Big data analytics is the process of surfacing useful patterns in the huge volumes of structured and unstructured data with which businesses are inundated every day. Businesses can uncover patterns, trends, or information that can help them improve processes in marketing, customer service, and other areas.
Big data analytics relies a variety of data sets that, when integrated, can provide more accurate insights than an analysis of a smaller amounts of data. More data makes it is easier to spot a trend or an outlier, and it can provide managers with an understanding of what customers want and how to improve business operations. Recent estimates predict revenues for big data and business analytics solutions will reach $260 billion in 2022.
Benefits of big data analytics include:
Big data brings with it issues that may not be present with smaller datasets. For instance, organizations that work with big data need a data warehouse to store the volume and variety of data for analytics and business intelligence (BI). They may need other supporting software or technologies, such as data lakes for storing large volumes of raw data. And they need people with specific skills to work with big data infrastructure, software, and technologies. These may include data scientists for building predictive algorithms, data engineers for building and maintaining the storage infrastructure, and business analysts who define key performance indicators and design reports and dashboards.
Businesses in nearly every industry can benefit from big data analytics, but a few industries are ahead of the curve when it comes to improving performance and competitiveness.
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A critical part of any big data analytics process is copying the data from sources that are not optimized for analysis into a destination data warehouse that is.
Raw data comes in three forms:
All of these kinds of data must be extracted from a source application or database, optionally transformed for analytics use, and loaded into a data warehouse via a process called ETL (extract/transform/load).
When the destination is a cloud data warehouse, a variation of this process, ELT, is a better approach because cloud platforms can scale more cost-effectively than on-premises data warehouses. With ELT, processing doesn't happen in the data pipeline; ELT transfers raw data directly to its final destination in the data warehouse, where it can be transformed as needed.
Big data analysis includes the following steps: process, cleanse, and analyze.
A business must identify data sources, then extract the target data for processing, or ingestion. This step is where ETL comes into play. You should choose an ingestion model that’s appropriate for each source by considering the timeliness with which you’ll need analytical access to the data. There are two ways to process data:
You wouldn't want to make business decisions based upon the analysis of poor-quality data, so you may need to do some data cleansing during the ETL process. If you build your own data pipeline, you may choose to incorporate some cleansing operations, such as:
Once an ETL tool has done its job and your data resides in a data warehouse, it's time for analytics to begin. The type of analytics application you use will depend on your needs and use cases, and you may end up using more than one. Three categories of analytics that companies deploy include descriptive, predictive, and prescriptive.
There are dozens of big data analytics platforms, and the ones you choose will depend upon your business goals and use cases. You may use one, or many, in order to discover information or patterns on which you can act. Gartner provides reviews of many of these platforms. In a recent survey, Stitch users mentioned these three tools they used often:
These tools are just a part of a much larger analytics universe that includes tools for most use cases and budgets.
One of the most important requirements for the implementation of big data analytics is the choice of a destination data warehouse optimized for analytics and business intelligence (BI).
Cloud data warehouses, such as Snowflake, Amazon Redshift, Microsoft Azure SQL Data Warehouse, and Google BigQuery, have numerous advantages over on-premises systems, including:
The cloud platform provides the ability to quickly scale to meet just about any processing demands. Administrators can scale processing and storage resources up or down with a few mouse clicks.
The cloud offers infrastructure on a cost-effective subscription-based pay-as-you-go model. Software and security updates are automatic and included in the subscription.
Cloud data warehouses have data security covered with always-on, end-to-end data encryption and built-in protection against loss of data (accidental or malicious), and they adapt to new security threats by deploying countermeasures quickly. Cloud data warehouses also address a variety of compliance standards, such as SOC 1 and SOC 2, PCI DSS Level 1, and HIPAA.
Cloud data warehouses are built for high availability, spanning many availability zones or data centers. If a data center goes out, work shifts to another available data center, and the disruption goes unnoticed by the user.
Learn more about the next generation of ETL
Big data analysis doesn’t have to be overwhelming. When you identify your data sources and prepare the data for the processing, or ingesting, phase, Stitch makes it easy to extract big data from more than 100 sources and replicate it to your target destination for analytics and business intelligence. Sign up for a free trial to get your data to its destination and begin analyzing it in minutes.
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