Use Cases

Implementing Data Lake Using Azure

An energy company with different verticals had to be set up with centralized data and a cloud engineering team. They had a central team of architects, who design the future state system, define what tools to use, define development standards and define DevOps processes. With Lera, separate project teams were identified to work with each business vertical, to follow clearly defined development and deployment standards and guidelines. Each team was enabled to adopt the same development methodologies and deployment processes. Code review process and deployments were strictly monitored or audited to ensure compliance.

Implementing Data Lake On CDH

A large agri-business has multiple business verticals, each vertical working in silos, and each vertical has their own project team. Many times they end up having separate data architecture and each project team adopts and follows different approaches for implementing data lake solutions. We standards for architecture and tools to be used by the different teams. We defined data ingestion approach, transformation approach and data access security to migrate and upgrade the infrastructure applications, thereby creating an effective transformation process that helped build a data lake on CDH.

Implementing Data Lake On Hadoop

Organizations face a huge challenge in processing big data from RDBMS systems in batch or real-time to set up a data lake on Hadoop to produce an output to suit their data warehouse or data mart. To do so requires transformation of the business layer to consume data from Hadoop. Our transformation accelerator provides a code-less framework and data engineers have to focus only on the business specific transformation and the other simple transformations after the raw data is ingested into the Hadoop environment. It is easy to validate or compare the results between old systems and the new consumption ready data on HIVE.

Improved Cyber Security

Data breaches involving financial service firms increased by 480% from 2017 to 2018. With each attack costing financial institutions millions, innovative solutions is a pressing need. Analytics can help detect anomalies, orchestrate and automate the response of organizations to threats. Our big data analytics can help you quantify and understand potential risks, determine security priorities and develop processes to counteract the damage in the eventuality of a breach. It helps you gain continuous insights to find critical threats faster and respond more efficiently by unifying responses across the organization.

Personalised Customer Experience

What matters to most customers in this year is greater personalization, more automated services, and easier access to services to capture market share. Big data gives you a full view on your business from customer behavior patterns to internal process efficiency and even broader market trends. This means you can make informed, data-driven decisions and, subsequently, obtain business results. Your data can give you valuable insights into user behaviour and help you optimize your customer experience accordingly. For example, by having a complete customer profile and exhaustive data on product engagement at hand, you can predict and prevent churn and achieve retention.

Price Optimization

Traditional pricing methods are influenced by internal perception, and further limited by lack of real-time market data resulting in simplistic pricing. With the right prices, your firm can draw in new customers while continuing to satisfy existing ones. FinONe uses Predictive Analytics to set a service fee specific for each customer or transaction. It maintains an accurate and updated Rolodex of all your customers (Golden Customer Record) integrated to a dashboard that visualizes and helps to make profitable pricing decisions that are sensitive to buyer behavior (also pricing algorithms leverage real-time market data), maximizing profit and income.

Upselling/Cross-selling

Insurance companies utilize external agents to reach the customers to sell their products. Using analytics, insurers can individually determine purchase probabilities. AI offers customers upselling and cross-selling tailor-made additions to insurance portfolios that have already been arranged. Historic data can be used to define patterns of change based on customer attributes and personalize offerings according to customer understanding. Thus insurers can determine demographic behavior and segregate the location-based cross-selling and upselling of the products to specific customers.

Churn Prediction/Prevention

It is difficult for insurers to gauge customer behaviour post contract termination. Using customer and transaction data as well as other information, Analytics is able to determine which customers are more likely to cancel contracts in the future. Text mining can be used to analyze messages from all input channels. Algorithms evaluate the customer’s mood and detect changes in mood over the course of time. This allows conclusions to be drawn about customer satisfaction and the likelihood of churn. Insurers can analyze customer behavior towards the product, personalize offerings and take preventive measures to retain the customers.

Better Patient Care

Health care organizations generate copious amounts of data every day through Electronic Medical Records (EMR), Electronic Health Records(EHR), billing, clinical systems and research. With the help of advanced data analytics we can revolutionize patient care by improving care delivery. Visualization tools and analytics strategies can model patient patterns and highlight opportunities to make work adjustments and scheduling changes. LERA’s solution analysis collects data from various sources which helps the health care organizations treat their patients in a holistic manner and provide personalized care to enhance health outcomes.

Drug Discovery

Cost optimization in Drug discovery is a key strategy for pharmaceutical companies. However, improperly designed clinical trials, unsuitable patient population, and lack of competitive differentiation result in drug failures. Analytics can transform growth by mapping clinical trial trends to identifying risks, highlighting the gaps in the efficacy and safety of drugs and narrowing down on test sites, accelerating drug discovery and development. LERA’s solution analysis helps the health care organizations treat patients holistically and provide personalized care to enhance health outcomes and help the organization to understand the patient’s needs.