Contextual analytics: Realize data potential to the fullest
7 MINS READ
Most modern-day organizations across industries utilize deep data analytics to drive business decisions across all strategy, growth, risk, operations, finance, and human resources.
While analytics has gained importance as the most valuable tool for business decisions, its contextuality makes it practical and business-oriented, especially for banking, financial services, and insurance (BFSI) firms.
However, a 360-degree approach to contextual analytics can only be achieved when BFSI firms truly understand their business context by analyzing the data in the connected ecosystem in which they operate. Rapid digitalization has created an abundance of data that fuels the contextualization of business analytics for decision-making. BFSI firms must analyze this data to generate meaningful insights, which is crucial for building sustainable business models.
Making the most of the data ecosystem.
For years, the banking, financial services, and insurance (BFSI) sector has been using data generated from its business systems to analyze performance with relevant metrics and key performance indicators (KPIs).
These KPIs provide a significant understanding of the prevailing state of affairs and identify the areas to focus on for varied optimizations. It is essential to understand which optimization lever is practicable in relation to external factors such as the geography of operation, socio-economic stature, and the market dynamics, including peer actions. BFSI firms should introspect if they are optimizing their investment of time and effort or if there are any parameters beyond their control impeding the optimization effort and how their peers and new entrants are approaching similar situations.
Our experience shows that while seeking such answers, firms primarily depend on abstract qualitative understanding, personal intuition, and some outdated metrics, which cannot be tied to defining internal KPIs in most cases. To be context aware, BFSI firms need to utilize the enormous value hidden in the data ecosystem effectively and precisely.
The BFSI context
Contextualization focuses on the scientific understanding of the business ecosystem within which a BFSI firm operates.
The foundation of contextualization is built by gathering data points around the parameters of the ecosystem, which include the depth and breadth of the market and allied services, the efficacy of resource planning, and utilization. Once the data points are gathered, contextualization demands identifying the inter-relationship among those parameters, which leads to meaningful insights. When these insights merge into a BFSI firm’s internal KPIs, the contextualization comes full circle.
For instance, a global bank might be focusing on identifying micro levers to capture and retain market share in highly competitive, conventionally high-income geographies and segments. In this case, the presumption is that profitability is directly traceable to the conventional spectrum of income levels. However, there can be a justifiable business case to target the quick wins in the under-served middle-income localities or high-earning freelancer segments.
Similarly, an increase in the overall life expectancy, the society’s behavioral shift toward a more adventurous lifestyle, and cross-border collaborations at the business-to-consumer (B2C) and business-to-business (B2B) levels give rise to newer risk identification parameters that need to be factored into future insurance product design. Marketing teams in retirement planning organizations may be tempted to design strategies based on demographic characteristics and asset classes. Financial services organizations factoring in the impact of mass migrations and asset risks triggered by pandemics or geopolitical situations in retirement product and service design will reap significant benefits.
Correct use of data will avoid guesstimates or intuition-based contextualization. For example, while a bank might register top-class performance in the time-tested segments, locations, and products, it might show superiority in comparison with the peer group. However, as the ecosystem in which it operates is constantly changing and the demand is shifting toward context-aware products and services, the bank will miss the opportunity if the context is not understood. Early detection of such shifts and deep insights on disruptors in the form of new entrants with newer products as well as cross-segment analysis of established peers is critical to embark on large-scale transformation and growth. Consequently, contextual analytics is necessary for deciding the right marketing strategy, customer engagement, workforce mobilization, and sustainability. The approach needs to be sophisticated, on time, and relevant to company-specific KPIs to facilitate quick and accurate decision-making and reflects a holistic combination of inward and outward views. Extensive parameters need to be defined by looking at the specific business domains. It is also essential to quantify and logically connect those parameters to the bank’s or insurer’s KPIs for tangible comparison.
In our view, without proper contextualization of analytics, BFSI players will face several hurdles and will remain followers rather than leaders. This includes spending huge resources, assuming unrealistic objectives, failing to take timely action in a changing scenario, and missing opportunities in supply chain upgrades. In addition, traditional banks and insurers will be slow to acquire depth in niche areas, while newer firms will be limited by scale. In short, while the past decade has been about the advancement of analytics, hereon, the focus should be on contextualization.
Multiple challenges hinder BFSI organizations from utilizing the right ecosystem data at the right time to make their analytics contextual.
Let us examine the critical challenges firms face and how they can be tackled:
Lacking direction: BFSI firms fail to deploy enough resources to address the contextualization gap as the value of analytics contextualization is yet to be understood. However, a robust design-thinking approach would propel them in the right direction.
Insufficient sponsorship: This stems from a lack of direction among organization leaders. If the strategic direction and subsequent benefits are unclear, the expected sponsorship may not materialize. Expert storytelling and articulation by skilled business and technology leaders could help in gaining sponsorships.
Infrastructure overhead: Often, the thought of infrastructure overhead in terms of inadequate optimization and high demand for maintenance outweighs the prospect of greenfield initiatives. The latest progress in professional cloud providers and multiple product alliances has drastically reduced infrastructure costs and resource demand.
Technical complexity: At the onset, creating an effective amalgamation of ecosystem data with a wide range of variation and volume might seem complex. However, experienced professionals would be able to implement an effective data platform with the right mix of technologies like big data, not-only structured query language (No SQL), and a graph database (GDB) to churn out business value.
Risk of unsuccessful outcome: While future-ready nimble organizations are already convinced and are taking steps toward bringing in contextuality, the notion of contextuality might seem like an ‘out-of-the-box’ idea for industry followers. A prioritized use case-driven approach can prove fruitful in demonstrating the outcome to skeptics.
While BFSI firms can take up bespoke technical solution implementation, a structured approach will have far-reaching benefits. A holistic broad-level approach (see Figure 1) to creating a framework for achieving contextual analytics will help realize the potential and unlock exponential value that differentiates them from peers.
Figure 1: Holistic broad-level approach
Once the challenges are addressed, and initiatives are undertaken, the opportunities to utilize the insights are enormous. These opportunities will influence varied business performance pillars (see Figure 2).
Figure 2: Gross benefit categories
Putting contextual analytics to work
BFSI firms need to have context sensitivity from the ecosystem factored into their analytics initiatives.
Factoring in context sensitivity will help firms create specific, measurable, achievable, relevant, and time bound (S.M.A.R.T.) goals in the true sense. The key to success in contextualization depends on how well the insights are connected to KPIs at a granular layer, how objectively the benefits are quantified to influence expected business outcomes, and how scientifically the firm-specific interventions are prioritized. The entire contextualization journey must be traceable to the senior management to gain strategic importance. Operational level budget allocation, program governance, talent mobilization, and finally, execution and monitoring will automatically follow.
To begin with, taking an iterative approach will gradually benefit realization and incorporate lessons learned in a continuous feedback loop. Contextualization initiatives must be compliant with all forms of intellectual property (IP) rights as well as data protection regulations, as mandated by federal and local supervisory bodies. These include the Fair Credit Reporting Act (FCRA), Fair and Accurate Credit Transactions Act (FACTA), Health Insurance Portability and Accountability Act (HIPAA), Gramm-Leach-Bliley Act (GLBA), California Consumer Privacy Act (CCPA), and General Data Protection Regulation (GDPR). In addition, talent identification should ensure deep industry and regulatory knowledge combined with analytical skills to enable a successful outcome. Embedding agility in the process will ensure a nimble approach that is adaptable to business and regulatory changes. In our viewpoint, BFSI firms must adopt contextual analytics as a critical strategic initiative by employing the right process and the right expertise to create exponential value and stay ahead of the curve.