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Many have acknowledged that big data is big and are willing to put resources behind it. We have observed numerous big data successes benefiting Business-to-Consumer (B2C) companies, but what about Business-to-Business (B2B) firms in the financial industry and financial technologies arena? We cordially invite you to complete the following True or False exercise in deciphering true values of big data in the right context of ‘fit for purpose’.
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   © 2014 All right reserved  –  Data Boiler Technologies, LLC. Page 1  of 9     617.237.6111   info@databoiler.com  DataBoiler.com White Paper Big Data Truths and Myths in the B2B Financial Industry Many have acknowledged that big data is big and are willing to put resources behind it. We have observed numerous big data successes benefiting Business-to-Consumer (B2C) companies, but what about Business-to-Business (B2B) firms in the financial industry and financial technologies arena? We cordially invite you to complete the following True or False exercise in deciphering true values of big data in the right context of ‘fit for purpose’.     Please answer True (T) / False (F) / Unknown (?) for the following statements in the context of today’s B2B firms in financial industry or financial technologies arena.   Your  Answer See Our  Answerat 1.   Big data initiatives are for the primary benefit of improving client experience. Page 2 2.   B2B firms are innovators in adopting the latest and most advanced technologies. Page 2 3.   Transparency is much improved because B2B firms have lots of data. Page 3 4.   Veracity refers to perfect exactitude of data in one centralized data warehouse. Page 4 5.   Enterprise architecture projects are prioritized because I.T. knows the big data’s ROI.   Page 5 6.   Tech vendors join forces with each other to seek higher values through big data. Page 5 7.   Big data emphasis on standardization and a single golden copy of security master. Page 6 8.   Data is counter intuitive, conventional wisdom always prevail in product development. Page 6 9.   There are well kept rosters for the # of data sources & how data is used and reused. Page 7 10.   Big Data supports tightening of risk appetite and favor more frequent risk reporting. Page 8 Did you invite your technology, operations, product, finance, compliance, risk, and others teams (including your vendors and distributors) to jointly participate in this exercise? We ask as in all likelihood, a team effort will generally score higher due to a collective thought process rather than from an individual perspective. Calculate your total score using the following:    Add 3 points for every correct answer    Subtract 2 points for every incorrect answer    Add 1 point for every question you answered ‘unknown’  (1 point is for your honesty and cautiousness. Keep in mind there are high sunk fixed costs in technologies investment, we help you discern truths and myths for your big data bet.)    If you did invite others to jointly participate in this exercise, add 5 points to your score because you just overcome one of the biggest challenges in big data theology  –   ‘silos’.     © 2014 All right reserved  –  Data Boiler Technologies, LLC. Page 2  of 9     617.237.6111   info@databoiler.com  DataBoiler.com Okay, let’s compare our answers to yours beginning on the next page.   Warning:  Big Data can tear our conventional wisdom apart and toss away our long-held intuitive thoughts. It opens up new opportunities and transforms the way B2B firms operate in the financial industry and financial technologies arena. You are about to expose to lots of ironies and contrasting ideas  –  that is how we come up with the better strategies. Here the story begins …  1.   Big data initiatives are for the primary benefit of improving client experience.   FALSE  The observed successes in the B2C arena are dominantly in the area of enhancing client experience; a direct transplant of the B2C approach to B2B does not necessarily yield the same benefits. One may argue that everything they do in business is about pleasing clients and it is a universal principal regardless of a firm being a B2C or B2B. Don’t get us wrong as we are always a Champion in the Voice of Clients, but sadly the B2B reality suggests otherwise. We all have seen B2B firms cut back on their service costs because it is not a revenue driver, nor new business development opportunity. Some adopt the mentality of “don’t fix it if it is not broken”. Heighten by the 2008 financial crisis, banks, broker-dealers, and insurance companies of all size are pressured to save costs wherever possible. They replaced high cost in doing relationship management the old fashion way with machines for automated call center solutions, shared services, and outsource many of their mid-and back-office operations. Are these really enhancing client experience or are they streamlining motives to save costs? B2B are result oriented. You cannot lure a B2B firm to spend on unnecessary purchase because they like your warm and fuzzy service. B2B emphasis on value-adding and demand solid return on investment (ROI) for their projects. If big data cannot bring new products to sell, show new ways to beat competition, and cut costs or re duce risks, then what’s the ROI in Big Data for B2B?  Enhancing client experience and better fact-based decision making should not  be the end-goals or “primary” benefit of B2B’s big data projects, but one of the means towards concrete business results. To peruse at Data Boiler’s ability to crystalize  concepts for the Big Picture  approach to Big Data , please visit our website at www.DataBoiler.com for use cases and details. 2.   B2B firms are innovators in adopting the latest and most advanced technologies.   FALSE  B2C retail banking may be the one constantly modernizing their channel distribution technologies from ATM, credit cards, on-line and mobile banking, etc. However the financial service industry in general tends to lag behind other industries for most advanced technologies because dependability always takes priority over innovations. Not only because there are high stake involved in financial transactions, but financial companies handle money or securities on behalf of others that potential misappropriation of clients’ assets deem a significant risk.     © 2014 All right reserved  –  Data Boiler Technologies, LLC. Page 3  of 9     617.237.6111   info@databoiler.com  DataBoiler.com Regarding B2B firms operate in convoluted capital market, retirement, insurance and corporate finance sectors the stake is even higher in dealing with institutional counterparties than the consumers. An unstable network in the B2B sector could cause huge loss to a company, or even have catastrophe effect on the overall market. Each day, B2B firms are combating with fragmented processes and deluge of data from variety sources. The complexity and high velocity of data (especially algorithm trading) in B2B also lead to veracity data problems. Financial engineers in the front office are creative in coming up with new products and/or new investment strategies, but middle-/ back-office are struggled with legacy systems that many are still running on mainframe computers using COBOL, FORTRAN, PL/1, Borland Delphi, Centura, CLIPPER, Clarion codes. B2B firms tend to be skeptical about cloud computing and open information exchanges. They are conservative in choosing technologies that are already stabilized and proven to be dependable. Thus we can conclude that B2B firms are not   using the “latest” and “most advanced” technologies.  B2B firms interested in harnessing the power of Big Data may ask if they need to replace all of their office computers with tablets, latest mobile devices and buy expensive hardware. Upgrades should only be done as needed and the cost of Big Data platform is less than 1% of traditional database. It’s all abou t ideas instead of what expensive machine you own. We won’t lure you to purchase expensive hardware. Check out our website at  www.DataBoiler.com to see boiling hot ideas and use cases. Big Data can be as easy as slicing a piece of cake. 3.   Transparency is much improved because B2B firms have lots of data.   FALSE  Things are definitely faster but not  necessarily more transparent in the cyber era. Risk and Compliance control folks need to take root in deciphering the underlying and have slow-motion frame by frame pictures to see if there is any tolerance or non-conformance. We can understand how non-transparency may have occurred because of financial innovations, rather than firms trying to hide anything from stakeholders or intend to do anything wrong. We are able to help you discern truths versus myths. Take for example the accumulator and convertor process to hold shares in omnibus account making the underlying position non-transparent. This process enables the available of no-load funds to investors. Just that the process to reduce number of accounts on mutual fund record system in rebating the broker/ dealers to offset client transaction fees also complicated the accounting, revenue management and fee billing process in the trade lifecycle. Take another example, the ERISA 408(b)2 and 404(a)5 fee disclosure rules in the retirement space. It won’t be straight forward for plan participants to understand the various categories of fees and revenue sharing arrangements. More importantly, the rules call for accessing values of retirement services versus fees charged. A mere regurgitation of DOL Form 5500 r eports won’t satisfy ERISA’s requirements, there ought to be appropriate benchmarking of investment performance when comparing fees for different investment options.   © 2014 All right reserved  –  Data Boiler Technologies, LLC. Page 4  of 9     617.237.6111   info@databoiler.com  DataBoiler.com A third example is that there are different types of exchange traded funds (ETFs) beyond consider the style or investment category of the fund. An ETF can use full replication, optimization, synthetic swap and other representative sampling to benchmark a certain index. Also, exchange trade products (ETPs) can have different structures such as commodity pool, grantor trust, unit investment trust, mutual fund, or note. There may be security lending income for the ETF and possible affiliation with counterparties. All these represent different risks, tax exposure and other factors impacting the fun d’s performance. Thus not every ETP are created equal. The list of non-transparency in financial market can go on and on, this added layers of administrative burden on the risk and compliance officers. While leveraging Big Data to do pre-cognitive detection of fraud could be the ideal, we have multiple ways to ease your Risk and Compliance  administrative burdens. We are at your service to support your specific needs, please contact us to schedule an appointment to find out more. 4.   Veracity refers to perfect exactitude of data in one centralized data warehouse.   FALSE   Veracity is one of the four ‘V’s in Big Data according to IBM, it refers to “the level of reliability associated with certain types of data”. The task is to “manage the reliability and pr edictability of inherently imprecise data types”. Just that data reliability does not  necessary mean the exactitude of data has to be “perfect”, nor   put everything into one “centralized” data warehouse. According to Big Data: A Revolution That Will Transform How We Live, Work, And Think by Viktor Mayer- Schönberger and Kenneth Cukier, “looking at vastly more data also permits us to loosen up our desire for exactitude … With big data, we’ll often be satisfied with a sense of general direction … we sacrificed the accuracy of each data point for breadth, and in return we received detail that we otherwise could not have seen … we gave up exactitude for frequency, and in return we saw change that we otherwise would have missed …”. This is an awakening call for us to give up devotion to exactitude. Haven’t we all experienced typing not the exact words in Google, but the search engine is able to predict what we are looking for and auto- correct our typos? There’s no sophisticated language algorithm behind Google in correcting our typos. The trick is simply because Google search engine has seen these misspelled words enough times, thus it is able to infer what people are usually looked for the right terms. Nevertheless, big data is often “messy, varies in quality, and is distributed among countless servers” per Viktor and Kenneth. It does not make sense to centralize everything into one data warehouse. It’s like putting all eggs in one basket. Also, by the time a common standard has been agreed upon, the business has changed. Therefore I.T. architecture ought to be agile for rapid responds to market changes. Check out our website at www.DataBoiler.com to see how I.T. Agility Optimization Services can enable Big Data success with a lean and scalable architecture.
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