Data Warehouse

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Data Warehouse

At Paradigm SES, we enable our clients to ensure that high data quality exists throughout the complete lifecycle of their data. We help companies develop processes to ensure effective data management throughout the enterprise such as accountability for the adverse effects of poor data quality and ensuring that the data an enterprise has, which can be used by the entire organization.

Our strategy considers the people, processes, and information technology required to create a consistent and proper handling of your organization's data across the business enterprise. We provide all kinds of data management practices with the necessary foundation, strategy, and structure needed to ensure that data is managed as an asset and transformed into meaningful information.

EXPERIENCE

Our team at Paradigm SES has over 17+ years of experience in Enterprise Performance Management. Our Data Management services include data migration, data cleansing, data warehousing and Data Mapping.

The consultants of our company are efficient at our work and are well-versed and knowledgeable in data management.Data integration involves combining data residing in different sources and providing users with a unified view of them.

Data Migration

Our consultants advise companies on preparing, extracting, and transforming data to move it from On-Prem to Cloud. Data Validation is also performed to ensure data integrity. Data migration occurs because of several factors (such as data recovery, consolidation, maintenance or upgrades, and so forth). Business processes operate through a combo of human and application systems actions, which are then often orchestrated by business process management tools. When these change, it can require the movement of data from one store, database or application to another one to reflect the changes to the organization and information about customers, products and operations. Examples of some of these migration drivers are mergers and acquisitions, business optimization, and reorganization to attack new markets or respond to competitive threat.

The first two sections of migration are typically routine operational activities that the IT department takes care of without the rest of the business being involved. The last two sections directly affect the operational users of processes and applications. They are necessarily complex, and delivering them without significant business downtime can be challenging. A highly adaptive approach, concurrent synchronization, a business-oriented audit capability, and clear visibility of the migration for stakeholders—through a project management office or data governance team—are likely to be key requirements in such migrations.

Schema migration

We can help your company move from one database vendor to another, or upgrade the version of your current database software. For Physical Data Migrations, a physical transformation process may be required since the underlying data format can change significantly. This may or may not have an effect on behavior within the applications layer, depending largely on whether the data manipulation language or protocol has changed. Migrations from Sybase, MySQL, DB2 or SQL Server to Oracle (or vice versa) include a testing cycle to be ensure that both functional and non-functional performance has not been adversely affected. Applying a schema migration to a production database is always a risk to the databse. Development and test databases also tend to be smaller and cleaner. The data in these databases is better understood or, if everything else fails, the amount of data is small enough for a human to process. Production databases are typically huge, old and full of surprises. The surprises can come from many sources:

For these reasons, the migration process needs a high level of discipline, thorough testing and a sound backup strategy. For more information, click here to learn more.

Data Cleansing

Paradigm SES offers Data Quality Assurance to discover inconsistencies and other anomalies in your company's data. We offer data cleansing services (e.g. removing outliers, missing data interpolation, etc.) to improve the data quality. Data Preparation is typically the first step in data analytics projects. It can include many discrete tasks such as loading data or data ingestion, data fusion, data cleaning, data augmentation, and data delivery.

We make take the hassle out of the data management process by handling:

  1. systematic errors involving large numbers of data records (data coming in from different sources)
  2. individual errors affecting small numbers of data records (data entry errors)
Once this process is complete, the data set that was just cleaned is consistent with the other cleaned data sets. An example of a Data Cleansing Project can be found in this Case Study.

ETL (Extract, Transform, Load)

ETL is a process that extracts data from different relational database management source systems, then transforms the data and finally loads the data into the Data Warehouse system. It is a tool that helps companies analyze their data to make critical business decisions. As sources change, the Data Warehouse will automatically update because of the recurring activity of ETL. It also helps to migrate data and converts to various formats and types to accomodate one consistent system.

Step 1: Extraction

Data is extracted from different source systems into the staging area. The staging area validates extracted data before it moves into the Data Warehouse.

Step 2: Transformation

Transformations are done in the staging area so that performance of source systems are not corrupt. The data that was extracted is raw which will then be cleansed, mapped and transformed.

Step 3: Load

A volume of data is loaded in a relatively short period (nights). Therefore, the loading process should be optimized for performance. In case of loading failure, recover mechanisms should be reconfigured to restart without data integrity loss.