For businesses and organizations of all kinds, transforming data into actionable intelligence can mean the difference between struggling and thriving. Maximizing the value of information requires data analytics: the process by which raw data is analyzed to reach conclusions.
While almost every organization analyzes some data, modern analytics enables an unprecedented level of understanding and insight. How far has your company gone toward a data-led, analytics-driven culture—and what’s the next step?
It all starts with the data pipeline.
Understanding the Data Pipeline
Establishing a well-developed data analytics approach is an evolutionary process requiring time and commitment. For organizations that want to take the next step, it’s critical to understand the data pipeline and the life cycle of data going through that pipeline.
- Ingest: Data Collection
The first stage of the data pipeline is ingestion. During this stage, data is collected from sources and moved into a system where it can be stored. Data may be collected as a continuous stream or as a series of discrete events.
For most unstructured data—IDC estimates 80 to 90 percent1—ingestion is both the beginning and the end of the data life cycle. This information, called “dark data,” is ingested but never analyzed or used to impact the rest of the organization.
Today, one of the biggest advanced data analytics trends starts right at the ingestion stage. In these cases, real-time analytics of streaming data happens alongside the ingestion process. This is known as edge analytics, and it requires high compute performance with low power consumption. Edge analytics often involves IoT devices and sensors gathering information from devices, including factory machines, city streetlights, agricultural equipment, or other connected things.
- Prepare: Data Processing
The next stage of the data pipeline prepares the data for use and stores information in a system accessible by users and applications. To maximize data quality, it must be cleaned and transformed into information that can be easily accessed and queried.
Typically, information is prepared and stored in a database. Different types of databases are used to understand and analyze data in different formats and for different purposes. SQL* relational database management systems, like SAP HANA* or Oracle DB*, typically handle structured data sets. This may include financial information, credential verification, or order tracking. Unstructured data workloads and real-time analytics are more likely to use NoSQL* databases like Cassandra and HBase.
Optimizing this stage of the data pipeline requires compute and memory performance, as well as data management, for faster queries. It also calls for scalability to accommodate high volumes of data. Data can be stored and tiered according to urgency and usefulness, so that the most-critical data can be accessed with the highest speed.
Intel® technologies power some of today’s most storage- and memory-intensive database use cases. With Intel® Optane™ Solid State Drives, Alibaba Cloud* was able to provide 100 TB of storage capacity for each POLARDB instance.
- Analyze: Data Modeling
In the next stage of the data pipeline, stored data is analyzed, and modeling algorithms are created. Data may be analyzed by an end-to-end analytics platform like SAP, Oracle, or SAS—or processed at scale by tools like Apache Spark*.
Accelerating and reducing costs for this phase of the data pipeline is critical for a competitive advantage. Libraries and toolkits can cut development time and cost. Meanwhile, hardware and software optimizations can help keep server and data center costs down while improving response time.
Technologies like in-memory analytics can enhance data analytics capabilities and make analytics investments more cost-effective. With Intel, chemical company Evonik achieved 17x faster restarts for SAP HANA *data tables.2
- Act: Decision-Making
After data has been ingested, prepared, and analyzed, it’s ready to be acted upon. Data visualization and reporting help communicate the results of analytics.
Traditionally, interpretation by data scientists or analysts has been required to transform these results into business intelligence that can be more broadly acted on. However, businesses have begun using AI to automate actions—like sending a maintenance crew or changing a room’s temperature—based on analytics.
For a more in-depth resource about the data pipeline and how organizations can evolve their analytics capabilities, read our e-book From Data to Insights: Maximizing Your Data Pipeline.
How far has your company gone toward a data-led, analytics-driven culture—and what’s the next step?
The Four Types of Data Analytics
Data analytics can be divided into four basic types: descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics. These are steps toward analytics maturity, with each step shortening the distance between the “analyze” and “act” phases of the data pipeline.
- Descriptive Analytics
Descriptive analytics is used to summarize and visualize historical data. In other words, it tells organizations what has already happened.
The simplest type of analysis, descriptive analytics can be as basic as a chart analyzing last year’s sales figures. Every analytics effort depends on a firm foundation of descriptive analytics. Many businesses still rely primarily on this form of analytics, which includes dashboards, data visualizations, and reporting tools.
- Diagnostic Analytics
As analytics efforts mature, organizations start asking tougher questions of their historical data. Diagnostic analytics examines not just what happened, but why it happened. To perform diagnostic analytics, analysts need to be able to make detailed queries to identify trends and causation.
Using diagnostic analytics, new relationships between variables may be discovered: for a sports apparel company, rising sales figures in the Midwest may correlate with sunny weather. Diagnostic analytics matches data to patterns and works to explain anomalous or outlier data.
- Predictive Analytics
While the first two types of analytics examined historical data, both predictive analytics and prescriptive analytics look to the future. Predictive analytics creates a forecast of likely outcomes based on identified trends and statistical models derived from historical data.
Building a predictive analytics strategy requires model building and validation to create optimized simulations, so that business decision‒makers can achieve the best outcomes. Machine learning is commonly employed for predictive analytics, training models on highly scaled data sets to generate more- intelligent predictions.
- Prescriptive Analytics
Another advanced type of analytics is prescriptive analytics. With prescriptive analytics, which recommends the best solution based on predictive analytics, the evolution toward true data-driven decision-making is complete.
Prescriptive analytics relies heavily on machine learning analytics and neural networks. These workloads run on high performance compute and memory. This type of analytics requires a firm foundation based on the other three types of analytics and can be executed only by companies with a highly evolved analytics strategy that are willing to commit significant resources to the effort.
Data Analytics Use Cases
Intel® technology is changing the way modern enterprise organizations do analytics. With use cases that span many industries—and the globe—Intel works to continuously drive analytics forward while helping businesses optimize for performance and cost-effectiveness.
For automakers, quality control saves money—and lives. At Audi’s automated factory, analysts used sampling to ensure weld quality. Using predictive analytics at the edge, built on Intel’s Industrial Edge Insights Software, the manufacturer can automatically check every weld, on every car, and predict weld problems based on sensor readings when the weld was made.
Training AI to read chest X-rays can help patients and providers get a diagnosis faster. Using Intel® Xeon® Scalable processors to power a neural network, research organization SURF reduced training time from one month to six hours while improving accuracy.
Smartphones and mobile internet have created unprecedented amounts of mobile data. To enhance customer experiences, telecommunications company Bharati Airtel deployed advanced network analytics using Intel® Xeon® processors and Intel® SSDs to detect and correct network problems faster.
Intel® Technologies for Analytics
With a broad ecosystem of technologies and partners to help businesses create the solutions of tomorrow, Intel powers advanced analytics for enterprises worldwide. From the data center to the edge, Intel works at every point in the analytics ecosystem to deliver maximum value and performance.
- Intel® Xeon® Scalable processors make it possible to analyze massive amounts of data at fast speeds, whether at the edge, in the data center, or in the cloud.
- Intel® Optane™ technology represents a revolutionary approach to memory and storage that helps overcome bottlenecks in how data is moved and stored.
- Intel® FPGAs provide acceleration within the data center to improve response times.
- Intel® Select Solutions are verified for optimal performance, eliminating guesswork and accelerating solution deployment.