The key component shaping new-gen digital economy is Data with its swift technological advancements. Enabling large volumes of data flowing-in via multi-touchpoints, without the competence to store, structure, and analyse data, makes it rather ineffectual. An Enterprise –level unified and integrated data lake can facilitate smart data driven decision-making.

Aspire's Enterprise Data Lake solutions simplify speedy data discovery and analysis of legacy data, customer trends, patterns across different offerings and the organization at large. We deliver:

Technology-agnostic customized Framework
Persist Data in its raw form
Cloud Data Lake
Data Integrated System
Data Governance & Data Security
Advanced Analytics & Machine Learning
Enterprise Collaboration
Intelligent Data Lake Management

Key Features

The potent features of Date Lake will bring you bouquet of opportunities.

One Stop Shop

A data lake enables the enterprise in measuring the performance; audit requirements for financial organizations can be easily served

Power of Real-time Analytics

Simulation of future performance with current data becomes possible. Eg. Financial Advisors in Wealth Banking can perform a simulation of how well they are performing with regards to a client’s investment

Smart Data Collaboration

Data from enterprise applications, legacy applications, streaming data from IoT devices, social media, data from machine logs, mobile apps, click-streams are key to analytics and better decision making

Jump-start ROI

Organizations investing in a data lake have shown a 9% increase in revenue (Aberdeen Survey).Empowering business users to perform timely analysis

Data lake on Cloud

Reduces operational costs, offers higher availability and scalability

Automate Data Discovery

Real time analytics, full text search, Machine Learning can be performed to uncover insights and to automate timely business decisions

Data Lake Versus Data Warehouse

Data Lake	Data Warehouse
Will not replace Data Warehouse	Evolution of data warehouses into data lakes will increase
Perform Advanced and Real-time analytics	Perform "As-Is Reporting"
May or may not contain curated data	Will contain curated data

Building a Data Lake

What are the best practices in building a Data Lake that can withstand the onslaughts of increasing amounts of data
and automate the data discovery process seamlessly

A data lake should be well suited to cater to both "data in motion" and "data at rest". It should imbibe the 5 V's Volume, Velocity, Variety, Veracity and Value
Focus on building a golden record of master data, thereby increasing the 3 C's of data Correctness, Completeness and Consistency
Enable Data Discovery would mine data effectively, enable rapid and faster ingestion of data. It is ELT unlike ETL in traditional warehouses
Predominantly enable Data Security and Data Governance to assign the rights and responsibilities as to who can load what data, who can read, etc
Help users understand data, how to read / analyse with Data Catalog
Persist data in its raw form
Facilitate Data Transformation when reading data at a high speed
Maintain a complete track of how data has changed over a period of time - Data Lineage

Use-cases

Customer Intelligence

A domain that is horizontal to any vertical domain like Banking, Retail, and Manufacturing etc. Organizations can derive upteem benefits from a data lake like:

Data Lake is a Confluence of all internal and external data in an organization
Data related to Stakeholders, Employees, Customers and Market all can be stored in a data lake
A source of “Customer 360 view” giving an opportunity of all the relationships a customer has with an organization
Easily ingest external data (unstructured data) like Facebook, LinkedIn Twitter data of a customer (with customer’s consent). This helps an organization with the below:
- Customer 720 View - A behaviour pattern of customers based on both internal and external data)
- Customer Journey – Indicates to the organization of their customer experience specific to the products
- Customer Persona – Helps create a personality data base of each segment of customers that an organization has
- Ideal Customer Profile – An organization can look into its data lake to understand where it has succeeded and create an optimal practice model by creating an Ideal Customer Profile
Increasing Lead Pipeline – With so much of customer intelligence available, the sales and marketing teams can easily increase their lead pipelines. Examples sfdc’s data.com

Banking

The diverse operations in Banking lead to multiplication of data and how a data lake can add value:

Real-time fraud detection in card payments
Risk Scoring of Payments in Real-time & Batch
Detecting Anti-Money Laundering
Building Customer 720 view to understand your customer better; Cross-sell and Up-sell banking products
Faster Regulatory Reporting
Merchant Analytics - provide sophisticated analytics to merchants as a way of driving merchant rewards programs.

Retail, Restaurants & Ecommerce

Building a data lake will reap the organisation benefits such as:
- Understand customer behaviour to offer real-time recommendations / promotions to increase items in shopping cart
- Personalize in-store experience
- Increase conversion rates using predictive analytics
- Customer Sentiment Analysis to measure brand sentiment
- Market Basket Analysis to understand and analyse customer preferences over your products