Can we really call it "Big" Data ?

Introduction

?'Big data analysis' has created a lot of buzz in recent times and several sectors have identified that it can be a big game changer. With the increasing adoption of this technology, all the modern industries are finding the real value of big data analysis. Basically, it refers to the scrutiny of large data sets including a variety of data types (big data) to unveil market trends, hidden patterns, customer preferences, unknown correlations and other useful information. Such analysis has proved to be highly effective in devising strategies for new revenue opportunities, better services, effective marketing, improving operational efficiency and many more tasks. It also helps the organizations and entities to stay ahead of competitors and derive maximum benefit.

Big data describes the volume of both structured and unstructured data that businesses capture on a regular basis and the methodologies used to manage and analyse it. The concept was popularized in 2001 by Doug Laney, an industry analyst who broke down its definition into the three V’s: volume, velocity, and variety.

Big Data – And it’s 7 V’s:

• Volume refers to the ever-increasing amount of data collected. The scale of big data is what earned it its name and is partly what makes it so important to know how to manage it.
• Velocity refers to the accelerating speed at which data is created and processed. Big data has made it possible to process data in real-time.
• Variety refers to the different forms and sources of data. In fact, 90% of data created is “unstructured,” meaning it is not easily captured, searched, or analysed.

In 2013, Mark van Rijmenam published an article that introduced four more V’s to further define the complexity of big data:

• Veracity refers to the reliability of data. While data is powerful, having inaccurate data is worthless. Programs, models, and analyses are only as dependable as the data on which they are built.
• Variability refers to the uncertain nature of data whose meaning is constantly changing. As data becomes more and more complex, the context in which the data originated largely influences the meaning of the data itself.
• Visualization refers to the need to present data in an accessible and meaningful way. The reporting functionality of big data is equally as important as the processing and analysis.
• Value refers to the positive business outcomes created by a data solution. Businesses should focus on getting maximum value from their big data strategy. Ultimately, the ability to become more efficient, proactive, and predictive can lead to a substantial competitive advantage.

Need for Big Data

‘Big Data’ is taking the world by storm. Think data analysis, and you might visualize statisticians sifting rivers of neatly organised numbers (smartphone, sensor data, think space lab results - the ocean of information that is the Internet) for insights into everything from stone age to online shopping trends of this contemporary world. Data has become a very integral part of human life (Oops! I shouldn’t have limited myself to only Humans).

Financial planning and trading applications offered by Banks, through smartphones and social media; cloud technologies are these days being widely accepted, and in many cases robotics are already reducing cost and increasing quality. The number of fintech start-ups (technology-based companies that often compete against traditional financial-services) has risen more than 50 percent, Since 2011.

"Data really powers everything that we do." – Jeff Weiner, chief executive of LinkedIn.

Historically, this data has been largely underutilized but with advances in technology, they are now able tap into different types of unstructured data – machine data from ATMs and servers, social data from Facebook and Twitter, clickstream data from websites, voice logs from call centres, communication data from e-mails, etc.

The figure below clearly summarizes the views of CEOs of most of the prominent companies as to how prepared and open their organisations are towards Predictive analytics.

Source: PwC 2020 Banking Survey

Fig 1: CEOs from various organisations say their firms are unprepared for innovation

Data is about answering questions, Big data is about answering big questions, & some of the biggest questions are often asked financial services industry. Big data in financial services makes it easier for predicting financial trends and protecting investors from disaster.

Emerging Trends in Telecommunication Sector

Telecom industry, in India has crossed two decades post privatization of this sector. Since then to summarize it in short - "Innovation, consolidation, and maturation", leaving behind 12 major mobile telecom operators operating in the

country. Presently, the total revenue of telecom operators is about ~ ? 2 trillion with a burden of ~ ? 2.5 trillion debt with a waning voice and SMS revenue. This is happening due to price wars in case of voice and declining SMS revenue due to the advent of new instant messaging applications, like WhatsApp, Hike, Google Duo etc. Operators are facing rapidly changing business rules, disruptive technologies, intensified regulatory environment leading to eroding service margins.

In today's scenario, where we have Airtel with huge market share, Idea & Vodafone coming together to fight it out with Airtel, Jio investing heavily in 4G VoLTE segment and total telecom industry experiencing chaos - the only stabilizing factor for telcos is revenue expected to be generated from data provisioning and deriving value from this data. Telcos can use advanced analytics on customer and network data to generate a 'real-time view' of customer preferences and network efficiency. This could empower them to make near real-time and fact-based decisions and hence enable a forward looking, focused, decisive, and action-oriented culture in the company.

“As we see the wave of DATA taking over the VOICE for telecom operators and the act that every customer is leaving his footprint across various transactions, it is equally important to churn these insights and convert into meaningful consumption form, so that entire team takes decision cohesively.

Areas where Telecom Industry can effectively leveraging data and analytics:

1.   Blockchain Technology

• The appeal lies in its ability to decentralize databases by linking separate transaction information together through a line of computer code, effectively doing away with a central governing body.
• This also offers a more secure and efficient way data sharing.

2. Sales & Marketing

• Influencing customer purchase behavior through real-time targeted and personalized offers
• Event-based marketing campaigns that use Geo-location and social media, allowing differentiated responses
• Cross- and up-sell targeting (new product, upgrade, feature, service)
• Sale of (anonymous) customer insights based on usage data to shops, media agencies, etc.
• New product/service innovation based on real-time usage patterns

3. Call Drop Analysis

• A large scale disruption or outage in network can lead to call drops and poor voice quality which "kills" the reputation of the service provider and can increase the attrition among its customers. Telecom companies should continuously monitor their networks for such disruptions and resolve root causes at the very early stages.
• Dissatisfied customers may sometimes be hesitant or too busy to report frequent call drops but would have a greater tendency to churn out in search of better services/coverage.

4. Churn Prediction

• Retaining customers is one of the most critical challenges in the maturing mobile telecommunications service industry. Prediction of customers who are at risk of leaving a company is called as churn prediction in telecommunication.

Acquiring a new customer is more expensive than retaining the old one.

• With help of predictive models and intelligent machine learning algorithms, it is possible to accurately identify customers who are likely to lapse. Bringing together data collected on customer usage, transactions, social media complaints, they can create factors which can identify customers at risk of moving out.
• Techniques such as Fuzzy Logic & decision trees, which enable long-term predictability and early detection of customer’s value loss and profiling, allow marketers to use variables to easily identify potential churners.

5. Identifying New Revenue Streams and Business Models

• In Indian economy, a rising middle and increased purchasing power coupled with massive data usage opens a plethora of opportunities for telecom companies.
• Asia-Pacific countries have the world’s largest middle-class growth, and with it, a huge population of millennials who have grown up in the digital world.

Emerging Trends in Financial Services Industry

‘Disruption’ is the right word to describe what Predictive analytics has done to financial services industry. 79% of Millennials across US identified financial services industry as most transformed sector when it comes to generating, storing & handling data in terms of both

volumes and variety. The changes in the banking and financial services industry in years to come will be seismic. Global banks are setting up development & thought centres and specialized teams to focus on blockchain, heralded as a disruptive force that offers multiple opportunities such as overhauling existing banking infrastructure, speeding up settlements and streamlining stock exchanges.

Source: IDC Reports

Fig 2: Global Financial – Services IT Expenditure

*Spending Projections are for 2019

Financial Service Industry as such is being continuously challenged by shrinking revenues and need to improve operational cost efficiencies. Rising fin-tech innovators and incumbent technology giants are deploying new business strategies & models causing disruption and challenging traditional financial service business models. Regulators across all the geographies are demanding stricter compliance and stronger financial discipline, from the Federal Reserve in the US to the European Banking Authority (EBA) in the EU, the Prudential Regulation Authority (PRA) and the Financial Conduct Authority (FCA) in the UK.

Financial institutions have the benefits of large customer bases and access to rich transactional data. Creating newer business models or frameworks that leverages the available data allows financial institutions to monetize data to deliver superior customer value. Winning in this dynamic market will be underpinned by how the financial institutions can derive value from data. The convergence of machine and human intelligence is disrupting traditional decision-making by equipping organizations with knowledge and insight to predict and prescribe business outcomes. Advances in big data and analytics have led to new products, solutions and services making financial institutions smarter, agile and more competitive. Newer regulatory and compliance requirements, fraud and anti-money laundering preventive steps are placing more emphasis on stronger governance and risk management. Data security and data protection is gaining significance. This is driving up operating expenses necessitating financial institutions to explore avenues to improve operational efficiencies.

Key trends in data analytics reshaping the financial services industry:

• Enormously huge data sets to analyse to reveal patterns, trends and correlations
• Real-time predictive & prescriptive analytics for driving dynamic & deep actionable insights
• Risk & compliance demanding availability of reliable, measurable & secure data
• Adoption of (Internet of Technology) machine learning and cognitive capabilities
• Democratization of data enabling more self-service (Customer Friendly)
• Consumerization of Business Intelligence through best-of-breed data discovery, exploration and visualization tools
• Digital platforms powered by holistic view of customers
• Data and Business Intelligence landscape transformation and modernization to reduce cost and embrace new-age technologies
• Data Warehousing - Analytical master data management capabilities
• Strengthening data governance capabilities

Areas where financial institutions are effectively leveraging data and analytics:

1.   Fraud Detection, Security & operations

• Analysing customer data to achieve customer intimacy by providing next best action and customer lifecycle interventions
• Mandiant’s 2013 Threat report indicates that 63 percent of all data breaches are reported by third parties, and the median number of days before detection is 243.

2. Governance, Risk, and Compliance

• To demonstrate good governance, risk, and compliance (GRC) one needs a real-time picture of entire financial operation. Using big data in financial services industry can bring together all the GRC data for analysis.
• This not only minimizes risk but also promotes compliance in the event of an audit
• All the performance data is on one place for easy access.

3. Consumer and commercial banking

• Supporting consumer analytical models focused on customer lifetime value analysis, customer call centre analytics and deposit growth analytics
• Voice of customer analytics to measure customer sentiment in social media
• Supporting 360 view to enable cross-sell and upsell

4. Increased Interest in Analysts with Experience in Python and R

• With enormously growing amounts of data to be analysed, the open source languages such as Python and R are becoming increasingly popular.
• Python allows for collaboration with other users and is considered “general use”
• Both have data visualization capabilities, although R is slightly superior in that regard.

5. Capital markets, cards, wallets and payments

• Augmenting card and customer data with new-age parameters to derive competitive product pricing models, discounts, festive offers, innovative rewards, assess credit worthiness for underwriting and recommend optimal lines of credit.
• Deriving deeper insights into dynamic portfolio performance, liquidity positions and working capital requirements.

Conclusion

Telecom and Financial institutions are rapidly transforming themselves into “data-driven” organizations with data collection and analysis underpinning their business strategy and day-to-day decision-making. “Data” in such institutions has reached the C-suite level with most organizations appointing Chief Data Officers (CDOs) who are responsible for defining the enterprise-wide data and information strategy, governance and quality, controls and policies.

In very near future, “Big Data” will simply become “Data,” as the adoption of these trends and technologies would become a necessity from a luxury. For now, big data remains a burning topic. We can therefore expect an increasing number of industry leaders to investigate and embrace as they seek to maintain their distinct competitive advantage.

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