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2022’s Go-To Guide to Data Analytics in Banking & Financial Services

2022’s Go-To Guide to Data Analytics in Banking & Financial Services

Compounding high costs of bad data are the opportunity losses banks risk with slow efforts to scale their Data Analytics function.

Not as a set of discrete projects, data analytics must evolve into an actual business discipline. The imperatives to do so are the twin drivers: advancing technologies (the exponential growth in meaningful data and available computing power) and enormous economic pressure banks face today.

Three ways data analytics generate an increase in bank’s profits.

  1. Amplify P&L levers (accelerate growth, enhance productivity, and improve risk control)
  2. Find new sources of growth (creating new business models, e.g., offering data analytics with others in the partner ecosystem)
  3. Deliver on the promise of a digital bank (enhanced omnichannel experience at lower costs)

Before analytics is applied to structured or unstructured sources, financial organizations have to resolve industry-specific challenges (regulatory requirements, data security, data quality, and data siloes).

Today leading banks leverage the power of analytics in more ways than one. One uses machine-learning algorithms that predict currently active customers who might drop business. Another use is to analyze competitor campaigns that curb any unnecessary discounts banks may be offering. Yet, another uses analytics to parse big data to discover microsegments in its customer base to create that next-product-to-buy.

2022 priorities for getting more out of data analytics investments.

  1. Post-crisis, as banking analytics use-cases increase across sales & marketing, HR, Risk & Compliance, and IT, banks will get more bang-for-their buck as they align analytics priorities to strategic vision.
  2. The second boost would come when managers scale analytics pilots by augmenting technical production and engineering capabilities. To succeed would mean to absorb data-driven iterations into work rhythms, something that change management programs can help in.
  3. The third priority concerns staffing. Individuals chosen for analytics roles (data engineers, scientists, ML engineers, e.g.) must bring a collaborative mindset.
  4. Financial organizations will

create value beyond the logical use-cases (digital marketing, transactional analysis, cybersecurity) by exploiting rich data sets by synching data across organizations and finding innovation breakthrough areas.

All said and done, much of these priorities would be possible when banks use robotics to eliminate 20% – 40% transactional accounting work. Not only will this allow finance teams more time for decision-making, but it also helps them gauge how best predictive analytics meshes with the performances they seek.

Banking analytics as it plays across the selling process.

Senior managers tasked with banking operations, and profitability must step back from the customer life cycle to tease out interlocks where analytics brings information and value. It is discussed below with the corresponding analytics benefit.

  • Customer Identification and acquisition (acquisition analytics and campaign design)
  • Customer relationship management (managing portfolio and meeting transactional needs)
  • Customer cross-sell (need analysis, demography, credit history analysis, next-best-product)
  • Customer retention (churn prediction, lifetime value modeling)
  • Customer value enhancement and increasing wallet share (behavioral segmentation, product affinity modeling, and differentiated pricing)

As the saying goes, “Future is already here; it’s just not evenly distributed.” Banks will need to smooth out customer road maps so that each one receives high-quality and personalized relationships.

How does all this tie-up to 2022 predictions for banking data analytics? 

A recent study of 10,000 companies reported that 71% are in the midst of or stand at the edge of disruption. On a 0 to 1, banking moved from 0.43 in 2011 to 0.52 in 2019. In 2020, 1 in 5 banking and payments sectors players were less than 15 years old.

The reasons are familiar – disruptive Gen Y expectations, Fintech entries, accelerated digital banking, and shifting regulation.

Given the mandate – ‘transform or make way’ – no two banks would (or should) approach data analytics the same way.

Practical steps for chasing analytics transformation and the next frontier.

The reasonable steps will remain the same as before: identify business problems, centralize data, automate processes, focus on decision making, optimize finance cycles, fight for talent, and drive continuous improvement. The innovators will turn their data analytics engines in pursuit of three strategic objectives:

  • Reinforcing the core (augment existing core bank offering)
  • Creating a new distribution channel (becoming a preferred partner to third parties)
  • Launching innovative ventures (develop new businesses and business models)

Conclusion.

In the final analysis, as the fog lifts over the current crisis, data analytics in banking will pay dividends for players that use it for intelligent pricing, selling, retention, and intelligent prospecting.

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2022’s Digital Transformation Trends (and Imperatives) for Banks

2022’s Digital Transformation Trends (and Imperatives) for Banks

As is customary Forbes published 2019’s seven digital transformation trends that January. Not many believed that an antagonist unseen to the naked eye, was plotting to transform world economies in ways unseen to the mind. A year later, WHO declared the public emergency. The world as we knew it, changed overnight.

But the Forbes-predicted trends – mobile banking, mobile pay, mobile apps, blockchain, big data, automated wealth managers, backend optimization –all proved right on the money. Adoption of those digital technologies not only accelerated overnight but also is changing the face of financial services. While a recent study elaborates on impacts specific to mortgages, banks, insurance, and worker compensations; future transformations in financial services undeniably lie at the sweet spot of tech. innovation, talent availability, hybrid work models, and platform modernization.

Before we unscramble 2022’s top digital transformation trends for banks, it is profitable to see what the key imperatives for leaders in the financial services are.

Three key Imperatives for financial services

  1. Rigorously chase revenue opportunities by drawing closer to the digital customer. How? By employing advanced analytics that pre-empts post-pandemic customer expectations and facilitates value extraction via non-traditional collaborations.
  2. Redesign operations built on regionalized resilience and transitioning to end-to-end digitization that reduces costs and increases flexibility. Also critical is to embrace a work force now acclimated to hybrid productivity models.
  • While these two levers boost competitive advantage, it’s the third – reimagining with digital – which if executed in quick time will be 2022’s game changer. And what does that imply for banks? Literally, the sky. But primarily it means to use Data, IoT, and AI, in ways that allows for real-time decision making to confidently execute on and monetize the reimagined (disruptive) business models.

Digital Transformation – The size of it all.

Global economy is on course to embrace its digital destiny. 65% of world’s GDP is scheduled to digitalize by 2022. Direct digital transformation investments will top $6.8 Trillion between 2020 – 2023.

But as a recent BCG study shows among 80% of companies that embark on transformation projects, 70% came up short. So, what will decide the winners? The quick answer is, not only ones that make the mindset shift to embrace digital but also, the ones that sidestep common judgement errors, as these trends highlight.

Digital Transformation trends for the Banking Sector

Number of ‘cohesive’ banks will increase (a.k.a. true AI-first financial services)

More data-centric financial services companies will map their digital journeys so equal attention is placed on both people (consumers, employees, clients) and data sciences (the mined meaningful insights) to orchestrate decisions. Once people, practices and cultures ‘cohere’ to the new reality; actions of the ‘cohesive company’ will be felt on ROI’s and other key metrices. But how do banks know they have reached the important point of no-return? Simply, when, in their daily decision making, data begins to take precedence over intuition.

In strategic terms, what do cohesive companies do next? Slew of initiatives, or the ‘flip’.

More cohesive banks will master the ‘flip’

The three ‘flip’ levers – banks re-platforming to the cloud, switching their IT spending from operations and maintenance to innovation, and thirdly, taking on non-traditional, non-financial business goals – will decide 2022’s digital value champions. A related study shows how a few organizations treated the economic downturns and depressed market conditions as crucibles for innovation. The flip, as results show, encourages innovating with new technology at scale, as opposed to retrenchment.

The reason why mastering the flip becomes important is the new wave of digital disruption that is adding to the banks’ vulnerability. Before Amazon, Facebook, Google start acting like full service financial institutions, banks must go fully digital. And fast.

Like never before, the way to a consumer’s wallet will pass through the heart

Even so, that digital adoption took off in Europe and US in six months leading up to mid-2021, it was faster in India, Mexico and Brazil. As this McKinsey survey study points out, 44 percent of digital consumers don’t fully trust digital services and will use less of digital services post-pandemic (more true for industries as travel, banking, telecom, and entertainment).

Premier financial services institutions, with significant technology investments, will be wary of this customer churn. Although, in a way, this clarifies the immediate challenge. What, then, will be the ‘highest peak’ for banks in 2022?

In two words – Win Trust. The supporting strategy will of course include re-engineering processes that provide enhanced data security, beef up cyber-resilience, compensate in case of errors, and frictionless payment processes.

How does all of this add up?

As the cofounder of Singularity University, Peter Diamandis says, in the next decade, we will experience more progress than in the past 100 years.

The question then is: How prepared will the banks and financial services company be?

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DataOps : The new DevOps for Analytics

DataOps : The new DevOps for Analytics

Understanding the similarity and differences between DataOps and DevOps; and the relevance of DataOps in present day Banking

DataOps, while often spoken as the ‘new DevOps for Analytics’ is a collaborative data management practice focused on communication, integration and automation of data flows across an organization.

DevOps and DataOps leverage different organizational people and their expectations. DevOps serves software developers who embrace complex details of code creation, integration and deployment. DataOps users on the other hand, are usually data scientists and analysts focussed on building and deploying models and visualizations. The DataOps mindset focuses on domain expertise, and is interested in getting models to be more predictive or deciding how best to visually render data.

Where by using DevOps (Continuous Integration and Continuous Delivery) leading companies (Amazon, Google, Netflix, Facebook, Apple) have accelerated their software build lifecycle (earlier called ‘release engineering’) to reduce deployment time, decrease time to market, minimize defects, and shorten time required to resolve issues; DataOps seeks to reduce the end to end cycle time of Data Analytics – from idea origination to value creation through charts, graphs and models.

The Similarities between DataOps and DevOps

DevOps and DataOps both rely on similar architectural principles (cloud delivery) for Continuous Integration and Delivery, as also they harvest cross-team (development, operations, analysts, architects, scientists, quality monitoring and customers) collaborative energies that drive value creation and innovation.

In fact as modern organizational cultures promote ‘Data Literacy’, newer approaches (like self-service data preparation tools) come equipped with their own in-built data operations. As a result, today data practitioners not only collaborate and co-develop insights in a zero-code environment, but also streamline work delivery across the organization.

Another significant end purpose that unites the two is large scale global consumption and provisioning. As DevOps and DataOps function in high-speeds, multi-geography scenarios whilst accommodating lots of users, both need a unified management environment, where monitoring, and cataloguing can happen concurrently.

The ‘Factory-model’ in DataOps

Whereas Agile and DevOps relate to analytics development and deployment, data analytics additionally manages and orchestrates a data pipeline. Data continuously enters on one side of the pipeline, progresses through a series of steps and exits in the form of reports, models and views. The data pipeline is the ‘operations’ side of data analytics and is called ‘data factory’, just like in a manufacturing line where quality, efficiency, constraints and uptime need to be managed.

The Intellectual heritage of Data Ops

DataOps combines agile philosophies, DevOps principles, and Statistical process control (a lean engineering tool) in balancing four critical Data aspects, namely, engineering, integration, security and quality – to uniquely manage an enterprise-critical data operations pipeline.

Andy Palmer

Banking and DataOps

Broadly speaking, DataOps targets improving data and analytics quality, reducing cycle times for creating new analytics, and increasing the productivity of the data organization exponentially.

More so, Banks have an additional criticality in adopting DataOps powered infrastructure: that of preventing (and bouncing back) from system breaches, platform outages, and process malfunctions that inevitably erode consumer experience and trust.

As DataOps powered platforms start to play a more pivotal role, banking organizations will realise that data needed to fuel innovation cannot be left siloed in legacy applications. Accurate data for precise decisions at the exact speeds will not only combat bottlenecks and outages but DataOps platforms that combine on-demand data delivery with data compliance will boost Customer experience transformation and innovation – especially as  more customers shift to online and mobile banking channels in the current pandemic crisis.

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Top 3 Technology Focal Areas in Banking in 2021

Top 3 Technology Focal Areas in Banking in 2021

It is crystal gazing time and as COVID pandemic would indicate yearly predictions can be a risky exercise. 2020 saw forced changes across Industries and geographies. For cost optimizations retail banks embraced intelligent and leaner operational models by leveraging open ecosystems. Cloud migrations with their advantages of security, data analytics, and storage assisted banks to pursue digital transformations, revamp business models and accelerate innovation capabilities. Additionally, Banking as a Service (BaaS) is enabling banks to monetize their data, services and infrastructure as consumable API’s for third parties, and co-create new products with faster time to market.

Along with the megatrends that COVID accelerated, namely Digitization, workplace virtualization, Safety surveillance, Cost reduction focus and new-age ecosystems; here is a round-up of the top three technology focus areas in the Banking sector.

  • Use of AI and Data for Hyper-personalization: Micro-segmenting as a digital strategy will underpin the shift from mass production to mass personalization. Banks that treat customers as segments by themselves will gain loyalty, increase lifetime value and reduce churn. 2021 will see banks ramping their data capabilities and advanced analytics to tailor real time financial recommendations. Surgical precision will permeate hyper-relevant content, hyper-specialized products, customized pricing.
  • Green Banking: Expect this year to be an inflection point banks to focus on managing climate risks, adopting green operations, and developing green products. From motivating customers to opt for zero emission vehicles with clean-energy loans to zero processing fees on electric vehicles, to manufacturing cards from recycled plastic, and creating a corporate position for a chief sustainability officer; corporations world over are pursuing green banking to counter environmental and climate risks by incorporating them into their corporate governance.
  • Voice Technologies and Autonomous finance: Technology trends, often, are driven by Gen Z habits. Present day voice assistants tell us everything from weather forecasts, stock market data, plays songs or offer road directions. In 2021, expect voice technologies to play a larger role in autonomous finance. They will reshape the way people interact with money and its service choices. From basic information, and account services, to using voice as one of biometric data security features, the ways of autonomous finance to power customer convenience will permeate daily banking processes. Banks will make serious progress with AI and ML managing user money across the vast portfolio of products and services.

In summary, 2021 will be a year of consolidation of all that the previous year accelerated – Digital banking, E Commerce, Contactless payments, AI advances in security etc.

In fact, as the year progresses, expect financial organizations to consolidate gains that have come from: Redefining the future of workforces and workplaces, humanizing digital experiences, and the neo-normal benchmarks set for Customer centricity.

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Evolution Of The Data Analytics Industry

Evolution Of The Data Analytics Industry

As early as the year 1848 scientists discovered that the “Prefrontal Cortex” of the brain is a small area that defines the personality of an Individual. Later studies have shown that this area is responsible for higher order data processing, decision making and executive functions of human beings.

Drawing a loose analogy one could argue that the Data, Analytics function performs that role within an Organization. As the modern corporation has evolved, so has the “Prefrontal Cortex” over the last 30 years. And one would argue that this evolution and development will gather pace as we move ahead.

This piece examines this evolution over the last thirty years.

Analytics 1.0: The era of ‘Business Intelligence’

Organizations have always recorded, aggregated and analysed data about production processes, sales, and customer interactions. Data sets were small and stable in velocity to allow for segregation in data warehouse for analysis. However, more time was spent in preparing data for analysis and relatively little time on the analysis itself – which was painstakingly slow often taking weeks to perform.

At the start of the new millennium modelling of data for analytics received a boost thanks to Ralph Kimball and Bill Inmon who did some pioneering work. Analytics stepped into mainstream when the relational database came of age. Technology vendors came out with products like IBM DB2, Oracle V3, Sybase (SAP) and the first standardized SQL based decision support systems went live. Still most analytics efforts were focussed on Descriptive and Diagnostic outcomes. This era lasted till the early to middle of the millennium.

Internet goes Global: Enter Analytics 2.0

Amazon (1995), Hotmail (1996), PayPal (1998), Google (1998)

Early and Mid 2000s businesses recognised the need for powerful new tools to get ahead in the market. Many technology Innovator Companies sought ‘first mover’ advantage with accelerated new products – OLAP , Reporting, Data Mining and ETL. This led to the emergence of specialist tool vendors like Informatica ,Business Objects and SAS.

In mid to late 2000’s organizations shifted away from pure RDBMS to MPP (massively parallel processing), specialized toolsets, and advanced analytics – all in recognition of ‘DATA as a critical asset’. And data volumes grew dramatically as did the cost of storage and processing.

Analytics 3.0 starts as the World goes Social

LinkedIn (2003), Skype (2003), Facebook (2004), Twitter (2006)

In this age Web apps went into a hyper growth mode. More events, more users, more transactions and the start of the smart phone and connected era.

Technology players responded with massive multi-rack systems, 100’s of computing cores, and Terabytes of Storage. Distributed computing, advanced query plans, columnar data models and Re-programmable hardware. Major players created a new wave of MPP OLAP’s (Online Analytical Processing) platforms – Vertica (HP), Greenplum (Pivotal), Netezza (IBM), and Exadata (Oracle).

But soon organizations realise that Big Data could not fit or be analysed fast enough on a single server this led to the move to Hadoop and distributed parallel processing. To deal with relatively unstructured data, companies turned to a new class of databases known as NoSQL. New technologies – ‘In memory’ and ‘In database’ analytics were introduced for faster processing. Machine learning models started to be used for advanced analytics. The world of bland boring reports gave way to compelling and intuitive visualizations.

This era continues into the late 2000’s and early ‘10s, coinciding with the rise of the ‘Data Scientists’, the Open source revolution, Fast Data, API’s and IoT’s.

In 2013, it is recorded that WhatsApp in a day sends 31 billion messages and 700 million photos sent. These are unimaginably large data volumes and growing!

Analytics 4.0 : “Fast-Pervasive Data” is replacing “Big Data”

The next generation data scientists used both computational and analytical skills and business context to solve various problems. Analytics got embedded into decision and operational processes. As technology continues to push further – Streaming and Real time analytics is made possible by Apache Spark, Kafka among Open Source platforms. Today their new avatars are available on the leading cloud platforms enabling massive Streaming and complex analytics.

In summary much like the way Human Intelligence (Prefrontal Cortex) drove rapid progress, the Data, Analytics function is driving competitive edge in the world of business. The onus is on CxOs to drive the rapid evolution of data and analytics and integrate it into key business processes.

But one factor decision makers need to understand is the pace of change in Data Analytics technology. And how the right choices could be a major competitive differentiator for their business. As Theodore Roosevelt said “The more you know about the past, the better prepared you are for the future.

This was originally published on Business World website and is being reproduced here.

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