Analytics is the powerhouse for progressive organisations in today’s competitive world.  Businesses have realised the importance of having the right analytics organization backing business intelligence and advanced analytics. Strategies led by informative insights gathered from analytics can improve profitability by optimising revenue, cutting costs which in turn improves both top line and bottom line revenues for an organisation.

Analytics help businesses to make better decisions every day, ranging from strategic choices to everyday frontline decisions that marketers, financers, operations specialists exercise. It helps in understanding market trends, customer behaviour and patterns that set the business direction. Building the right analytics platform is not easy and it may sometime take years to build a world-class, centralized, data driven analytics platform.

Data consolidation challenges:

Having a mix of legacy systems and modern systems with wide variety of databases may lead to a challenge in data consolidation. Breaking down all data silos and extracting data from different sources alongside maintaining a single enterprise data lake is an effective solution to this. The huge volumes of data from different data sources are ingested into the lake by batch and stream processing every day.

In a scenario where data privacy challenges keeps looming, there is a need for compliance with Payment Card Industry Data Security Standard (PCI DSS). This will ensure standard encryption/decryption and hashing mechanism to protect the data. Dedicated risk and fraud management teams will further ensure data security.

Usage of Analytics:

The data lake processes 50 million records each day and then aggregates and summarises it chronologically to generate intuitive reports. The same data can be reused for running advanced analytics models. This data can be used in various functions like Operations, Business development, Marketing, CRM analytics, Social media and Fraud risk management.

Business development:

The machine learning models can forecast transactions volumes monthly/quarterly to help the businesses achieve the targets set for each product. Analytics provide insights about the product performance and their market share with their competitor’s landscape. Moreover, it helps business design and develop the right scheme of offers for the segmented customers as well as for quantifying the effectiveness of offers in bringing new business.

Fraud and risk management:

Traditionally, fraud and risk analytics is a set of business rules that was used to assign a risk score to every transaction. It has now slowly evolved into combining the power of algorithms and Hadoop streaming tools and technologies to detect online fraud by building risk profiles from historic data. Offline algorithms are built for anti-money laundering detection and prevention. Using fraud and risk management tools effectively to assess business risk and prevent breaches by predictive analysis of historical data is adding high value to financial businesses.

Social media analytics:

Companies collect social media data by using open source twitter and Facebook API’s and builds machine learning algorithms for analysing the positive and negative sentiment analysis. If done for a multitude of products, helps top management to take corrective actions on negative sentiments and resolve customer problems through social media messages.

Operational analytics:

The data generated by various resources such as servers, firewalls, switches and databases in the cloud and on-premise environments are monitored in real time to prevent downtime. The machine learning models can predict transaction growth year-on-year to help the organisation prepare a strategy for capacity planning and hardware expansion.

CRM Analytics:

Customer Support is resource intensive. A chatbot is a simple automated service that can instantly respond to clients’ questions and help them solve their issues. Chabot’s usually assist by solving simple tasks that only require a quick response, leaving more time for customer service representatives to focus on complex customer needs that demand high-touch interactions.

Tools and technologies:

Adopting open source technologies such as the Apache Hadoop ecosystem and deep learning tools, such as Tableau, Python, R, Hadoop, Hive, Apache Spark Apache Kafka, H base, Sqoop to name a few will help the products and the success factor attached to them by using effective analytics tools.