EITN’S FSI ROUNDTABLE Pt.1: End-to-end views of big data

By Charles F. Moreira

The term “big data” is one of the most bandied about buzzwords in an industry. A well-known, flamboyant CEO of a big technology company has even once described big data as more fashion-driven than women’s fashion.

Whilst there seems to be no clear consensus on what the buzzword exactly means, Wikipedia describes big data as, “A collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications.”

On 14th May, 2013, Enterprise IT News hosted a round table at G Tower in Kuala Lumpur, where panelists from the banking, insurance, equities, software solutions, data centre and telecommunications industries shared their opinions and experiences of the challenges and opportunities for big data in the financial services industry (FSI).

Pre-event: The stage at GTower’s, Bridge Bar on the 28th floor

However, even they did not concur on its exact meaning. To some, big data consists of very large amounts of structured and unstructured data, while others believe it consists only of unstructured data.

If these other two terms are even more confusing, one of the easiest examples of a structured database is an address book where each field such as name, company, designation, street address, postcode, city or town, state, country, e-mail address, phone numbers, fax numbers, etc are very diligently entered into each relevant field, for easy sorting, searching, filtering and retrieval – like in your mobile phone for example.

Such structured databases have been maintained, managed and processed using relational database management systems (RDBMS) for a few decades now, including the DOS-based dBase III, III Plus and IV from Ashton Tate, which was hugely popular on PCs throughout the 1980s and is still used to maintain membership lists and other databases today.

The two moderators before event (L-R): EITN managing director, Debbie Wang and EITN editor-in-chief Catherine Yong

However, the data contained in email messages, websites, blogs, Facebook walls, Twitter posts,  SMSes, etc., today do not neatly fit into predefined fields or perhaps we might say “data pigeonholes” of relational database systems.

For example, how would one neatly enter a Tweet such as “Bank XYX’s service sucks,” or “Those dudes at ABC Bank gave me really great service and the chick behind the counter is cute,” into the data pigeonholes of a relational database?

However, being able to process and analyse data contained in those Tweets is invaluable to the reputation, marketing strategy and success of banks and many other enterprises and organisations big or small.

Well that’s an example of unstructured data, along with non-textual data such as photos, videos and sound files, all of which are generated in such large quantities every day, that they require massive computing power to process and analyse, massive amounts of storage and massive bandwidth to transport and the demand is constantly growing.

Meeting of minds: EITN roundtables are meant to be a meeting of minds for sharing of experiences and best practices (L-R): Alain Boey, Adrian Lim, Eric Lam and TdC’s Head of Enterprise Sales, Lee Weng Fak

For bankers like Alain Boey, Bank Simpanan Nasional, Senior Vice President and Head of Management Transformation Office big data is unstructured data, including voice and video files, rather than large structured files that already have market tools to handle.

To Dennis Foong, Head of Post Trade Services, Technology & Systems, Bursa Malaysia (Malaysian Bourse) big data is correlation of structured data, and how it relates to external factors such as political sectors and retail trends.

Without further ado, let’s see what the panelists from different areas like data management and data storage, to data security, governance, compliance, reporting to data connectivity and data analytics, had to say about how their respective industry views big data for the FSI sector.

Adrian Lim, Head of the FSI Strategies Business Unit of TIME dotCom (TdC) opined that big data was still in its infancy in Malaysia.

Big data has to be processed into intelligent information for decision makers to act upon and TdC’s role is to provide the pipes which transport that data to the back-end systems for processing.

James Hatcher, Managing Director, Seeburger Asia Pacific said that big data can be both structured and unstructured.

Examples of structured data are credit card transactions, while unstructured data includes videos downloaded by consumers and in service businesses, big data would include an insurance adjuster in the field taking pictures to upload for claims applications and these would be linked to the related structured data, such as the claimant’s records, etc.

Big data is defined in three ways – namely, Human-to-Human and how we users interact with each other; Human-to-System and how users get the information they want quickly; and System-to-System, where big data happens the most and in big volumes generated by different types of interactions.

David Blumanis, Schneider Electric, Vice President for Regional Data Centre Solutions said that data is growing exponentially and big data sees all sorts of data interplaying with each other.

Because of big data, the data centre becomes a point of contention for FSIs because it is expected to be the infrastructure which supports and manages this data.

“Data is dynamic and moving around, as it needs to be ‘close’ to the data users,” said Blumanis. “While, yes, data storage costs are going down, however the exponential data growth is faster, and as the availability  of data becomes critical (and therefore the redundancy around that data) becomes important – i.e. the infrastructure to store, access and manage data.”

Big data forces more areas of automation to make data available, while on the infrastructure side, it means making data collection, and storage management cost effective.

Lloyd Lee, Vice-President of Sales, AIMS Data Centre described big data as data captured from sensors and devices.

“All these data are translated into storage and is processed, and the question for FSI players now is how data is being transacted, all of which requires a stable infrastructure and connectivity,” said Lee.

He sees double-digit growth in demand for storage capacity and power for data processing.

Eric Lam, RSA Asia Pacific, Business Development Director for Identity Protection and Verification believes that big data includes all data – i.e. all forms, current or past.

Big data in FSI’s is often attacked because there is money to be made.

And to get value from big data, Lam replied that the hottest job in the world right now is that of the data scientist.

Nearing roundtable discussion time, the panelist chairs are filled! (L-R): Adrian Lim, Lloyd Lee, James Hatcher, David Blumanis, Eric Lam, Alain Boey and Dennis Foong

The Harvard Business Review describes data scientist as “The Sexiest Job of the 21st Century,” while IBM describes the job thus: “What sets data scientists apart (from legacy business or data analysts) is a strong business acumen, coupled with the ability to communicate findings to both business and IT leaders in a way that can influence how an organisation approaches a business challenge. Good data scientists will not just address business problems, they will pick the right problems that have the most value to the organisation.”

EITN says!
The main offshoot of big data is almost always analytics and insights to act upon, to enhance something or even prevent undesirable events. Analytics is, the most obvious actionable component from big data, so much so, other less obvious, but equally components tend to be overlooked.

For example, data storage, security and connectivity – with exponential growth of data, how do FSIs balance that with increased costs incurred from growing demand for storage, bandwidth, energy and better data governance & security?

Needless to say, big data means different things to different people. And especially when it comes to how different tech vendors have to approach it, to ensure that value could be ‘squeezed’ out of it for the FSI industry.

We explore all that in Part 2.