Enterprise Data Management
The insights below provide expert opinion on the latest challenges, trends and research findings in the area of Enterprise Data Management.
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Semantic Web and Big Data: Moving past the Relational Data Model
John Randles CEO Bloomberg PolarLake
A common complaint from buyers of Data Management Platforms is they have difficulty differentiating between competing commercial Data Management offerings. This is often down to vendors saying the same words and competing along the same lines. For example the “the 25,000 fields in my data model has a home for your entire set of Vendor Data” pitch is often used as is the “my list of adaptors is a superset of all you will need and longer than my competitors’ list”. When everyone says the same thing, offer what seem to be the same solution and have the same number of clients then buyers become frustrated.
Tackling the Inherent Conflicts of Dealing with Commercial Reference Data Models
John Randles CEO PolarLake
The common thinking about implementing a Reference Data Management system is that there are two approaches: build your own data model or buy one from a commercial vendor. The premise for buying a commercial data model is that the commercial data model is a superset of all the fields, attributes, relationships, vendor adaptors etc. of the client’s requirements to make implementation faster than building it yourself. The data model will then be maintained and upgraded by the vendor as new feeds, asset classes, relationships, identifiers and downstream systems need to be supported. All sounds good!
Has Reference Data outgrown the humble Data Model?
John Randles CEO PolarLake
What would be the outcome if you commissioned a group of traditional Reference Data Management experts to build the World Wide Web back in the early 90s? As a renowned expert in Data Modeling there is nothing for it but to build a Data Model, identifying all attributes and relationships to be used for the sum of experiences demanded by the user. You have gifts as a Data Modeler your peers can only dream of. And who cares if you end up with 25,000 attributes in your Data Model – you understand them far better than any mere mortal could and are determined to show your mastery of modeling tools, CERN will be impressed. You would tell CERN as long as they put in place the right “governance” process all will be well.
Do you have a Data Jail in your Reference Data Management Infrastructure?
Cliff Cunningham, Director Product Marketing PolarLake
A Head of Enterprise Data Management at a large Financial Institution recently expressed frustration at their existing Reference Data infrastructure behaving like a Jail for their data. It was an intriguing analogy and an accurate description of the experience of a lot of Financial Services firms on how they interact with their data. In what other area of a Bank’s operation would a valuable, expensive commodity, useful to the business in all aspects of the operations be so difficult to consume? Can I easily interact, view, measure and monitor my Reference Data resources as I need to as a manufacturing outfit monitors its physical supply chain?
The Role of Security in Reference Data Management
Warren Buckley, Chief Technology Officer, PolarLake
If the financial crisis of the last few years has taught us anything it has been that control and transparency in reference data management are now even bigger issues for anyone involved in operations and IT than they were before the crisis. While there are a huge number of stories relating to unsecured client account information, mostly within Retail and Consumer environments, what about the control required for non client account data or reference data as we know it? In the post Lehman Brothers world of control we have seen in the market a massive focus on who in a firm gets access to all sorts of reference data, largely seen as not worth the effort to control in the past. Why is the trader in the equities division requesting massive amounts of fixed income pricing data? Why is the commodities analyst requesting settlement instructions for financial institutions? And why is all data on all asset classes being sent to all downstream systems, leading to a reference data security free for all?
Reference Data Supply Chain Management is Here Today
John Randles, CEO PolarLake
Back office efficiency is top of the business agenda in most Investment Banks and Asset Managers driven by risk management and regulation demanding faster delivery of data into downstream systems. With that our clients are reporting downstream consumer getting much closer to the data, more specific on what they want and the timeliness of the delivery.
Aite Group recognises PolarLake’s Reference Data Distribution Leadership
Cliff Cunningham, Director Product Marketing, PolarLake
Aite Group, a leading independent financial services research and advisory group, has recognised PolarLake as the closest in the market to an “off the shelf” Reference Data Distribution solution.
Reference Data Distribution – A special case in Business IT alignment
John Randles, CEO PolarLake
There is no end to the amount of literature in the world of IT, Business Consulting and the Harvard Business Reviews of this world on the age old debate in how to align business and IT. But how relevant are these works to the Reference Data world? When it comes to Reference Data we see it as a very peculiar case. This is because business requirements are so distributed across the organization and so many stakeholders that are peers of each other.
Why ETL just isn’t good enough for Reference Data Distribution
Warren Buckley, CTO PolarLake
ETL as a concept sounds very well suited to the world of Reference Data Distribution. Extraction, Transformation and Loading all sound like good things to do when distributing Reference Data. This is why a lot of the ETL vendors show up at the usual Reference Data conferences. It all sounds logical. And lots of Financial Institutions are using it for Reference Data Distribution, usually because there is an available license and it is notionally “free” to use. This all sounds straightforward?
Reference Data Integration Goes Prime Time
John Randles, CEO PolarLake
In this time of massive industry crisis why should Financial Institutions be concerned with Reference Data Integration? The answer is simple: cost and risk, everybody’s focus right now.
Why Reference Data Distribution is Critical to Successful EDM
John Randles, CEO PolarLake
There has been a massive surge in the amount of data fund managers have to deal with. This is a result of globalization, the massive increase in trade volumes, more complex asset classes and the mix in asset classes, among other things.