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: benefits
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:
- Improved decision-making: Business leaders can make informed decisions or assess problems or failures in real-time.
- Enhanced customer satisfaction: Businesses can provide a personalized customer experience, enhance customer interactions, or generate offers based upon customers’ buying habits.
- Reduced costs: A business can use big data analytics to discover process improvements that save the company time and money.
Big data: considerations
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.
What industries benefit from big data analytics?
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.
- Banking: Banks and other financial institutions manage and analyze big data to help them profile and segment their customer base, identify customer spending patterns, cross-sell products, and prevent and manage fraud.
- Health care: Health care organizations use big data analytics to obtain a 360-degree view of patients and doctors, to help providers create personalized health plans for individual patients, and optimize hospital growth by improving effectiveness and personalization.
- Education: Educational institutions use big data analytics to determine exactly where, when, and how enrollments are changing, or predict which students are likely to succeed at an institution and which may be more likely to drop out or fail.
- Manufacturing: With the insights from big data analytics, manufacturers can minimize waste, improve quality, and increase output.
- Retail: Big data analytics allows businesses to know more about their customers, provide what they want, and reach them in the right marketing venues. These insights can help inform strategies to promote repeat and return business.
Moving big data with ETL/ELT
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:
- Structured data is quantitative data that resides in a fixed field within a record or file. A spreadsheet is an example of structured data. Structured data is easy for big data programs to use and analyze.
- Semistructured data doesn’t reside in a relational database, but it has some organizational properties that makes it easier to analyze, such as semantic tags. HTML code, JSON documents, and XML are examples of semistructured data.
- Unstructured data includes text, dates, numbers, and facts that appear in text messages, videos, social media posts, email, photographs, and more. It is not organized in a predefined manner. It’s more challenging for big data analytics programs to use and analyze.
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.
How to begin big data analysis
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:
- Stream processing: Data is sourced, manipulated, and loaded as soon as it’s created or recognized by the data ingestion layer. Stream processing can be expensive because it requires systems to constantly monitor sources and accept new information, which means you may have to pay for more processing power to maintain performance. However, it may be appropriate for analytics that require continually refreshed data.
- Batch processing: Here, the ingestion layer periodically collects and groups source data and sends it to the destination system. Businesses use batch processing when having near-real-time data is not important, because it’s easier and less costly than stream processing.
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:
- Standardizing the data: Often, the way data is structured in a source data store makes it difficult or non-optimal to load into another data store.
- Data typing: You may need to type the data entering your system and maintain that type (currency, date, etc.) as it travels through your ETL process.
- Deduplicating: You may need to deduplicate or eliminate irrelevant data.
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.
- Descriptive analytics takes data and turns it into something business managers can visualize, understand, and interpret. It provides intelligence into historical performance and answers questions about what happened. Examples include customer, operations, and sales reports.
- Predictive analytics tools provide insights about likely future outcomes — forecasts, based on descriptive data but with added predictions using data science and often algorithms that make use of multiple data sets. Examples include sales forecasting, consumer credit scores, and retailers’ suggestions for what you may want to read, view, or purchase next.
- Prescriptive analytics takes predictive analytics a step further by offering advice about what actions to take. It examines possible outcomes that result from different possible actions and suggests which actions will have optimal outcomes.
Big data analytics: platforms
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:
- Chartio is a web-based dashboarding tool with options for both drag-and-drop and SQL querying functionality. It’s suitable for advanced users with SQL expertise, and for semitechnical users for fast, one-time, and ad-hoc analyses. Chartio is appropriate for organizations seeking to empower data analysts with BI functionality.
- Looker is a data exploration tool with a web-based interface designed to be intuitive for users. It features a data modeling language called LookML that outsources complex SQL programming to the tool’s engine. Looker is ideal for data discovery use cases where technical and semitechnical users must build reports quickly.
- Periscope Data is an SQL-first BI tool, with optional drag-and-drop functionality and support for detailed analysis in Python and R programming languages. Periscope is suitable for technical teams and those who need a data visualization platform. It stands out with a particularly sophisticated data governance module.
These tools are just a part of a much larger analytics universe that includes tools for most use cases and budgets.
Big data analytics and cloud data warehouses
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:
Speed and scalability
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.
Stitch — the best way to get big data to its destination
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.