billions of touch points to work for the customer’s benefit.
BA’s analytics team is enhancing its big data program with support from Opera Solutions. The airline is using data and applying it to customer decision points with the three pillars to the Know Me program being:
- Personal recognition—This involves recognizing customers for being loyal to BA, and expressing appreciation with targeted benefits and recognition activities;
- Personalization based on irregular disruptions like being stuck on a freeway due to an accident – A pre-emptive text message… We are sorry that you are missing your flight departure to Chicago. Would you like a seat on the next one at 5:15PM. Please reply Yes or No.
- Service excellence and recovery—BA will track the service it provides to its customers and aim to keep it at a high level. Given air travel constant problems and disruptions, BA wants to understand what problems its customers experience, and do its best to recover a positive overall result;
- Offers that inspire and motivate—BA’s best customers are business travelers who don’t have time for irrelevant offers, so Know Me program analyzes customer data to construct relevant and targeted “next best offers” for their consideration.
The information to support these objectives is integrated across a variety of systems, and applied in real-time customer interactions at check-in locations and lounges. Even on BA planes, service personnel have iPads that display customer situations and authorized offers. Some aspects of the Know Me program have already been rolled out, while others are still under development. Early results are very positive; customers have expressed pleasure at BA’s understanding of their air travel needs.
The Need for New Data Roadmaps
New IT paradigms (cloud resident apps, mobile apps, multi-channel, always-on etc.) are creating more and more complex integration landscapes with live, “right-now” and real-time data. With data increasingly critical to business strategy, the problems of poor quality data, fragmentation, and lack of lineage are also taking center stage.
The big change taking place in the application landscape: application owners of the past expected to own their data. However, applications of the future will leverage data – a profound change that is driving the data-centric enterprise. The applications of the future need one “logical” place to go that provides the business view of the data to enable agile assembly.
Established and startup vendors are racing to fill this new information management void. The establish vendors are expanding on this current enterprise footprint by adding more features and capabilities. For example, the Oracle BI stack (hardware – databases – platform – prebuilt content) illustrates the data landscape changes taking place from hardware to mobile BI apps. Similar stack evolution is being followed by SAP AG, IBM, Teradata and others. The startup vendors typically are building around disruptive technology or niche point solutions.
To enable this future of information management, there are three clusters of “parallel” innovation: (1) technology/infrastructure centric; (2) business/problem centric; and (3) Organizational innovation.
Data Infrastructure Innovation
- Data sources and integration — Where does the raw data come from?
- Data aggregation and virtualization- Where it stored and how is it retrieved?
- Clean high quality data — How does the raw data get processed in order to be useful?
Even in the technology/infrastructure centric side there are multiple paths of disruptive innovation that are taking along different technology stacks shown below.
At the Analytics Executive Forum, I facilitated a session on Omni-channel analytics. It struck me how every leading consumer facing firm seems convinced that mobile is becoming the dominant B2C interaction channel. Mobile is the gateway to insight based marketing and the “always addressable customer”….
Insight-based interactions – The company knows who you are, what you prefer, and communicates with relevant, timely messages, using the power of analytical intelligence to detect patterns, decode strands of information and create meaningful offers and value.
The “always addressable customer.” This is a consumer who fits the bill on three fronts simultaneously: (1)
- Owns and personally uses at least three connected devices; (2)
Goes online multiple times throughout the day; (3)
- Goes online from at least three different physical locations
The opposite of insight-based is “spray-and-pray” marketing - The company has very limited knowledge about who you are, forgets what you prefer, and tries to reach you with off-target communications that alienate you – based on fragmented data, poor data quality and inadequate integration, resulting in confusing, chaotic interactions. A good example: “I have 2 million frequent flyer miles with your airline and still do not get any recognition, respect or value from this loyalty.”
As companies architect new insight based mobile use cases I suggest that they look at what is coming next. With IOS 7, Apple is delivering several new features – Passbook, Beacon.
Retailers, banks and other customer facing firms/brands better pay attention. 100+ million iPhones are automatically getting this feature with the new OS upgrade making this a mega-disruptor in the coveted target segment everyone is chasing. Read more
Consider this…eBay’s “Singularity” Teradata warehouse exceeds 40 petabytes. According to eBay, the company’s data volumes are 50+ terabytes per day in new incremental data, processing 50+ petabytes
and tens of millions of queries per day, with 99.98% availability and more than 50 petabytes of online storage.
Data is valuable. Data is plentiful. Data is complex. Data is in flux. Data is fast moving. Capturing and managing data is challenging.
So, if you are a senior leader in a Fortune 2000 company. How do you structure your group to deliver effective BI, Analytics or Big Data projects? Do you have the right structure, toolset, dataset, skillset and mindset for analytics and Big Data?
Organizing for effective BI, Analytics and Big Data is becoming a hot topic in corporations. In 2012, business users are exerting significant influence over BI, Analytics and Big Data decisions, often choosing analytics and visualization platforms and products in addition to/as alternatives to traditional BI platform (reporting and visualization tools).
Interested in slicing, dicing, measuring, and analyzing data for customer and business insights?
According to a recent survey by Bloomberg, 97% of companies with revenues of more than $100 million are using some form of business analytics, up from 90% just two years ago.
While businesses have embraced the idea of fact-based decision-making, a steep learning curve remains. Only one in four organizations believes its use of business analytics has been “very effective” in helping to make decisions. Data is not just ignored but often discarded in many organizations as the business users can’t figure out how to extract signal from data noise.