Data Warehouse Foundation – 4 weeks implementation

Most companies generate an impressive volume of data from many data silos like ERPs, CRMs, e-commerce, web & app analytics, loyalty programs, ad campaigns, social media and more. However, just because a company has these data, it does not mean that it is being used as a strategic asset to better engage customers, transform products, empower people, and optimize operations.

You can unlock the power of your data and enable faster business insights for your users by building a unified and usable data warehouse, customized by R Systems for your company’s needs.

You can ingest structured data like transactional databases or semi-structured data like spreadsheets, XML or CSV files that reside in the cloud or on-prem, into a data platform that provides high-availability, low costs, and consistency. You can control the flow of cleaning, moving, and transforming data by building pipelines that can be triggered manually or automatically (ETL processes). The processed data can be published into an Azure SQL Database and visualized using a BI solution such as Power BI for analytics.

By centralizing your data into a SQL Database, you will get a scalable, fully managed database engine with built-in high availability of 99.99% SLA, intelligence, and automatic performance monitoring. You can easily audit and comply with international security standards like GDPR, HIPAA and more, using always-on encryption, data masking and raw-level security for your databases.

Potential benefits of DW Foundation can include:

• Data consistency: data from multiple formats is converted into one standard format

• Accessibility: end-user access to a wide range of enterprise data and run simple queries and reports without being experts in data science

• Auditability: data warehousing provides secure access to those that have a legitimate need for a specific data

• Data sanitation: data is cleaned so that duplicates or errors reflected in queries and reports do not lead to inaccurate insights

• Data Dashboarding: data stored in the DW can be displayed in interactive dashboards in highly graphical forms

Use cases for DW Foundation services:

• Analyze and query data from different sources to drive business insights:

o customer segmentation

o acquire, retain, and grow customers by enabling better marketing and sales campaigns

• Use DW as a base to apply ML and AI to identify historical trends and apply them effectively to future situations

o Trendspotting: ex. pizza delivery orders increase during rainy days

o Purchase predictions: ex. make predictions about purchase propensity for separate groups of customers that can be synchronized into marketing platforms

• Customer/Product profitability: ex. create a comprehensive customer 360 profile and understand if a customer generates a revenue stream greater than the cost of their acquisition, selling, and serving

• Invoice reconciliation: ex. match bank statements to the outgoing and incoming invoices to make sure that all accounts are clean, and every book entry is correctly matched

DW Foundation consulting service has the following workflow and deliverables:

• We start with an assessment to identify relevant source of data

• Then, we design the architecture for ingesting all these data in a common data warehouse

• The data will be moved automatically using import/export pipelines. The data will be formatted, cleaned, validated, summarized, reorganized, and stored in the warehouse for reporting in a BI Dashboard

• Develop the software components, testing and migrating to production workloads.

• Training and managed services

DW Foundation will be implemented using modern cloud solutions available in Microsoft Azure and will be designed following cloud adoption framework. An example of such architecture can include the following:

• Logic Apps/Event Grid

• Storage account

• Data Factory

• SQL Database

• Power BI (the BI license in not included in the consulting service offering; the customer can choose his preferred BI solution)

This scenario is suitable for companies interested in building a Data Warehouse with small to medium data sets (up to 1-4 TB), with structured and semi-structured data, that do not require that complex queries should be resolved at high speeds.