Today, businesses large and small are looking at data in a new way. No longer is it just a collection of information that is stored and used for a few reports.
Due to emerging technologies like the Internet of Things, business analytics, Big Data and data warehousing, more businesses view data as a valuable commodity. The data you collect can be used to provide additional services, understand customers better and make better, informed decisions in the moment they’re most needed.
What can data warehousing and Big Data do to help your business grow?
What Is Data Warehousing and Big Data?
Big Data is a broad term referring to the sheer amounts of data available today and the ability to store, analyze and use that data. A data warehouse is a computer system that can store massive quantities of data, report on that data and use it for better business intelligence.
Both concepts are at the core of digital transformation, which is changing the way work is done. With digital transformation, which includes Big Data, analytics, automation, artificial intelligence and other advances, helps companies improve business processes, transform products, understand customers better and empower employees. In some cases, digital transformation has allowed new players, such as Uber and Airbnb, to appear and dominate industries quickly.
“Every company is becoming a data company, and mining data is helping transform products and make them more relevant in the current market dynamic – marking a key shift from hindsight to foresight,” wrote Bertrand Launay, chief operating officer for Microsoft India.
Is Data Warehousing Necessary?
Today, if your business is not using business analytics tools, it’s a good bet your competitor is. According to IDC, the worldwide market for Big Data and data analytics is expected to reach $260 billion by 2022 with a projected compound annual growth rate from 2017-2022 of 11.9 percent.
The fastest rates of revenue growth are projected for retail, banking and professional services. However, data warehousing and analytics are evident in industries from manufacturing to health care to government.
What Are the Benefits Of Using Data Warehousing?
There are several advantages to investing in a data warehouse for your business, including:
- Better business intelligence for all facets of your business, including market segmentation, sales, marketing, inventory and finance
- Less time integrating and consolidating data coming from multiple sources
- Better data quality and consistency
- Faster insights leading to more informed decisions in the moment
- Improved market forecasting
- Streamlined information flow
For cloud-based data warehouses, there are more benefits, including:
- Rapid scalability as your data volume, variety or collection speed grows
- On-demand storage adjustments
- Fast access from any connected device
Who Are the Major Players in Data Warehousing?
There are several leaders in the data analytics space. According to the Gartner 2019 Magic Quadrant for Data Management Solutions for Analytics, the leaders in this space include some of the biggest names in tech — Microsoft, Amazon, Oracle, and SAP. Here’s a look at each:
- Microsoft. The Azure data warehousing product benefits from high availability rates, global data redundancy, speed in deployment, operation and scalability, advanced data visualization and integration with other Microsoft products.
- Amazon. Amazon describes its product as “data lakes.” Loading your data to the cloud-based lakes in real time lets you use many analysis tools, including Redshift, a data-warehousing tool that lets you run complex queries against structured and unstructured data.
- SAP. SAP HANA uses machine learning, artificial intelligence and advanced analytics to drive its multimodal data management platform. Its features include text mining and analytics, series data processing and trend forecasting, and streaming data processing analytics.
- Oracle. The company offers several data warehousing products and touts their ability to build more sophisticated queries, manage and maintain data for optimal performance, and build complex data models.
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