What big data burdens are financial institutions facing?</h3> There are several burdens. Workplaces are chaotic and business suffers from poor information management. Secondly, operations are inefficient due to internal legacy system problems or data velocity issues. There are ineffective operations caused by poor performance of vendors/distributors. Data sources overlap, there are contradictory data versions, and pipes do not fit well with another. There is ongoing combat with non-standard technologies and frequent changes of requirements. To adapt, firms sometimes attempt to retrofit projects as ‘big data’. Frequently, they try a hundred different things and hope some of the ideas may work.</p> What is stopping firms from implementing data changes and how should they improve data management?</h3> They are often stuck with keeping up their legacy systems, while unsure of the dependability of new technologies. They often lack an ecosystem for effective repurposing, reuse, and recycling of data. To alleviate this, at a high-level, firms should setup project management offices to allow a dedicated focus on data management. However, depending on their stage of development, different firms may pursue different paths.</p> There has been talk about standardisation of data, can it be done and will it be helpful in the end?</h3> Instead of standardisation, firms’ focus should be on optimisation. There is a need to balance the costs and benefits for standardisation, as well as preserving IT agility for rapid respond to business changes. Business often changes by the time a common standard is agreed, and putting everything into a central warehouse could be expensive. Standardisation is not necessarily bad, but also imagine if the industry only has one standard. We won’t want to see all users being affected by racked up prices from someone with monopolistic power.</p> How can you calculate returns on big data?</h3> Returns on big data can be calculated and benefits can be reconciled in general ledger level using a job costing method. Return on investment should always be measured for big data and any major IT. projects. Unfortunately, our observed practices for resources deployment and project prioritisation are predominately determined by fierce fights among departments, top urgency from either seniors’ or clients’ requests. But big data is about the ideas. It doesn’t mean expensive hardware with unknown returns.</p> What are the interesting areas of product development?</h3> There are product development opportunities everywhere. The emphasis is on whether business has a game plan to prioritise the opportunities arising from the bigger picture? For example, capital market, retirement, insurance, and corporate finance, in all of these sectors we serve clients and help them design their next-gen systems and re-engineer their value-chain processes These sectors are undergoing tremendous changes and present various opportunities. Some of the opportunities are domain specific, some could be linking up the front-/middle-/back-offices together, and some focused on crossover synergy to build a utility model for the industry.</p> Should enhancing client experience and better fact-based decisions be the end goal of big data?</h3> The end goal should always be about bringing new products to sell, finding new ways to beat competition, cutting costs and reducing risks. Lukewarm service cannot get business-to-business firms to spend on unnecessary purchase. They demand true values (i.e. solving problems) and solid return on investment.</p> </p>