With over 20 years technical experience, we have the required systems, solutions and skills to make it happen.
DMIX builds custom integration for CRM systems like Salesforce and Microsoft CRM 365 into ERP systems like Microsoft Dynamics 2012 and 365 to others such as SAP and Sage. This is done using the latest cloud technology platform Microsoft Azure on a pay-as-you-go basis thus eliminating the need for upfront capital expenditure on the necessary IT infrastructure.
DMIX data modelling expertise specifically in the Finance and Insurance Industry allows its consultants to design and implement data models required to support the analytical needs of any business whether it be algorithmic and/or visualization orientated.
DMIX has developed its own Messaging Service “Easy SMS” which provides a platform for a single user to a multi-national corporation to manage contacts in order to send and receive messages. Integration endpoints built using The Windows Communication Foundation (a runtime and a set of APIs in the Microsoft .NET Framework) allows for seamless integration into CRM and ERP systems.
Data Modelling IBM Information Framework. Data Warehousing based on Ralph Kimball. Data Storage either using traditional RDMS’s Microsoft, Oracle or NoSQL variants like Azure Cosmos DB.
CRM to ERP e.g. Salesforce to D365FO. CRM to Customer Send SMS or Email. Lead to CRM e.g. receive and action lead message response.
Metric and KPI rich data models e.g. Microsoft Tabular Cubes.
Interactive visualizations with drill through e.g. Microsoft PowerBI including advanced analytics using R integration.
Terminology and Concepts
Most frequent questions and answers
In computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis, and is considered a core component of business intelligence. DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data in one single place that are used for creating analytical reports for workers throughout the enterprise.
The data stored in the warehouse is uploaded from the operational systems (such as marketing or sales). The data may pass through an operational data store and may require data cleansing for additional operations to ensure data quality before it is used in the DW for reporting.
The typical extract, transform, load (ETL)-based data warehouse uses staging, data integration, and access layers to house its key functions. The staging layer or staging database stores raw data extracted from each of the disparate source data systems. The integration layer integrates the disparate data sets by transforming the data from the staging layer often storing this transformed data in an operational data store (ODS) database. The integrated data are then moved to yet another database, often called the data warehouse database, where the data is arranged into hierarchical groups, often called dimensions, and into facts and aggregate facts. The combination of facts and dimensions is sometimes called a star schema. The access layer helps users retrieve data.
The main source of the data is cleansed, transformed, catalogued, and made available for use by managers and other business professionals for data mining, online analytical processing, market research and decision support. However, the means to retrieve and analyze data, to extract, transform, and load data, and to manage the data dictionary are also considered essential components of a data warehousing system. Many references to data warehousing use this broader context. Thus, an expanded definition for data warehousing includes business intelligence tools, tools to extract, transform, and load data into the repository, and tools to manage and retrieve metadata. ~ Wikipedia
In the world of database technology, there are two main types of databases: SQL and NoSQL—or, relational databases and non-relational databases. The difference speaks to how they’re built, the type of information they store, and how they store it. Relational databases are structured, like phone books that store phone numbers and addresses. Non-relational databases are document-oriented and distributed, like file folders that hold everything from a person’s address and phone number to their Facebook likes and online shopping preferences. ~ Upwork
The following link has a very good explanation.
Customer relationship management (CRM) is a technology for managing all your company’s relationships and interactions with customers and potential customers. The goal is simple: Improve business relationships. A CRM system helps companies stay connected to customers, streamline processes, and improve profitability.
When people talk about CRM, they are usually referring to a CRM system, a tool that helps with contact management, sales management, productivity, and more.
A CRM solution helps you focus on your organization’s relationships with individual people — including customers, service users, colleagues, or suppliers — throughout your lifecycle with them, including finding new customers, winning their business, and providing support and additional services throughout the relationship. ~ Salesforce.com
An ERP system includes core software components, often called modules, that focus on essential business areas such as finance and accounting, HR, production and materials management, customer relationship management (CRM), and supply chain management. Organizations choose which core modules to use based on which are most important to their particular business.
What primarily distinguishes ERP software from stand-alone targeted software — which many vendors and industry analysts refer to as best-of-breed solutions — is a common central database from which the various ERP software modules access information, some of which is shared with the other modules involved in a given business process. This means that companies using ERP are largely saved from having to make double entries to update information because the system shares the data, in turn enabling greater accuracy and collaboration between the organization’s departments.
BI encompasses a wide variety of tools, applications and methodologies that enable organizations to collect data from internal systems and external sources; prepare it for analysis; develop and run queries against that data; and create reports, dashboards and data visualizations to make the analytical results available to corporate decision-makers, as well as operational workers.