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From Science To Art: Big Data Can Paint A Clear Picture For Banking CMOs, CIOs

Mike Panzarella and Neetu Shaw with Perficient answer common questions about “Big Data” in banking.

Financial marketers are under immense pressure to manage emerging challenges and lead new strategies that blend branch, online, mobile and social channels for their business. The movement to mobile and digital channels has spurred banks and credit unions to make sense of the vast volumes of information generated by those channels to draw new insights and create new products. That’s why some CMOs are taking the lead and trying to tackle Big Data — one of the biggest trends in banking, yet one of the most hotly contested terms in the data analytics space today. While it’s not necessarily a new term in the financial services industry, the heightened attention on Big Data today has raises questions about the way banks and credit unions view, understand, lead and implement their marketing efforts.

What is your advice for banks and credit unions looking to implement Big Data technologies? Where should they start?

“As CMOs evaluate Big Data, the need to collaborate with CIOs is now greater than ever.”
— Mike Panzarella, Perficient

Mike Panzarella (MP): When it comes to financial services institutions, the first place to start is identifying a use case where traditional approaches may be problematic, especially with regard to scalability, processing or analytical capabilities. Obviously, the use case must provide real business value and should not require extensive integration. For example, if you have to integrate a number of heterogeneous data sources with dozens or hundreds of tables in order to have the data set you’ll use on the Big Data platform – this might not be the best initial use case.

Neetu Shaw (NS): It starts by understanding who is asking the question — what role they play within an organization and what line of business they are focused on. It’s important to target communications based on their needs. After determining who you are having the conversation with, the next step is to better understand what their immediate and strategic information needs are. Why they are even interested in Big Data? Is it just because it’s the latest buzzword? Are they requiring help? It’s our goal to understand how they’re dealing with data today, how their business processes information today, and what existing investments in data platforms they currently have.

I would suggest using cloud-based solutions for traditional Big Data platforms such as Hadoop to analyze geographic and psychographic data, which can easily be mapped to existing customer data in a loosely-coupled fashion.

What are the top Big Data use cases for financial marketers and banking executives?

MP: The financial services market is really a hot market for Big Data. There are so many use case patterns and banking trendsetters in this space that CIOs and CMOs are already working through some of these data mining challenges today. They represent real, near-term opportunities for banks. Five key business areas that present low-risk opportunities for tangible performance using Big Data include: fraud detection, consumer sentiment, intelligent forecasting, customer profiling and target marketing, and customer service.

  • Optimize enterprise risk management and fraud detection patterns. As banking and payments have moved onto mobile and online channels, the opportunities for fraud have expanded. Big Data helps banks analyze credit quality, monitor fraud, reduce customer churn.
  • Keep up with the digitization of consumer behavior. Banks can learn from customer behavior in what some call the digital era of engagement banking. Banks need to keep pace with the preferred channels of interaction like mobile and social, and as financial institutions mature, so should their marketing analytics strategies. Big Data has the power to help banks use “social listening” to understand customer sentiment, interact and communicate with customers in real time using the channel they prefer.
  • Benefit from intelligent forecasting. Intelligent forecasting is a business analytics strategy that allows companies to obtain real-time information from operational systems and send forecasting data back through the pipeline to corporate decision makers. This analysis helps make business decisions around resource allocation, managing forecast risk, accelerated marketing and efficient management of sales teams, pipeline and forecast data.
  • Expand customer profiling, target marketing and cross-selling. Understanding and acting upon customer behaviors should be the primary focus for improving marketing performance with Big Data. For marketers and customer experience professionals, generating customer insights and understanding Voice of Customer (VOC) data enables segmentation strategies based on demographics, transactions, behaviors and social profiles.
  • Benefit from quality assurance and customer service. In real time, recorded customer service calls can be used to prevent or stop a negative customer experience or enhance the customer service at the call center level.
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( Read More: Is Banking Really Ready For Big Data? )

What impact does “digital disruption” have and how can they see through the noise it creates?

MP: CMOs and CIOs alike are faced with challenges when it comes to Big Data. Customer engagement has become critical in the digital age. The velocity of customer sentiment and brand awareness has forced banks to embrace strategies where social media is merged with traditional bank services. Along with the explosion of mobile device usage, there has been a profound impact on the financial services industry in terms of managing and synchronizing customer engagement and brand awareness across branch, web and mobile channels.

This digital disruption has banking CIOs and CMOs challenged with focusing more on transformational, data-driven solutions. They’re challenged with building an effective and elastic repository of data that allows them to build models, fine tune the information, and drive effective performance, while maintaining a “golden profile” of external and internal data sources.

One major challenge that I’ve witnessed that has people confused is when talking about Big Data most companies are looking at “data at rest” to identify a pattern and template. “Data in motion” is what catches activities in real time. If banks don’t have streaming Big Data solutions, they won’t get the full value of Big Data assets they are creating. The real-time component of Big Data provides a lot more marketing value than data at rest in terms of real-time offers and promotion of customer segmentation.

“Digital disruption requires companies sift through the noise created by a new wave of banking innovation, and identify new ways of using technology to fundamentally change the way we view and interact with customers.”
— Neetu Shaw, Perficient

NS: Digital disruption requires companies sift through the “noise” created by a new wave of banking innovation, and identify new ways of using technology to fundamentally change the way we view and interact with our customer. By and large, banks have been challenged with digital disruption, in that many are unable to deliver a unified, secure customer experience across all channels and marketing touch points.

In terms of infrastructure, most are still coping with an inadequate information architecture that is required to support this type of innovation. Many of them don’t even have simple processes and models created around the customer that allow them to profile, segment or prospect individual customers. Additional challenges around access, quality, and granularity of data pose significant threats to any serious conversation about Big Data.

The bottom line is an organization must create a holistic customer experience that redefines how they engage with customers across channels and touch points. The inability to do so means that these disconnected, customer digital experiences will continue to hound these organizations in the short-run, resulting in the destruction of their brand credibility in the long-run. Big Data enables the creation of these unified experiences that traverse multiple channels.

Can you provide examples of types of data that fit into the Big Data category for financial institutions?

“As companies explore the potential use cases of Big Data for their business, leveraging existing data sources will be imperative.”
— Mike Panzarella, Perficient

MP: The key to successful Big Data requires focus on a compelling business opportunity as defined by a use case. As companies explore the potential use cases of Big Data for their business, leveraging existing data sources will be imperative. Among the most common and easiest data sources used for Big Data initiatives in financial services include: financial transactions, social media streams, clickstreams, internal unstructured data, application log files, in-stream monitoring, location-based data and digital/rich media.

How do financial institutions find value in the data?

“With a holistic approach to a Big Data strategy involving many key stakeholders, financial institutions can better leverage and gain value from relevant information.”
— Mike Panzarella, Perficient

MP: The easiest way to get started with delivering value is to talk to your peers, understand the key business challenges, and align these problems with your corporate goals and strategies. It is important for financial institutions to hone in on outcomes that provide the greatest return on investment based on top business objectives for the organization. By taking a holistic approach to setting a Big Data strategy, by involving many of the organizations’ key stakeholders, financial institutions can better leverage and gain value from relevant information across the enterprise. By aligning business capabilities required with existing data sources and identifying gaps in those sources, Big Data opportunities can more easily be categorized and defined.

Most of the value in Big Data lies in agile analytics, and that’s why advanced analytics is a fast-growing realm in the information management space. Agile analytics enables real-time dashboards of customer and operational KPI metrics as well as sentiment analysis of social channels to provide a financial institution with an up-to-the-minute pulse on corporate performance and customer satisfaction.

The ability to blend Big Data analytics and digital strategies can turn actionable analytics into marketing results. When done right, Big Data has become a critical tool for competitive advantage. It helps organizations gain customer insights, and in turn, generate segmentation models that better create service offerings, product feature enhancements, and customer loyalty and reward opportunities. In short, Big Data delivers big results.

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( Read More: Big Data: Big Opportunity In Banking… Or Big B.S.? )

How would you explain the lack of Big Data success stories in financial services? At what point will we begin to see them?

One of the biggest challenges right now for the industry is to understand how Big Data can deliver on its promise of increased revenue and provide a competitive advantage for financial institutions bringing new products and services to the marketplace. Organizations should address five roadblocks in order to deliver on the promise of Big Data. These are:

  • The Aha Moment – Vendors and service providers must continue to provide more thought leadership, granular data modeling and specific templates to generate that “aha” moment for organizations, and provide a better model and visualization of how technology can solve a business problem in a more meaningful way.
  • Budget Constraints – Developing a true cost-benefit model may be difficult when significant upfront development costs with tools like Hadoop are common. New cloud-based and turnkey analytical platforms for Big Data make setting up a platform – and seeing a return on investment – more achievable than ever before.
  • Knowledge Gaps – IT strategies and business processes for Big Data are very different. Gaps in data storage and processing strategies, plus lack of CIO know-how or direction will cause banks to falter. Banking technology professionals may also still lack knowledge of Big Data management tools. Technical and end-user training may also prohibit banks from adopting Big Data.
  • Cost Overruns – A vast majority of banks’ traditional data governance and data management practices aren’t capable of supporting Big Data requirements and can lead to costly and delayed data analytics projects.
  • Business Alignment – Banking CEOs and key stakeholders have very focused business objectives. Often times these business objectives aren’t in alignment with Big Data ideas making this a top roadblock for financial services organizations.

This being said, there have been success stories in the industry. A recent Wall Street Journal article referenced how the big four banks – JPMorgan Chase, Bank of America, Citigroup and Wells Fargo – are beginning to use Big Data technology to better analyze customers’ behaviors and drive business and marketing decisions.

What are some tips for financial institutions looking to jump start their projects and achieve “quick wins” with Big Data?

MP: Here are some tips to get organizations on the right path when it comes to Big Data.

  • Doing nothing is not an option – it’s not about whether or not to embark on the Big Data journey. The focus should be on understanding where to start and what the business value is.
  • Don’t boil the ocean – prioritize technology investments.
  • Develop a roadmap – look for guidance on what technologies are the best investments based on current business strategies and existing investments.
  • Find value from within – audit and leverage information that already exists in corporate data sources, understanding existing data assets can help drive more streamlined Big Data use cases.
  • Be a leader in the social revolution – look for data in new sources, going beyond traditional structured data sources.
  • Enable a competency center – build a team of stakeholders that promote collaboration, open communication and alignment of business and technology.
  • Change management is critical – banks need to ensure that standardized methods and procedures are used to minimize impact on the organization.
  • Manage risk – staff projects with data analysts that have a business focus and ensure that they have the support of IT and data stewards in the enterprise to help align the business needs with Big Data initiatives.

Remember, Big Data is both a science and an art. In order to be successful CIOs and CMOs in the financial services industry need to partner if they want to experience the true value Big Data can bring.

Mike Panzarella is the director of industry solutions for Perficient’s Financial Services National Business Group. An experienced information technology leader in the financial services industry, Panzarella has more than 20 years of experience with Big Four consulting and commercial banking firms, and proven expertise in BI/DW, Big Data and the convergence of social and mobile.

Neetu Shaw serves as Perficient’s enterprise information solutions principal, providing strategic thought leadership in developing and implementing a common EIM framework for clients. Shaw focuses on helping clients with traditional data warehouse, data integration and enterprise data architecture.

You can download Perficient’s “Big Data Planning Guide for Financial Services” white paper for insights on how financial marketers can better understand how unmanaged, social data can be combined with managed, internal data to drive new insights into customer behaviors.

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