Data Profile
Data profiling is the process of examining the data available from an existing information source (e.g. a database or a file) and collecting statistics or informative summaries about that data. You profile data to determine the accuracy, completeness, and validity of your data. Our Data profiling tool is built to run on variety of platforms including on-premise and Cloud (both on Azure and AWS). It enables capturing the following:
1. Collecting descriptive statistics like min, max, count and sum.
2. Collecting data types, length and recurring patterns.
3. Tagging data with keywords, descriptions or categories.
4. Performing data quality assessment, risk of performing joins on the data.
5. Discovering metadata and assessing its accuracy.
Please contact our technical person to download or know more about the tool.

2. Data Quality
Data quality is a measure of the condition of data based on factors such as accuracy, completeness, consistency, reliability and whether it’s up to date (also described as timeliness). Data quality is the process of conditioning data to meet the specific needs of business users. Data is organization’s most valuable asset, and decisions based on flawed data can have a detrimental impact on the business.

Our Data Quality tool helps with improving quality of data by executing business rules. These business rules are externalized and can be configued to suit business user needs. No coding is required to configure these rules. In some complex cases code needs to be written which is minimal. Supports both AWS and Azure platforms.
Some of the examples of improving data quality are as following:
1. Validate address format
2. Validate the address is formatted correctly and that it exists
3. Phone number formatting and validation
4. Financial data (like credit card number, SSN and etc.)
5. Can define customized rules that are required by the business
Please contact our technical person to demo, download or know more about the tool.

3. Data Lake
Data Lake is a storage repository that can store large amount of structured, semi-structured, and unstructured data. It is a place to store every type of data in its native format with no fixed limits on account size or file. It offers high data quantity to increase analytic performance and native integration.

Our Data Lake component allows you to ingest and import any type and amount of data without having to write single line of code. It can be customized by the type of feed and supports both batch and real-time systems. This process allows you to scale to data of any size, while saving time of defining data structures, schema, and transformations. Both Azure and AWS are supported by this component.

Components of the data lake are as follows:
1. Data pre-processing – helps with streamlining different data sources
2. Data Ingestion – enables ingesting and importing of data into the data lake
3. Data Standardization – cleanses the data to standardize for down-stream consumption in a consistent way
4. Data Aggregation – allows users to specify data required to procure on demand
5. Collections – provides data sets as and when required to consumption systems
Please contact our technical person to demo, download or know more about the tool.

4. Data Masking
Masking of data ensures that sensitive data is replaced with realistic but not real data in testing environment thus achieving both the aims – protecting sensitive data and ensuring that test data is valid and testable. This is a data security technique in which a dataset is copied but with sensitive data obfuscated. This benign replica is then used instead of the authentic data for testing or training purposes.

Our data masking component which is supported on AWS and Azure supports the following major techniques used in the Industry:
1. Static data masking – masking the data at the source
2. Dynamic data masking – masking the data during execution
3. Encryption – encrypting the data per business requirements across environments

Industry standard techniques are employed to maintain the referential integrity between data entities. This helps reduce confusion during the testing cycle.
Please contact our technical person to demo, download or know more about the tool.

5. Master Data Management
Master Data Management (MDM) is the technology, tools and processes that ensure master data is coordinated across the enterprise. MDM provides a unified master data service that provides accurate, consistent and complete master data across the enterprise and to business partners. It combines the software tools, data collection processes, and IT technology for enterprise-wide coordination of data to increase the accuracy and consistency of data gleaned from across your business.

Our MDM tool is no-code configurable tool developed on Cloud (Azure and AWS) that supports mastering Customer, Product, Fund, Dealer, Location and other main entities of the Organization. It leverages standing algorithms to match data across disparate sources.

This cloud native tool supports the following features:
1. Standardizing the data across sources
2. Cleaning the data as required by business rules
3. Matching of data across sources
4. Merging the data based on trust scores provided by system and attribute
5. Removes duplicate data – deduplication process
6. Creation of Golden record for consuming systems
Please contact our technical person to demo, download or know more about the tool.