The IDC DataSphere forecast report predicts that global data creation and replication will have a 23% compound annual growth rate between 2020 and 2025. Another study by Statista suggests that global data creation will grow to over 180 zettabytes during that same period.
Cheaper data storage and advanced analytics technologies are fueling the current data explosion. However, collecting that data in one place where you can analyze it remains a complex task. Fortunately, organizations can use a big data warehouse to collect, organize, and heavily analyze data on demand instead.
What Role Does Big Data Warehouse Play?
Big data warehousing enables businesses to consolidate large amounts of data from multiple sources and optimize it for analysis. This can help improve business efficiency, make better decisions, and discover competitive advantages. They also store a lot of historical data and can handle very fast, complicated queries using online analytical processing (OLAP). Let’s take a look at some use cases and benefits of data warehouses with respect to big data and explore situations where it could improve your business.
Key Use Cases Of Big Data Warehouse
The following are examples of how a big data warehouse can be utilized for various purposes:
Customer Acquisition and Retention
Customers leave digital footprints that detail their preferences, needs, purchase behaviour, etc. Businesses use big data to observe these consumer patterns and then adjust their products and services according to specific customer needs. By doing this consistently businesses ensure customer satisfaction, and loyalty, which leads to a considerable increase in sales.
Amazon has utilized its big data to create a personalized shopping experience for users, making suggestions based on previous purchases, what other customers have bought, browsing patterns, and more.
Potential Risks Identification
Business environments are high-risk, so businesses need risk management solutions to solve problems. Big data is important in developing effective risk management processes and strategies. Big data analytics and tools can quickly reduce risks by optimizing complex decisions for unforeseen events and possible threats.
IoT Data Integration
IoT devices, like smartwatches, kitchen appliances, and security systems generate immense data sets that can be analyzed to improve various processes. This data has to first be collected and stored in a relational format so it can support real-time or historical analysis. After that, queries are run against millions of events/devices to uncover anomalies in real-time or predict future trends based on past data sets.
IoT data analysis is also a quite complex and time-consuming process, but it can be made easier with the right platform. A high-performance platform that is easy to access and flexible enough to respond quickly to changing conditions is essential. This data can be summarized and filtered into fact tables with a data warehouse to create time-trended reports and other metrics.
If the data integration by IoT seems interesting, you might also be intrigued to check out big data integration and the benefits and challenges it poses.
Analyzing Large Stream Data
Large data streaming is a method to process sizable quantities of real-time data, deriving enlightening trends and predictions. A nonstop stream of unstructured data gets analyzed before it’s stored; if not processed at the moment, the value of the information decreases.
This processing occurs rapidly across several servers simultaneously in real-time; once streamed, the data cannot be reanalyzed.
Large data sets are constantly being generated by numerous sources. These data can range widely, from a mobile device or web application log files to in-game player activity and e-commerce purchases. The processed data is then used for various analytical purposes, such as aggregations, filtering, correlations, and sampling. Businesses gain keen insights into customer behaviour and activity by analyzing large stream data, which can reveal things like service usage, website clicks, device geolocation, and server activity. Data warehouses take this a step further by organizing the information to display overall statistics.
Why Choose Intone?
When an organization captures the full benefits of data, they’re able to change with the market and customer demands. In this process, no data can be ignored, which is why gates need to be open from every source that data is getting generated within an organization through its useful now or future because no one knows what information can be collected by connecting these data sources. To make this happen you need one integration tool that can connect to almost any tool in the market and process data with no latency. IntoneSwift is one such tool that will take care of collecting and storing your data while you are busy concentrating on its analysis of it. This is where data warehousing comes in handy; it offers businesses a way to store data centrally and consistently, making it simpler for business users to access.
At Intone, we prioritize our clients and work to give them the best service with the data analysis service assistance they need through IntoneSwift. We offer
- Knowledge graph for all data integrations done.
- 600+ Data, and Application and device connectors that can read, transform and load data which is in the form of structured, semi-structured, and complex structured.
- A graphical no-code low-code platform.
- Distributed In-memory operations that give 10X speed in data operations.
- Attribute level lineage capturing at every data integration map.
- Data encryption at every stage.
- Centralized password and connection management.
- Real-time, streaming & batch processing of data.
- Supports unlimited heterogeneous data source combinations.
- Eye-catching monitoring module that gives real-time updates.
Contact us to know more about how we can help you!