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20 Experts Reveal the Most Important Big Data Technology Trends Shaping Banking and Finance Today

Financial institutions are making use of Big Data in big ways, from boosting cybersecurity to reducing customer churn, cultivating customer loyalty, and more through innovative and personalized offerings that make modern banking a highly individualized experience. Data drives the modern financial industry in many ways.

But what specifically are these trends in Big Data technologies that make such experiences possible? What trends are today’s financial services professionals tapping into to set themselves apart from their competition? And what role do modern financial services pros see Big Data playing as we move into the future?

To gain some insight into the ways that Big Data has transformed and continues to shape financial services, we asked a panel of financial services professionals to answer this question:

“What are the top trends in Big Data technologies in the banking industry?”

To learn more about how Big Data shapes banking and other financial services today and how the industry may change in response to Big Data’s continuing evolution, read what our experts had to say below.

Meet Our Panel of Financial Services Data Pros:


16_someshwar_chiduralaSomeshwar Chidurala

Someshwar Chidurala is the Digital Marketing Analyst for Orchestrate Technologies, LLC, a U.S. based business process management organization with headquarters in Dallas, Texas.

“When it comes to Big Data initiatives, most banks continue to focus on…”

Qualitative analytics that will alleviate risks and optimize the forecasts on customer intelligence. However, one of the primary developments can see Big Data platforms getting profoundly integrated with conformance policies like Data governance, and compliance mandates could become more manageable.

Banks may also experiment with leveraging the data from IoT to be utilized in their mobile-banking activities. Specific applications built to exploit Big Data can also lead to optimization of portfolio management and overall retail-banking functions.


15_kris_st-martinKris St. Martin

Kris St. Martin is Vice President of Insurance and Bank Program Director at CBIZ.

“The biggest data trend in banking is…”

The constant threat of cyberattacks.

Cyber Risk is no longer simply an IT Issue. Cybercrimes are costing the global economy nearly half a trillion dollars a year, according to the insurer Allianz. The persistent threat of Internet attacks is no longer simply an IT issue; it has become a business issue facing all industries, especially the financial services industry.

Criminals are constantly finding new ways to take money and data from banks and customers through fraud and cybersecurity vulnerabilities. One common example occurs during online bank transfers. When a customer accesses their online bank account, the hacker may intercept the user name and password and within seconds transfer money to an overseas account.

To keep up with cybersecurity prevention, financial executives should ask themselves the following questions:

Train Customers – Are you providing training to your customers prior to their using online banking? A Customer lack of dual control and inadequate network protection can open the bank to liability over a loss.

Take caution – Should employees be permitted to use personal devices to connect to the network? It could inadvertently open the bank to additional risks. Institute a cybersecurity culture, coming from the board down, and integrate cybersecurity into your enterprise risk management (ERM) program.

Evaluate staff – Do you have the proper skills and expertise on staff? If not, secure them through hire or consultancy. This could make or break your entire security system.

Explore cybersecurity insurance – Is your cyber insurance policy up to snuff? Consider your cybersecurity insurance requirements and coverage as you respond to a breach. Cyber liability insurance is not standard and can come with procedure requirements and exclusions of coverage. Knowing your insurance will help avoid claims being denied.


14_sheila_lindnerSheila Lindner

As President of Canada’s foremost provider of high quality document management and business process outsourcing solutions, Octacom, Sheila Lindner oversees a fantastic team of people. She strongly believes that their employees come first, and that their quality people will guarantee quality services for their clients.

“The most interesting trend will be whether banks and financial services providers succeed in their mission to…”

Leverage Big Data while adhering to compliance requirements and security standards.

At present, these considerations are not sufficiently integrated into existing data platforms, which is increasingly problematic. Beyond this initial approach, we will see a trend of using Big Data to identify and patch vulnerabilities, thereby reinforcing and securing all matters of data governance, lineage, and compliance. The data platforms and solutions that address this priority while maintaining flexibility and scalability will be most successful.


13_brian_langeBrian Lange

@DsAtweet

Brian Lange is a Data Scientist at Datascope Analytics, where he leads design process exercises and designs analyses, web interfaces, and visualizations. He has a background in Computer Engineering and design from Northwestern University. Brian has contributed to projects for P&G, Thomson Reuters, Motorola and other well-known companies and his work has been featured on FlowingData.

“One of the biggest trends having an impact in banking today is…”

We’ve seen a lot of interest in training analysts and sometimes managers in these open source data science tools. For the analysts, in some cases the tools are necessary to do their jobs, because they’re replacing older proprietary solutions. Languages like Python and R give analysts many of the out of the box capabilities of statistics-specific tools while also giving them more freedom because of the number of community-developed libraries available for these platforms, and the fact that they’re designed to be more general purpose.

Depending on the types of problems analysts are working on, the sheer amount of data at their disposal makes using a Big Data tool like Spark attractive. As Big Data systems go, it’s quickly evolving these days, and getting a lot of attention and support from companies big and small.

As for the managers taking these courses, they want to increase their data literacy; to be able to know what’s possible, and speak the same language as their analysts.


12_george_popescuGeorge Alex Popescu

George Alex Popescu is the founder and editor in chief of Lending Times.

“The top trends in Big Data technologies in the banking industry are…”

A lot of banks are partnering, building or buying systems to allow them to take credit decisions on loans within 5 minutes. They are doing this using Big Data. They have to do this because of the competition from the alt lending space.

 


10_ryan_naudeRyan Naudé

@RyanNaude

The past several years have seen Ryan spearheading the data solutions division at Entelect. This role encompasses the management of everything database, business intelligence and, more recently, Big Data related. Ryan currently manages a team of engineers, who provide value, insight and intelligence on top of organisations’ line of business systems.

“In the banking industry today, we need to look at…”

The traditional model vs where we are going. Let’s take a traditional ETL. Traditionally data is pulled, mostly every night, it is then transformed in many different ways and disseminated to a well-defined and thought out structure. After that, algorithms for predictive and prescriptive analytics are run and one or many presentation layers are used to provide access to the data to end users. These traditional methods unfortunately don’t let you pivot quick enough to business demand, real time analytics are very limited and in the end these data warehouses become white elephants that serve a very specific purpose.

So what are the trends? I’m seeing a big shift from scheduled data loads to the ETL being a push from the source systems. The system pushes the data real time into a staging environment. From a technology point of view the big vendors have caught onto this and are all coming out with mechanisms to provide the service bus type approach. Stream analytics from Microsoft is an example of this as well as several technologies in the Hadoop ecosystem. I am also seeing a trend toward the sandbox type environment, data is pushed into a data lake, for lack of a better term, where users then query the data and provide structure to it. The theory behind this is sound, if you have 200 source systems trying to work out customers in each of those systems before being given access to the data is a massive undertaking, whereas with the data lake/data vault/sandbox approach users can just link up the data they need at the time they need it. This doesn’t satisfy the statutory and regulatory reporting need, but it does allow faster insights to be gained from data not necessarily required for those reports.

In terms of actual technology, I’m seeing a shift to cloud for very limited data sets. We want to leverage cloud for the power it can provide, but we are still not in a position where we can put all our data in the cloud, in South Africa at least. I’m seeing a shift to the streaming type ETL tools. About a year ago everyone was quite desperate to get onto an unstructured data platform, this doesn’t seem to be the immediate need anymore but more the sandbox environment regardless of technology.

I feel there is still a lot to be exploited in the unstructured data world, facial recognition to provide better customer service, voice stress analysis to monitor customers and staff in a call centre environment, sentiment analysis still has a bit to go and social media analytics hasn’t reached maturity yet.

In short, I think all the major vendors are constantly challenging each other with functionality and the trend is more toward how to tighten up all the different technologies to work together to provide the magic insight, and less toward which technology you actually choose.


09_sara_steinbaurerSara Steinbauer

Sara is an Information Systems professional at Nelson with over 15 years of experience in Project Management and Solution Development, specializing in industry best practices (BOMA, IFMA, OSCRE) business process analysis and enterprise systems and integration and optimization. Sara possesses proven experience in effectively managing projects as well developing technology-enabled process and workflow automation solutions that have significantly improved business efficiency and effectiveness.

“In the banking industry, one of the biggest trends in Big Data technology is…”

In real estate management, banks are using Big Data to analyze consumer/client demographics against lease trending more and more accurately to position branches, kiosks, and other service offices in key locations to maximize business including pop-up banks.


08_paco_darceyPaco Darcey

@clutch_co

Paco Darcey is a Business Analyst at Clutch. He graduated from the University of Richmond with a B.S. in Mathematical Economics and currently heads the BI & Big Data research at Clutch. In his spare time, Paco enjoys playing basketball and rooting for the San Antonio Spurs.

“Many of the banking clients that I’ve spoken to have emphasized the importance of…”

Security. Banks have migrated many of their legacy systems to the cloud relatively recently, and their employees need to be able to access a large range of data repositories on a regular basis. As you can imagine, this creates a unique set of challenges. It is extremely important that clients’ privacy concerns be taken into consideration, so identifying and preventing potential breaches is always a high priority.

Data integration and compatibility are extremely important in the banking industry as well. Many banks are choosing to work with Big Data consulting firms to build software tools for things like data visualization and extraction, for example. These types of projects involve a deep understanding of Big Data space, not only of the technologies but also how they interface with each other. It can be tricky to find the right subject matter experts who have experience with financial data and also the skills to work with a variety of Big Data tools. However, most banks are willing to spend the time and resources necessary to do so, considering the size and scope of the solutions they need to create.

Lastly, I would add that advancements in machine learning are a big part of the Big Data picture in banking. With constantly improving algorithms and an ever-expanding availability of structured and unstructured data, banks are able to improve their efficiency in terms of risk management, fraud detection, marketing campaigns, etc. These are only some of the prominent areas where Big Data has benefited the banking industry, but I think in the near future we will see it continue to add value in some newer, perhaps less intuitive places.


07_carrie_mcilveenCarrie McIlveen

@Metia

Carrie McIlveen is the U.S. Marketing Director at Metia. Carrie has over 18 years of experience in business, product and brand marketing. A majority of her career was at Washington Mutual Bank with the most recent position held as VP of NW Channel Marketing. At Metia, she actively manages companies branding, marketing campaigns, public relations, awards submissions and social media platforms.

“Some industries are notoriously slow to catch on to new technologies for a variety of reasons…”

That’s changing fast in the financial service industry, which has embraced digital marketing and the benefits (and revenue) it can bring. To deliver an engaging and consistent presence, banks are starting to join the social conversations online to enhance the customer experience and understand their clients better – which can result in lifelong brand advocates.

To do this effectively, the new trend is banks using data insights to enable real-time contextual relevancy so they can contribute to their community. This allows them to connect with audiences in new, fun, personalized, and educational ways. They can utilize digital data to monitor customer and market trends to provide value-driven, personalized messaging and tailor customer service to each individual client. By doing so, they can listen to their audience and engage with them in genuine two-way conversations.

With the rapid growth in mobile use and the accessibility of social media through smartphones, it’s critical banks continue to transform by using data and insights to connect with their clients. Across the world, financial institutions are investing heavily in mobile-friendly apps that make it easy for clients to conduct banking transactions while on the go. The result is rewarding – adds a deeper, value-added service for bank clients and provides a competitive edge for banks.

Banks are also continuing to look ahead at future innovations and technology advances to strengthen security, regulation compliance and improve fraud detection and prevention.


06_kirk-chewningKirk M. Chewning

@kirkchewning

Kirk M. Chewning attended Michigan State University, graduating with a bachelor’s degree in finance from the school’s Eli Broad College of Business. Along with his professional duties, he is actively involved in volunteering his time by teaching corporate skills to college students throughout North America.

For nearly 20 years, Chewning has been involved in the financial services industry. He is currently a partner and Co-CEO of Cane Bay Partners VI, LLLP.

“Big Data technology has become an integral part of the banking industry over the past few years, and will continue to drive innovation…”

Big Data gives companies an incredible volume of information and records at your disposal. Where banks and financial institutions might have tracked performance and metrics on a branch-by-branch or region-by-region basis in the past, the more advanced companies now have access to all their records, simultaneously.

That means they can build more accurate models of customer behavior and set proper pricing for loans and financial products to optimize profits and align the right products with the right customers. Multi-variant data analytics is key in decision making and particularly when you are testing a new hypothesis. Big Data can also help define a baseline for ‘normal’ operations, which gives companies a head start in detecting fraud and helps managers spot compliance and regulatory issues before they become a problem.


05_priyanka_prakashPriyanka Prakash

@FitSmallBiz

Priyanka Prakash is a financial specialist at Fit Small Business, an educational site for small business owners. A former business attorney, Priyanka now helps entrepreneurs understand credit scores, loan rates, and other financial topics. Priyanka has been referenced in Credit Karma, GOBankingRates, Wise Bread, and other leading publications.

“One of the trends of 2016 is the use of Big Data by consumer and business lenders in…”

Underwriting. Up until a couple years ago, lenders primarily looked at loan applicants’ credit scores in determining whether they should be approved for a loan. Now, lenders are using thousands of Big Data points, such as an applicant’s income, social media postings and reviews, length of time it takes to fill out a loan application, and more to evaluate creditworthiness. So far, only non-bank alternative lenders are underwriting using Big Data, but I think we’ll see banks doing the same very soon. Banks like J.P. Morgan Chase and Santander are already teaming up with alternative lenders to utilize Big Data underwriting and expand their customer base.


04_domenic_brooksDomenic Brooks

@El_Rey_Brooks

Domenic Brooks is from Datalabs, a data visualization & analytics agency in Melbourne, Australia.

“One of the biggest trends in the banking industry in terms of Big Data technologies is…”

We deal with the major Australia & Asia Pacific agencies and the trend we’re seeing across all of those that come to us is that they’re moving away from traditional marketing to more sophisticated digital marketing. In particular, moving away from brand focused advertising/marketing and into contextual, content, programmatic and even more sophisticated algorithmic/predictive targeting use through data collection.


03_dominic_suszekDominic Suszek

@globalradar

Dominic Suszek is the Founder and CEO of Global RADAR, a software company responsible for the creation of an Anti-Money Laundering and Terrorist Financing software solution developed to provide financial service providers a comprehensive tool to facilitate client on-boarding due diligence, automated risk rating and transaction surveillance.

The Global RADAR’s team of experts have extensive experience in assisting financial service providers globally. Strategically headquartered in London and Miami, the Global RADAR team members are located in eight strategically placed offices to support client needs across many time zones. Our client base spans many sectors, including corporate, brokerage, finance, gaming, government, insurance, manufacturing, and real estate in over twenty countries.

“Big Data technologies are shaping the banking industry in terms of…”

Mitigating financial terrorism with regulatory risk management with Big Data of late, the business world has been confronted with remarkable changes and difficulties that have compelled the need for many compliance regulations. In the banking sector, these regulations revolve around storage, access, analysis of client data in adherence with the regulatory requirements. The improvement of the information quality for existing client profiles while having effective systems in place to capture necessary information for new clients is ingrained in the regulatory requirements. Compliance issues have penetrated every facet of an organization from the front line personnel, client-facing associates, compliance officers, and management, all the way up to executive officers and board members. The new scenario that sprouts from the trending regulatory requirements: monitoring activity to ensure transactions are in line with new and existing client profiles.


02_tali_sorokerTali Soroker

@I_Know_First

Tali Soroker is a quant analyst at I Know First, a Fintech company that has developed an advanced algorithmic system to predict financial markets and supply Big Data solutions for institutions and large private investors since 2010.

“Financial technology is now a booming industry…”

With investments in the sector rising 75% in the first three months of January alone against the total investment numbers for 2015. The innovations that are being pioneered using these extensive investments are challenging existing brick-and-mortar banking corporations to remain competitive by adopting new systems of information sharing and payment processing platforms.

Bitcoin is a wildly unconventional and controversial form of internet currency that uses multiple APIs, or application programming interfaces, and blockchain technology as a basis. APIs allow for the exchange of data between users and enable them to transfer money (in the form of bitcoins) amongst themselves. The blockchain is then a collection of every transaction that has been made between two users of the Bitcoin system. While Bitcoin is thought by many to be a dying currency, losing popularity and support, open-sourced APIs and the blockchain technology used to document transactions is now the target of new innovation in the banking world.

The EU is hoping to be a leader in financial technology specifically in the area of open banking regulation. EU and UK banks are championing the practice of open bank APIs to allow for the exchange of customer data among banks and third-party applications, with the aim of driving competition and in some cases increasing security. With this new advancement, customers will be able to more easily switch banking services without losing years of deposit and transaction history, forcing banks to remain competitive in order to keep their clientele. This new form of information exchange will also change the way third-party banking apps access customer banking information. Currently, most third-party banking apps use something called screen scraping to access customer banking data, which exposes customers’ account login information and risks the security of their personal account.

Blockchain technology, the technology behind bitcoin transactions, is now attracting the attention of major banking corporations such as Citi, Credit Suisse, and JPMorgan. Last September, these banks along with a number of other high-profile corporations joined a coalition led by a firm called R3 in order to work towards implementing blockchain technology in conventional banking. Microsoft has also teamed up with this conglomeration of banks to provide support and services for R3’s blockchain labs. The potential for blockchain technology in the banking sector is huge. As developments continue, these large banks are hopeful that blockchain will be useful in trading anything from remittances to securities exchanges. Outside of the financial world, it’s possible that this technology will also be useful in advancing current voting systems, vehicle registrations and insurance claims, wire fees, and gun checks.


01_oliver_muhrOliver Muhr

@TeamSeerene

Driven by a passion for transforming the way businesses operate, Seerene
CEO Oliver Muhr has spent more than 15 years in the technology enterprise space. In his present role, he oversees strategic vision and business execution at Seerene, an advanced analytics company that lights up the black box of mission-critical software by visualizing code, applications and teams as 3-D cityscapes.

“The financial industry, and banks in particular, can leverage Big Data for two areas…”

One, they can generate new revenue streams by offering newer and better services to customers, such as mobile, security, etc. And, two, they can become more efficient and lean in order to better compete against the ongoing disruption caused by FinTech.

Banks have become basically software shops, employing thousands of programmers. But, while startups with only a handful of programmers can crank out new, innovative services, banks are unable to, despite having more resources. Banks need to become more agile, quick and innovative as it pertains to software, just in order to keep up.

A top trend in Big Data technologies in the banking industry will be to better read and understand Big Data. They need to optimize the maintenance of legacy applications and use the freed up capacity for new, innovative stuff. Given the massive amount of programmers they have, this would be a huge force and they could change the game to their advantage.


Binny MatthewsBinny Matthews

@dezyreonline

Binny Matthews is the Co-founder and CEO at DeZyre, an  online job-skills platform where people can get project experience in Big Data by working on projects from companies. Previously, Matthews was an Investment Banker at Credit Suisse, New York. He graduated with a M.S in Computer Science from Washington University in St.Louis.

“One of the biggest trends in Big Data technology in the banking industry is…”

The thought of waiting for hours in queues to transact with a teller, sitting with a bank representative who recently joined and has no idea about a particular customer, what his requirements are or what is the best way to serve a customer- leaves customers unsatisfied and disappointed with the financial services of a bank. However, advancements in technology and some highly advanced disciplines like Data Science have the potential to put an end to poor experiences with bank branches.

When people in financial sectors discuss about the banks of the future –they generally refer to external things that banks will have in the future. Highly advanced equipments like sensors and touch screens inside the office furniture that are connected to the IoT, banks will have an appealing and attractive appearance to the customers, etc. However, the reality to the banks of future depends on what will happen internally – the decision making processes in financial institutions will become completely data driven with the extensive adoption of data science discipline in finance.


Jordan HudgensJordan Hudgens

@JordanHudgens

Jordan is the CTO of CronDose.com, an online Computer Science educational platform, he is also a graduate student in the CS department at Texas Tech University where he researches Big Data applications with a focus on machine learning algorithms.

“One of the most significant trends in Big Data technologies for the banking industry is…”

The amalgamation of various machine learning algorithms. In recent history, organizations would decide on a single approach to integrating Big Data processes or technologies, however each algorithm has its unique set of limitations, whether it be performance or accuracy. With that in mind one of the top trends is to combine best of breed algorithms, leveraging their respective strengths and bringing them together to form a more unified approach to Big Data.


David KarpookDavid Karpook

@PayNetOnline

David Karpook is vice chairman of the Open Standards Consortium for Real Estate (OSCRE) and strategic business consultant at Planon. As an industry expert of technology solutions for facility management and real estate, he’s worked projects as a customer, trainer and strategist, managing workplace technology projects around the world. He holds degrees from Harvard University and the University of Florida.

Money and its value are changing at rapid speeds and soon the things we know today will be a thing of the past.

Technological advances including programmable, digital currencies, smart, self-executing and self-enforcing contracts, and publicly available, owner-less block chain ledgers will dramatically change the way we conduct
business. These developments are forming a new layer of the Internet, which is becoming known as the Internet of Money, and this development is a complex system that is changing the way we see, value, think about and even
use money.

The underlying technology that supports this “trustless”, disintermediated transaction system is called Blockchain. It actually has potential to be a lot more interesting than Bitcoin or other digital currencies themselves. Money is about more than just currency. We use money to assign value to goods and services. Money serves as a metric to compare the relative worth of goods, services, organizations, governments, even people. In bartering economies, the “money” exchanged during transactions may not be currency at all, but goods or services considered to have an equivalent value to the good or service provided. Certainly Blockchain has potential use as a system of record for these other types of value exchange.

All of this is just beginning. As as society, we are still in a an exploratory stage when it comes to possibilities for the Internet of Money. There are many legal and ethical questions that have come up and will still arise. For example, despite the self-executing nature of Blockchain contracts, can their outcomes be challenged in court? The answer to this and other questions is that we simply don’t know yet. Governments are wrestling with the question of digital currencies – in the U.S., tax authorities view them as assets rather than currency – and questioning whether they are too easily used for illegal transactions.

But the power of the block chain suggests that it is an innovation that will take root in a number of settings, and as it does, our world will get that much more wired, connected and complex.


Dave BuergerDave Buerger

@CUneXus

Dave Buerger is the Co-Founder and CEO of CUneXus and an award-winning financial services marketer and strategist, currently focused on developing the next generation of data-driven consumer lending technology. His company’s revolutionary cplXpress product suite has generated over $500 million in consumer loan volume. Buerger has presented the CUneXus product line at Finovate, Innotribe, Bank Innovation and more, and the company was recognized as one of just ten to watch on KPMG’s global report of “The 50 Best Fintech Innovators of 2014.”

“The top trends in Big Data in the banking industry are…”

In recent years, most Big Data use cases seemed to focus on compliance, security, and risk management. However, now it seems the industry is becoming more focused on Personalization and Automation, with the goal of driving engagement and product penetration (revenue).

Brand loyalty has grown thin and it’s easier than ever for consumers to take their business elsewhere. Faced with increased competition and a rapidly changing consumer mindset, the banking industry must embrace the inevitable shift toward a more Apple or Amazon type of banking experience, where unnecessary friction points are eliminated, digital and physical channels blend into an overall brand experience, and automation, personalization and ease of access power a more consumer-centric, on-demand experience. Intelligent use of data and the seamless real-time connectivity of all delivery channels are the keys to providing a comprehensive and robust approach to sales and service. Knowing your customer will be a critical component to staying relevant in an industry where 1-click access to financial products will soon be table stakes.

Through advanced data analysis, financial institutions can realize increased efficiency, better customer engagement, and deeper product penetration — while providing their clientele with the level of service and accessibility they’ve quickly come to expect. We’re in the midst of a fintech arms race. Financial technology vendors are focused on developing solutions that address these challenges, and new and exciting solutions will continue to emerge in the months and years ahead. Big Data is the fuel.


Daniel MeereDaniel Meere (via CIO)

@CIOonline

Daniel Meere is the Managing Director, UK at Axis Corporate and a contributor to The Disruptive CIO at CIO.com through his previous role with CSC.

NOTE: The following information is excerpted from Big Data Making Big Impact in Banking? via The Disruptive CIO. 

“The biggest trend in Big Data in banking is…”

The use of off-grid data.

Historically, banks have analyzed on-grid data only – but, in doing so, they need to understand how bias influences the data. After all, if a customer is truly unhappy, that person’s comments will be far more authentic in informal settings and when unsolicited or through informal channels.

The good news is that future opportunities are virtually limitless, especially in efforts to improve fraud detection and prevention—a huge priority in the financial sector. For instance, consider the possible creation of a utility that aggregates information for analyzing the entire industry for trends and patterns in fraud. When a new attack or scam surfaces, the information will immediately be shared with every bank. That way we would no longer see three banks discovering the same problem, respectively, months apart and having to deploy individual solutions. Just the cost and time benefits of independent discovery would make creating such a utility well worth the effort. This applies an approach similar to that of antivirus software in computers – once identified, all relevant points are informed and can act to prevent damage.

Similar benefits exist on an individual bank basis. For instance, analyzing data from a customer panel takes months, but Big Data analysis can provide far more insightful results in mere hours. Using social media as an example, mass comments can be analyzed in a far more timely and relevant manner, using views expressed a few minutes ago to offer a far richer data set.

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