1. Introducing analytics on Azure

According to a survey by Dresner Advisory Service in 2019, an all-time high of 48% of organizations say business intelligence in the cloud is either critical or very important in conducting their business operations. The Cloud Computing and Business Intelligence Market Study also showed that sales and marketing teams get the most value out of analytics.

As businesses grow, they generate massive amounts of data every day. This data comes from different sources, such as mobile phones, the Internet of Things (IoT) sensors, and various Software as a Service (SaaS) products such as Customer Relationship Management (CRM) systems. Enterprises and businesses need to scale and modernize their data architecture and infrastructure in order to cope with the demand to stay competitive in their respective industries.

Having cloud-scale analytics capabilities is the go-to strategy for achieving this growth. Instead of managing your own data center, harnessing the power of the cloud allows your businesses to be more accessible to your users. With the help of a cloud service provider such as Microsoft Azure, you can accelerate your data analytics practice without the limitations of your IT infrastructure. The game has changed in terms of maintaining IT infrastructures, as data lakes and cloud data warehouses are capable of storing and maintaining massive amounts of data.

Simply gathering data does not add value to your business; you need to derive insights from it and help your business grow using data analytics, or it will just be a data swamp. Azure is more than just a hub for gathering data; it is an invaluable resource for data analytics. Data analytics provides you with the ability to understand your business and customers better. By applying various data science concepts, such as ML, regression analysis, classification algorithms, and time series forecasting, you can test your hypotheses and make data-driven decisions for the future. However, one of the challenges that organizations continuously face is how to derive these analytical modeling capabilities quickly when processing billions of data rows. This is where having a modern data warehouse and data pipeline can help (more on this in the next sections).

There are a number of ways in which data analytics can help your business thrive. In the case of retail, if you understand your customers better, you will have a better idea of what products you should sell, where to sell them, when to sell them, and how to sell them. In the financial sector, data analytics is helping authorities fight crime by detecting fraudulent transactions and providing more informed risk assessments based on historical criminal intelligence.

This chapter will cover fundamental topics on the power of data with:

• Big data analytics
• IoT
• Machine Learning (ML)
• Artificial Intelligence (AI)
• DataOps

You will also learn why Microsoft Azure is the platform of choice for performing analytics on the cloud. Lastly, you will study the fundamental concepts of a modern data warehouse and data pipelines.