Data Integration, Migration & Analytics
For most of our clients, data exists in multiple places, in multiple apps, on multiple servers. Our services includes data integration and migration, which is a must if you want to truly see a 360-degree view of your business.
Data integration involves combining data from multiple sources and providing users with a unified view of it. For example, clients often run ERP solutions such as SAP or Oracle. TechAir is often tasked with integrating crucial ERP data into Microsoft Dynamics 365 for a better view of a customer, opportunity or project.
- Integrate ERP (i.e. SAP/ORACLE/NETSUITE) with Dynamics 365
- One-way or two-way integrations– We can make sure data flows one-way from ERP to CRM or that data flows two-ways between ERP to CRM.
- Integrate any API with Dynamics 365
- Integrate other databases using integration tools like Scribe, KingswaySoft or Azure (i.e. Access Database and Dynamics 365)
Data migration is the process of migrating data away from one system to another. Think your data is locked away on an old platform? Think again. We’re able to migrate your data away from one application to Dynamics 365, we just need the green light.
- Migrate and clean data
- Match data types
- Incorporate data migration best practices
- Migrate data from spreadsheets, applications, etc.
Our Integration Tools:
Insights and Analytics
Let’s set the scene. Your data is in place- it’s cleaned, organized (for the most part) and up-to-date. Now what? We need to harness this data into meaningful metrics. By meaningful, we mean insights that support informed decision making. This means spotting trends as they happen, quickly act on data and gaining deeper insights.
These metrics can be visualized in a variety of ways, depending on your needs:
- Dynamics 365 Dashboards and Reports
- Power BI, Microsoft’s Business Intelligence Tool
- Azure Data Lake
- Azure SQL Database
Artificial Intelligence + Machine Learning
We’ve heard the buzz words: AI, Machine Learning, Bots…but what do these words really mean?
Let’s start with Artificial Intelligence (AI). In simple terms, AI is a concept. It makes it possible for machines to perform human-like tasks by learning from experience. To allow the system to learn, you have to train it. After training, a system can demonstrate behaviors like planning, learning, reasoning, and problem solving, – all activities that would require intelligence had a human carried out those tasks.
Machine learning is really a branch of Artificial Intelligence. It’s based on the idea that systems can learn from data, identify patterns and make decisions- all with minimal (or no) human involvement. In other words, it’s the process of training a machine how to learn. An example of machine learning is training an application to identify profitable opportunities, based on client purchase history, profit margin, and relationship scores. ML allows you to train your machine based on a sample set of data. For example, you can train machine by giving it a sample set of cancerous cell images so that next time, it can identify those cancerous cells in a patient image.
Here are some examples of AI & Machine Learning in the real world:
- Dramatically reduce paperwork
- AI can significantly reduce admin tasks. There has been research that indicates that documenting and recording information takes 500 million hours per year, at the federal level.
- With AI, we can introduce “bots” that can automate activities such as invoice processing, proposal creation, form creation, auto-email sends, etc.
- Make sense of the data
- Dramatically reduce paperwork