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The Art of Data

5/4/2015

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Picture
Walking to work in Boston’s Innovation District this morning, it was hard not to notice artist Janet Echelman’s new art installation suspended over the Rose Kennedy Greenway in the middle of the Financial District. Approaching, heading east into the morning sun, it is impressive -- a huge net suspended between buildings -- but didn’t really resonate. After walking along the Greenway and looking back at the installation with the morning sun directly on it, the installation became a resplendent web of orange, green and red, floating in the midst of the stone and steel towers of downtown.

What does this have to do with data?

In many ways, data is a mix of components, architecture, science and art. Like the towers and the Greenway itself, architecture is how the components of data are planned, integrated and used on a day to day basis. The science is reflective of using new techniques and tools to construct new connections of information, and the art is what makes new insights come alive in the bright sunlight.

Every firm deals with the components of data, one way or another. Examples include using multiple applications with similar, but different, sources and perhaps integrating the results in Excel.

More advanced organizations have an architecture in place to increase efficiency and accuracy of this data, from centralized reference data stores to data warehouses and data marts, they know the importance of controlling the structured information and providing data governance to ensure transparency.

Cutting edge firms are using Data Science and, yes, art to look at their internal business processes and interaction with the external world to shed new light and creativity on how to better compete. The current generation of business intelligence tools are the sketch pads, paint and easels of this data art movement, but even these tools need architecture and science to really make it come alive.
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Workflow, data and analytics

3/25/2015

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If Warren Zevon were alive today and writing about the state of many companies, he might say “send workflow, data and analytics, the s*** has hit the fan.”

While the theme of “Lawyers, Guns and Money” is not a direct parallel with the sea changes happening due to new technologies and Big Data analytics, the resulting impact of hitting the fan may be for companies that are slow to adopt to new innovations.

Leading companies are looking carefully at workflow processes, creating automated validation checks and repair processes to ensure accuracy. They are looking at how data is stored and accessed across the entire organization, and they are looking at how they can improve the value of that data through advanced analytics.

In the financial services industry, there are many examples that are being broadly adopted, such as reference data management automation, and also Big Data analytics to support marketing segmentation.  New innovations include automating collateral management, improving data governance, streamlining fixed income attribution, and new models for asset allocation, among others. Firms are looking at how they can mix data stored across silos with unstructured data to provide true Business Intelligence with predictive analytics in order to provide new insights on their clients, investment strategies and overall business efficiency.

For companies not thinking about these new innovations, they may claim to be an “innocent bystander…down on [their] luck.”  Fortunately, it’s not too late to start.

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Innovation in Financial Services

3/10/2015

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In his seminal book The Innovator’s Dilemma, author Clayton Christensen famously coined the term “disruptive innovation” to describe how businesses need to adopt new business models and technologies to remain vital, yet are often reluctant to take the initiative to do so. This reluctance is often termed FUD (fear, uncertainty and doubt), but one could also add a “W” and “H” for what and how. Admittedly, FUDWH is an awful acronym, but hopefully the point is clear.

I was reminded of Christensen’s book at a financial services conference a few months ago.  There was a panel of Chief Information Officers at top tier buy and sell side firms discussing “Cloud Computing and Big Data.” Ten years ago, no self-respecting CTO/CIO in financial services would have considered running core applications outside of their firewall. Yet at this conference, as is the case in most businesses, the Cloud was not only accepted, it is considered as an advantage. Some of the benefits noted include a shorter time to deploy, lower maintenance costs, continuous upgrades, and less impact on stretched IT staff. The Cloud FUD is gone, as long as the supplier passes the information security questionnaires.

Then the topic turned to Big Data. There was less FUD and more WH on this topic, and that is the case with many people I speak with. What do we use Big Data for? How do we use it? One panelist suggested downloading some open source tools and playing around with them, which is not a bad idea, but hardly strategic. These new Big Data technologies center on highly scalable real time architectures, structured and unstructured data, business intelligence and predictive analytics. These tools are starting to be adopted in everyone’s marketing department (and CMO’s are taking a lot of the CTO/CIO’s budget), yet have not started showing real impact on the way financial services firms operate. Not yet, at least.

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    Bob Leaper is passionate about advances in technology and new business models.

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