BANKING ANALYTICS: ADVANCING CUSTOMER-CENTRIC APPROACH A NEW APPROACH GOT MOMENTUM IN 2023

M?BANKING ANALYTICS: ADVANCING CUSTOMER-CENTRIC APPROACH

INTRODUCTION

?Financial services industries were traditionally product-centric after Covid 19 pandemic exponential digitalization and software and Computer sciences changed the entire banking landscape. They transformed to be customer-centric with disruptive technology, evolving a tech landscape. Digital transformation (DT) has already become the future of the financial services industry. Banks, credit unions, and other financial institutions were already implementing new, digital solutions to address chronic difficulties and establish a more customer-centric approach to banking prior to the COVID-19 epidemic. That trend has only escalated in recent years, in large part to the rapid growth of so-called “disruptor banks” – mobile-first, app-based Fintech firms. Banking analytics, of all the digital tools now available, provides a mechanism to improve the customer experience, uncover chances for revenue development, and remain competitive in this increasingly turbulent market. DT in financial services is more profound than leveraging data and digitization. Customer personalization and customer delight experience now deepened after the pandemic. Smartphone penetration is now ubiquitous, as mobile banking becomes smooth and easy of utilizing for almost 89% of customers. ?and digital-only banks are transcending traditional banks. Unlike financial services organizations, fintech start-ups leverage technology and data analytics (DAs) as per customer preferences. Intense competition and tech disruption are the game-changer for fintech. Just an example of loan disbursal. Banks have a lot of paperwork KYC and due diligence processes that delay the loan disbursal. But?DAs?and AI make it easier for fintech start-ups to decide in minutes. Many leaders were leveraging compelling DAs use cases in banking and financial services. But they have to be updated regularly with the evolving tech landscape. The customer experience (CX) is the new competitive battleground for banks and financial services to leverage advanced analytics.

But now, ease of access and use, and resolutions in no time seem to be the new face of customer experience. The?financial services industry?has more challenges with the data flow from these multiple channels with omnichannel presence. AI is critical in the new CX –?The applications are manifold in financial services – chatbots, AI-powered automation, and AI DAs. Predicting customer needs, providing services, and resolving queries in no time enhance CX, the new norm for financial services. DAs are not about cutting costs but focusing on productivity leveraging advanced DAs for?fraud detection?can save costs up to 20%. Earlier it was just the automation of a document management system or repetitive processes. But now, it is more about leveraging technology for credit modeling, risk analysis, and fraud to leave humans for more critical projects.

Advanced Analytics in BFSI – Benefits

Updating the data analytics use cases in banking and financial services with the evolving data science methodologies can help organizations sustain stronger customer relationships. Observe a few more benefits of advanced analytics.

Customer 360-degree insights –? By leveraging AAs, financial services organizations can know more about customer preferences, multichannel touchpoints, and buyer behavior factors. There is a high chance that the sales folks might perceive a different need, but the data speaks another consumer behavior. Knowing the customer in detail is critical for?banking and financial services, unlike other industries.

Personalized customer experience?– Experts perceive personalization as another critical aspect of BFSI to reduce churn and improve revenues. Offering the right product at the right time while also reaching out with personalized information after understanding every consumer detail is now the norm for sales teams in BFSI. A report from Forrester says that a single-point improvement in?financial services organizations’?CX score can improve revenues from $5-$123 mn.

Prune operational costs –?Banks and financial services organizations are under constant pressure to maintain sleek profit margins and improve operations. Financial services firms can leverage predicting analytics, visualization, and AI to automate their workflows. Replacing paper-based forms with digital applications and using NLP technologies where ever necessary also helps in reducing manual efforts and errors.

Risk mitigation –?The main challenge for BFSI firms is to analyze risks like credit, claims, and fraud. Though the practice is not new, banks, insurance companies, and investment bankers need to update their risk approach with evolving technologies and exploding data from multi-channels. Financial services organizations can modernize their risk management practices more efficiently using predictive, behavioral, and?advanced analytics.

Competitive edge Fintech organizations with technology as their core are already disrupting financial services. Financial services organizations now need to adopt technology faster than before. Processing a loan application can be done in minutes with AI and advanced analytics, thereby providing more scope for customers. Data analytics in banking will enable you to understand unmet customer needs and help you unfold new consumer-centric business models.

Best Definitive Data Analytics Use Cases in Banking and Financial Services

Most of us know about the DAs' use cases in banking and financial services. We need to view them contrarily after the pandemic. The customer data is changing rapidly, and the touchpoints too. In order to implement DAs in banking successfully, the models should reconsider all the available data from the expanded sources. So, recalibrating the advanced analytics use cases with the changing consumer ecosystem.

Credit Modeling –?Credit risk modeling is not new in the banking industry. The traditional risk analytics models provided insights based on income sources, loan history, default rates, credit rating, demographics, etc. Many other factors need to be analyzed in conjunction with the standard data. Let us consider the case of consumer loans; different dynamics like social media profiles, utility bills, monthly spending, and savings give more profound insights into the default risk. Unstructured data plays a vital role in credit risk modeling too. AI-based text analysis and consumer persona provide deeper insights into the customers’ financial well-being.

Risk Analysis and Monitoring –?Banks and financial services organizations that implement dynamic risk models with advanced analytics seem to be more resilient to significant external changes. Risk models differ between Banks and financial services – credit risk, fraud, and liquidity risk are the major ones for banks; claims risk and fraud for insurance and portfolio risk analysis for investment bankers. The common risk for most financial services firms is?fraud detection?is continuously evolving. Ml, AI, and big data now enable organizations to analyze many transactions, not just based on historical data. Social media profiles, behavioral analytics,?predictive analytics, and advanced machine learning models are leveraged collectively for fraud detection.

Customer Life Time Value –?The trickiest one but looks like the most simple one to understand for anyone from the banking perspective. Customer lifetime value provides insights about the future revenue sources from the customer to focus marketing efforts and reduce churn. It is tough to estimate how customer behaviors change with time and the significant factors impacting their decisions. AI-powered advanced models recognize patterns more effectively in the data to provide behavioral insights that humans may not be able to identify.

Product Recommendation Engine –?Product recommendation engines are evolving in banking too. Multiple comparison sites are now available for each financial services product – loans, insurance, mutual funds, credit cards, etc. Consumers can make informed choices, but cross-selling financial products at the right time caters to customer needs and enhances trust. Ml models process data in real time from various content feeds to make the job easier for financial/investment analysts to offer personalized products and services.

Customer segmentation and personalized marketing –?Understanding every aspect of the customer is critical for personalization. Customers are now bombarded with different financial products at the same time. The place and timing of our marketing efforts matter in creating trust and showing intent to act on the marketing messages. We can also reduce awareness marketing efforts if we provide the knowledge at the right stage of the buyer journey.

AI-powered Virtual Assistants –?Consider the case of insurance; a loss or damage may not happen multiple times. It is the single touchpoint to show the customers how you care for them and ease the processes. Customers now prefer efficient self-service options to in-person contacts to process their requests. AI-powered virtual assistants add value in answering all the information queries about products, services, and eligibility criteria in financial services. They are also evolving to validate certain criteria based on the rules updated with the machine learning models. It wouldn’t be a surprise to see that an AI-powered assistant does the?insurance claims processing?in minutes.

??Banking Analytics means for a banker

Data analytics is “the science of evaluating raw data in order to draw conclusions about that information.” The raw data in the issue can be structured or unstructured, which come from internal or external sources. This can be used by businesses to achieve everything from learning more about their consumers and improving existing processes to creating predictive models and foreseeing growth prospects. This is a broad phrase that incorporates several types of analysis, including customer analytics, business analytics, predictive analytics, and so on. To that aim, banking analytics refers to any use of DAs in the this has long been a part of how banks and other financial institutions conduct business; in fact, the financial services industry as a whole was one of the first to embrace analytics, using it to monitor and anticipate market movements. Banks must increasingly use banking analytics to obtain granular insights from huge amounts of data referred to as Big Data and apply those strategic findings across all levels of the company.

How Advanced Analytics in Banking Can Help Banks

Banking analytics is a valuable asset to any institution and delivers numerous benefits, including A complete view of the customer, reduced operational costs, An outstanding omnichannel customer experience, Improved customer relations, and More effective risk management and mitigation, Some of the important

?Data Analytics Use Cases in the BFSI space

Most of us are familiar with data analytics use cases in banking and finance. Do we need to reconsider them in light of the pandemic? According to our specialists, customer data is rapidly evolving, as are touchpoints. To successfully deploy data analytics in banking, models must reconsider all available data from several sources. Let us reconsider advanced analytics use cases in light of the evolving consumer ecology.

Credit risk modeling:?It is not a new concept in the banking business. Traditional risk analytics models gave insights based on sources of income, loan history, default rates, credit rating, demographics, and other factors. Many other elements must be considered in addition to the basic data. Consider the scenario of consumer loans; many dynamics such as social media profiles, utility bills, monthly expenditures, and savings provide more in-depth insights into default risk. Unstructured data is also important in credit risk modeling. Deeper insights into clients’ financial well-being are provided via AI-based text analysis and consumer personas.

Enhanced Customer Satisfaction?– The trickiest but appears to be the easiest to understand for everyone in the banking industry. Customer lifetime value gives information about the customer’s future revenue sources, allowing marketers to focus their efforts and reduce churn. It is difficult to predict how client habits vary over time and the major elements influencing their actions.?AI-powered sophisticated models analyze patterns in data more efficiently, providing behavioral insights that humans may be unable to identify.

AI-powered Virtual Assistants?– Consider insurance; a loss or damage may not occur repeatedly. It is the one point of contact for showing clients how much you care about them and making the processes easier for them. Customers now prefer self-service options to in-person interactions for processing their requests. AI-powered virtual assistants add value by addressing all information requests regarding financial services products, services, and eligibility requirements. They are also evolving to validate specific criteria based on new rules from machine learning models. It would not be surprising if an AI-powered assistant processed insurance claims in minutes.

?TRANSFORMATION OF THE HUMAN PARADOX: FROM CUSTOMER CENTRICITY TO LIFE CENTRICITY

RESEARCH REPORT: IN BRIEF

?Amid global instability, consumers are allowing themselves to be inconsistent as they reconcile personal values with practical realities. Their decisions can seem paradoxical—and they are comfortable with that. 2/3 say making contradictory decisions is totally acceptable. But as increasingly self-reliant customers embrace complexity, businesses are still seeing them as one-dimensional walking wallets. To stay relevant, businesses must move past customer-centric models and embrace a life-centric view that sees people more fully. At a time when the world feels out of control, consumer behavior can seem contradictory: People are prioritizing themselves but want to effect change for others. They want to follow their values but not at the expense of value. They’re taking matters into their own hands but also want companies to hold their hand. These kinds of inconsistencies might not be new, but they’re increasingly considered normal and even good. In fact, Accenture research reveals that 69% of consumers globally think that paradoxical behaviors are both human and acceptable. Consumer needs are changing fast—and companies will have to evolve just as quickly if they want to stay relevant. Oversimplifying segmentation and underestimating the impact of life forces on behavior has led to a growing disconnect between what companies think their customers want and what consumers say they want. To bridge the gap, businesses need to widen their aperture and move from focusing only on the consumption of customers to seeing their customers as they see themselves: multifaceted, complex, and doing their best to adapt to unpredictable life circumstances out of their control. It’s time for companies to move from customer-centricity to life-centricity. Consumers are showing they are comfortable being multi-dimensional, but many businesses continue to see them in just one way: as walking wallets. “Stop hiding behind focus groups and marketing hype, and speak to people with an honest voice." Companies are focused on finding simple ways to define consumers and predict their behaviors. But our research reveals a major disconnect between what consumers say they most value from companies and what companies appear to be investing in. The disconnect is felt by consumers and businesses alike.64%of consumers wish companies would respond faster to meet their changing needs.88%of executives think their customers are changing faster than their businesses can keep up. At a time when consumer choice has never been higher—and the cost of switching to a new brand has never been lower—a relevance gap this significant could come at a great cost if not addressed. One thing is clear: The old playbook for relevance is now obsolete. It’s time to take on a new strategy.

?Life forces causing relentless instability

?The world today is radically different from the world of two years ago … or even two months ago. A non-stop barrage of external life forces—health, economic, social, environmental, political, and beyond—is affecting day-to-day decisions in unavoidable ways. Consumer prices are skyrocketing at their highest rates in 40 years, while the war in Ukraine indicates long-term consequences for global markets, food prices, and political stability. Major societal and cultural movements around the world are magnifying conversations around social justice issues, just as increased political polarization and a growing distrust in government and media complicate the path to change. Technology has democratized access to information, with Web3 and the metaverse hinting at a creative and dynamic future; but 43% of consumers say technology advancements have complicated their lives just as much as they have simplified things. With external forces exerting more pressure, and a list of practical and ethical considerations that keeps getting longer, people are facing more complex and more frequent decisions than ever before. 72% of consumers say external factors such as inflation, social movements, and climate change are impacting their lives more than in the past.60% of consumers say their priorities keep changing as a result of everything going on in the world. To help make decisions, consumers are looking to the people they trust the most: themselves. Now, they are ready to act in their own best interests because if they won’t, who will?

Three brands delivering life-centric solutions Allstate, Santander Sim, and Blue Buffalo are putting life front and center to help them meet customers' ever-changing circumstances and priorities.

Purpose, redefined

?Forced to adjust to circumstances beyond their control and armed with technology that gives them more access to expertise than ever, consumers are developing a stronger sense of self-reliance: Nearly 75% of consumers say they feel empowered to make key decisions in their lives. powered. As people become more self-reliant, they are also rethinking the values that drive them. Up to two-thirds of consumers say they have completely reimagined what’s important to them in life—a 10% increase over the prior year—but nearly 62% of consumers also say many new things are important to them because of what’s going on globally and locally. Redefining a sense of purpose amid a backdrop of unstable external life forces opens the door to inconsistencies—between what consumers believe or want and what they actually do.

Keep up with customers’ increasingly dynamic needs

Accenture Song’s David?Droga?and?Baiju?Shah explain how a move from customer-centricity to life-centricity can unlock the next?great wave of growth.

Making peace with paradoxes

?In an environment of perpetual change, consumers are working to reconcile their core values and sense of purpose with the demands and practicalities of everyday life. The outcomes can be messy and inconsistent: For example, as they feel the strain of supply chain disruptions, 55% of consumers are newly motivated to buy items produced locally and sustainably—but they also want to take back control of their wallets as prices rise. The result is a growing acceptance of paradoxes, in which people make peace with the often contradictory and conflicting consumption decisions they make from moment to moment. Paradoxical decisions are not new—what’s changed is the increasing frequency and comfort with which they are made. \People are giving themselves permission to be inconsistent. As they evaluate a growing list of things that matter to them, consumers realize they can’t expect perfect choices in every circumstance. As they make decisions, paradoxes become inevitable. And those inconsistencies are being seen as strengths, not weaknesses. If companies can’t match this new way of thinking, they risk falling behind.

From customer centricity to life centricity

?Businesses once looked to a product-centric approach focused on performance. Then they shifted to a customer-centric strategy, meant to prioritize experience. But now, the dynamics are more complicated. Until companies stop over-simplifying their customers and start accepting that they are ever-changing, multi-dimensional people deeply impacted by unpredictable external forces, they’ll find themselves stuck. They need to become?life-centric. Life-centric businesses deeply understand the different forces shaping customers’ lives and deliver the most relevant solutions for those contexts. Companies that embrace a life-centered approach— one that takes into consideration the humanity of the consumer, their shifting modes, and the unpredictable life forces that come into play along the way—are best positioned to thrive in the future.

?To move toward life-centricity, companies need to do three things:

?1. See customers in their full life

Our research shows that as consumers lean into their self-reliance and accept the inevitability of paradoxes, they are breaking every convention. By only focusing on static segmentation models and expecting a straight-line customer journey, companies risk missing out on the deeper insights underpinning behavior—and the ability to drive new value and relationships. The way forward is to take a holistic, dynamic view of who customers are and what motivates their behaviors—and to treat them as more than just buyers.

2. Solve shifting scenarios

Life-centric businesses are prepared to adapt so that they are delivering relevant options across their products and services to accommodate the shifting life forces impacting their customers. Consider where they are in terms of two key factors: time and control. In decision-making, time-based considerations (short-term versus long-term needs, urgent versus unrestricted schedules) can change from moment to moment, but always shape the way consumers make choices. Also critical is their appetite for control—sometimes they want something highly specific; other times they value inspiration and new ideas. Offering options that combine values and priorities in new ways will speak to consumers who are reevaluating what they want and need. Ultimately, businesses need to abandon the idea of one-size-fits-all and focus on flexible options.

3. Simplify for relevance

Amid the pressure of life forces and the chaos of everyday life, what customers ultimately need is simplicity. They are drawn to anything that cuts through the noise and makes their decision-making—and their lives—easier. Businesses that want to stay relevant need to find ways to clear the path for consumers to walk easily. To do this, companies can look to data, artificial intelligence, and expert inputs to help draw connections between their customers’ needs and the external life forces that are influencing them. At the same time, they must simplify from within by being ruthless about prioritization and continuously evolving.

Putting life front and center

Global instability isn’t going away. So as consumers work to navigate it, businesses must embrace a life-centric approach that helps them meet customers’ ever-changing circumstances and priorities. In this way, they will be best positioned to meet the future—no matter what new challenges are around the corner. Create growth through relevance at the speed of life.

?Accenture Strategy

Shaping our clients’ futures, combining deep business insight with technology.

?LIVE EXAMPLE

?Over the past two years,?Bank Jago has been systematically enhancing its data and analytics capabilities to provide intuitive, life-centric banking services for millions of its customers across Indonesia. Bank Jago’s overall aim is to help consumers, small merchandisers, and small businesses get closer to achieving their dreams and aspirations through better financial management. There are a few principal ways in which we do that. One is that we serve them through technology and fully digital delivery. Another is that we serve them as part of an ecosystem rather than as a stand-alone digital bank. That automatically has implications from a technology and AI perspective. From the beginning, we invested heavily in a modern technology stack, including advanced analytics and ML both of which are required because, without digital technology, our market scale would be difficult to achieve. Working this way has allowed us to gain more than 4 million customers in less than 18 months and offer differentiated, nonstandard banking features. We also aim to deliver life-centric banking. Analytics allows us to personalize and adapt to all our users. Because we’re embedded in an ecosystem, we have a rich set of data from both Bank Jago and our partners.

?By combining all of that, we can use data optimally, and we can offer a richer value proposition to our users.?We don’t treat digital as a channel to serve people with traditional financial products and services. Instead, we look to reinvent the entire value proposition, shifting the focus from money and financial services to the things that really matter to the lives of our users and the ways financial services can help them do or achieve those things. That’s also where the importance of data comes in. Our ecosystem approach is still relatively novel in financial services. It’s not about simply partnering with some companies to cross-sell; it’s about jointly building an ecosystem where we can infuse our financial services products within our partners’ lifestyle services, such as food delivery, transportation, or e-commerce. Providing that kind of digital lifestyle with financial services embedded is profoundly useful for the customer in a way that traditional financial services are not.

??About a year ago, we launched features called “pockets” and “shared pockets.” It was inspired by an Indonesian concept called the “amplop” system, in which a person uses envelopes to save money for different purposes. We didn’t see a digital equivalent of that in banks. So, we created that concept digitally, where users can have as many pockets as they would like to separate their money for different purposes. Then they can share the pockets with, say, their kids or spouse or another member of their household. “That is an example of a life-centric banking feature that aims specifically at how Indonesians like to approach the management of their money. For the first year or so after the launch, we were quite busy scaling the system and getting it ready for customers to use. In the coming year, we plan to launch other features like this one that will be truly differentiating. The sheer opportunity is what makes this both very exciting and very challenging. This is a market with 280 million people. But on top of that, probably 50 percent of them are either unbanked or underbanked. While that gives us a fantastic opportunity in many areas of the market, it’s also difficult because there’s a lot of work to do to understand what is needed that presumably hasn’t yet been provided with more traditional services. We have to continuously learn what they need, how we can adapt to them, and how we can offer a service and experience that’s right for them.

Our core strategy has changed remarkably little. Speaking for myself, I feel that our purpose to help people get closer to their dreams and aspirations through different styles of financial management still persists. But we have learned a lot about what it takes to execute against that aspiration and that purpose. We have many moving parts that all have to come together in an orchestrated way: technology, data analytics, machine learning, people, and so on. On top of that we have more than 30 ecosystem partnerships. Bringing everything together in the right way was harder than we had initially thought. So, we’ve had to adjust our perspective on how to properly coordinate these pieces.

We started this just before COVID-19 and had a lot of assumptions in terms of how to build an entirely new team from scratch, infuse our purpose and build our culture, and set a direction for that team. We were using tactics that would have worked in the past but didn’t work during times of COVID or physical lockdowns or in a remote workplace. Having to build this team, this organization, and this culture under these conditions was first-time, as it was for everyone else in the world. And we had to modify all our existing experiences and assumptions to achieve that.

Three things: First, don’t fall into the trap of only considering digital technology as a channel to provide existing products and services. It’s an opportunity to fundamentally reinvent your value proposition, and I believe that should be done in a life-centric manner so that it’s more about what people truly care about and not just about basic financial services. Also, that way, the data, AI, and analytics come into their own because it allows you to better understand not just groups of users but also individual users. That’s one way to max out the opportunity that technology and data offer to reinvent your value proposition.

Second, don’t settle for legacy technology or slightly older technology if you want to push the boundaries of what you offer and how you offer it to your users. In our experience, you have to invest in cutting-edge technology; using legacy systems just slows you down. Sooner or later, it prevents you from offering what you really want to offer.

Third, think carefully about the right way to combine a financial services culture and a tech culture. Because we are so heavily reliant on technology, data, and AI, we can’t get away from adopting a lot of the same practices that successful tech companies have adopted. However, banks still have relevant knowledge and experience to offer from decades of learning how to do risk management, liability management, and so on. How you bring those together so that it’s not one or the other is something that requires careful thought.

THE REASON FOR HUMAN-CENTRIC INNOVATION IS NECESSARY

?Even a great idea or mission can be crippled by a design that doesn’t put people first. Why should human considerations—such as their needs, wants, and dreams—only come into the conversation later? By centering empathy and understanding in design research, companies can both reduce risk in the idea-generating stage and ensure people are at the heart of the process. These needs also include the needs of the planet, society, and what matters to us as human beings. Explore our insights to learn more about human-centric design, including how to balance designing for individuals versus everyone, why first-hand research can change the game, and why it’s important to innovate for the needs of future people too.

?Building the AI bank of the future

?Making financial services available to the masses through AI. There is some ready-made program for use as under:

Finalta: Improves a bank's operational performance and financial metrics by benchmarking process efficiency, sales performance, and high-level strategy.

FinLab: We work with our clients to build custom solutions that combine deep industry expertise, quantitative analytics, and research while helping them build the capabilities needed for long-term success.

GCI Analytics: GCI Analytics helps transform portfolio strategies and uncover revenue opportunities across the entire customer lifecycle.

Panorama: Helps organizations define their strategy by identifying opportunities, improving financial planning, and benchmarking performances.

?Performance Lens:?Performance Lens is a McKinsey solution that helps asset managers make better strategic, operational, and organizational decisions using fact-based, actionable insights.

?PriceMetrix: Data-driven insights for wealth management advisors and executives.

?Performance Lens is a McKinsey solution that helps asset managers make better strategic, operational, and organizational decisions using fact-based, actionable insights.

FDIT firms are data and software service providers to the financial services community, operating at the intersection of finance and technology. These firms are playing an increasingly critical role across the financial services ecosystem. Our clients span exchanges, trading venues, custodians, clearing houses, financial data and analytics providers, credit agencies, and banking software providers.

CONCLUSION

This brings us to the conclusion of?Data Analytics for Beginners. We learned what data analytics is, the need for data analytics, and the different steps involved in it.?Then, we looked at the various tools used in data analytics and the application of data analytics. Finally, we saw a case study on Walmart and performed a demonstration on Linear Regression in R to predict sales based on advertising expenditure through various mediums.?Data analytics is important to understand trends and patterns from the massive amounts of data that are being collected. It helps optimize business performance, forecast future results, understand audiences, and reduce costs. The 4 types of data analytics are Predictive data analytics, Prescriptive data analytics, Diagnostic data analytics, and Descriptive data analytics. While data analytics is used by every business to understand their operations, the four top sectors that are using data analytics are Retail, Agriculture, Banking, and Government. Data science is an umbrella term for a group of fields that are used to mine large datasets and focus on deriving meaningful correlations between large datasets. Data Analytics is more focused on uncovering specific trends and realizing actionable insights.?Data Analytics is used to make sense of large amounts of data to derive insights and trends to improve business growth. The different types of data analysis are Descriptive Analytics, Diagnostic Analytics, Predictive Analytics, Prescriptive Analytics, and Cognitive Analytics. Many?data analytics tools?exist, but the top 10 are SAS Business Analytics (SAS BA), QlikView, Board, Splunk, Sisense, Microstrategy, KNIME, Dundas BI, TIBCO Spotfire, and Tableau Big Data Analytics.