Paolo Mittiga, data management domain partner at Wipro Technologies, presented the opening keynote at this week’s A-Team Group Data Management Summit (DMS) outlining a future of data management that will focus on the legal entity and real-time access to all data and be underpinned by technologies including semantic logic and dynamic ontology.
Mittiga was welcomed to the third A-Team Group DMS by editor-in-chief Andrew Delaney who touched on the challenges and technologies driving enterprise data management (EDM) and their part in hot topics identified and investigated by A-Team Group including regulation, legal entity identifiers, risk aggregation, valuations and corporate actions automation.
Mittiga reviewed three decades of business and data management technology development noting paper-based business in the 1970s and 1908s, the introduction of OTC derivatives in the 1990s, the need to trade derivatives in the 2000s as the only way to increase revenue and growing demand among bank clients for more information. Technology development encompassed mainframes, relationship database management systems, data warehouses and the development of many silos of data. Soft information moved through conceptual, logical and physical data models to become hard data information in a process of data modelling and storage.
“What we have today is the result of 35 years of technology evolution; a securities industry in which processing is dictated by asset classes and firms have many silos of data,” said Mittiga. “Banking has two key assets, data and people, but over the past few years there have been no policies on data. I have a new acronym for data – Desired Asset Tossed Away.”
When the financial crisis of 2008 sparked the new norm of regulatory pressure, growth in risk management and the need to drive down costs, data management took centre stage in response to the need for high-quality real-time data.
“We worked on perfect data models that took a huge amount of time to build, by which time the market had moved on. We have done this in data management for 30 years, but now we need to move on,” said Mittiga. Looking to the future, he described external and internal drivers for change as well as technologies that can deliver the results.
“Regulatory reporting is changing to oversight, which means regulators can ask for anything they want at any time and a bank’s data fabric must be able to respond. The industry will no longer be a securities industry, it will be a legal entity and client industry. The legal entity is the most important thing. Legal entity and client data must be clean for functions such as client onboarding and anti-money laundering,” he explained.
Mittiga also described a change in the data value chain with bank data aggregation ceding to a data utility, but warned that banks would have to prepare to take advantage of such a utility.
Internal drivers for change will bring down data silos, reshape business and technology to create synergies and reduce cost, encourage a buy not build approach to speed time to market, and foster outsourcing and partnership opportunities that present fixed costs for data support. Data governance, including data quality frameworks, will be key along with reference data consolidation and integration projects that will be critical to data quality and play into activities from client onboarding to valuations.
Turning to technology, Mittiga said: “Data model and store will become data store and model. This is a paradigm shift in which data will only be modelled on request.” To achieve this and create a new data fabric, Mittiga proposed development of a corporate semantic web including a resource description framework and the OWL web ontology language. He explained: “These technologies come from the web, but can now be used for data management. A semantic web describes and tags data without creating a spaghetti model. It is dynamic approach to data.”
Big data is expected to play a part and embrace not only the three Vs of volume, variety and velocity, but also complexity in merging and managing both structured and unstructured data. It will also change how data is managed and stored to provide a value chain from data to wisdom to competitive advantage. The Hadoop framework and tools for processing large datasets in a distributed computing environment are expected to gain traction, along with in-memory databases.
Mittiga said: “Data management is not a project, it is a long journey. The future data fabric will include structured and unstructured data, semantic logic, dynamic ontology, real-time access to all data and data that is easily distributed to consumers.”
Looking a little further out, Mittiga questioned why banks could not be like Google where any question gets an answer. “Bank data is a microcosm of Google data. In a few years time, it should be possible to have one box to enquire about the data of any company and the technology should allow an answer,” he said.