Flowing Gold: Harnessing Streaming Data

While maximizing all business assets is the goal of most business owners, many don’t have the in-house resources needed to capture and manage all the assets that their organization generates.

 
Streaming data is one such asset. It is constantly feeding corporate databanks with up-to-the-minute information, yet because its flow is transitory, the intelligence it offers isn’t available for use until after it’s been captured, cleaned, and integrated into standard data analysis programs. Streaming analytics tools are emerging to respond to this gap in business intelligence.

Understand the Status of your Data

The immense variety of data types, formats, and styles sometimes makes it difficult for ‘non-techie’ people to understand why the source of corporate information is as essential to business success as its content. Without that understanding, business leaders end up basing their decisions on obsolete data. The concern is significant and widespread:

• ‘Bad data’ – material that is wrong, partially wrong, or obsolete – costs American businesses more than $600 billion each year.
• Almost half (40%) of business initiatives fail because they rely on poor-quality data.
• Companies can lose up to 20% of their labor productivity by not ensuring that their data is as current and accurate as possible.

No business can grow to be successful if it’s dealing with any one of these situations. Clearly, those companies that don’t invest in technologies to manage and maximize the value of their data make mistakes that can potentially put them out of business.

Static vs. Streaming Data

One of the reasons so many companies lose track of their data is because they don’t focus on (or invest in) the technologies that manage it. All data that enters a corporate database starts as transient data – ‘data in motion.’ In the ‘traditional’ data development process, corporate supply chains contribute supplies, production lines produce products, and shopping and shipping happen as companies provide their wares to their customers. Each individual event and transaction between the development and production processes generate data that is captured and transmitted into home office data warehouses.

Traditional ‘extract, transform, and load’ processes (ETL) convert the data from its original format into one that is readable by traditional computer analytics programming. It’s only after these ETL procedures are done that it becomes possible to explore the intelligence contained within the data. Data typically held in corporate data warehouses is ‘static data,’ or ‘data at rest.’

However, the volume of ‘streaming’ data is growing every day, and the messages it carries are becoming more critical to company success. Streaming data’s impact on business success is more apparent as the number of devices sending it multiplies:

• The foundational success of the financial industry depends on its ability to read streaming data flowing from markets, consumer activities, and even changing industrial rules and regulations.
• Healthcare companies generate critical, immediately relevant information every moment, as doctors and patients interact, tests are run and results are shared, and treatment plans are created.
• The retail industry relies on information generated by its production-through-sale course of business, as inventory volumes change, sales occur, and products are transported across the street or across the globe.

 
The data generated by all these processes contains vital corporate intelligence; the capacity to access and apply that intelligence as it arrives is becoming the next differentiator between those companies at the top of their market and those that aren’t.

Streaming vs. In-Transit Data

The difference between ‘data-in-transit’ and ‘streaming data’ is their constancy. Both labels – ‘in-transit’ and ‘streaming’ – signify data on the move. However, data-in-transit is moving from one place to another, usually from a machine, program, or device to a data warehouse where programming integrates it for use in systems. ‘Streaming’ data refers to a continuous flow of information that has no beginning or end and is constantly reflecting and reporting on current activities.

The data documenting the number of spring dresses stored in a particular warehouse would be ‘in-transit’ as it moves from the store’s computer at the end of the business day into the head office data warehouse. The number of sales that are reducing that inventory volume would be ‘streaming’ data because they record and transmit a report of each sale transaction as it occurs.

More companies are seeking to maximize the value of their streaming data as they work to improve relations with their customers, reduce their costs, and find innovations that generate more revenue opportunities. A full 90% of business owners reported their intention to invest in ‘real-time’ data analysis technologies so they can make better decisions based on the data capturing critical business events as they occur.

Harnessing the ‘Always On’ Stream

Managing and maximizing the value of continuous ingestion pipelines is a complex process made more difficult because the flow of information never stops. Amazon’s AWS cloud services master this complexity by utilizing AWS Glue and Apache Spark to continuously consume data generated by streaming platforms Amazon Kinesis Data Streams and Apache Kafka. This architecture facilitates Glue’s capacity to provision, manage, and scale the infrastructure required for ingesting data from both data lakes or warehouses and data from streaming services. The AWS cloud offers both Elasticsearch and DynamoDB for streaming storage, both of which utilize cutting-edge ETL processes to ensure all data values are available and usable, regardless of their streaming status.

Spark’s Structured Streaming program provides the foundation for the streaming data ingestion/transformation/loading service. The Structured Streaming engine begins running your queries as you enter them, then automatically updates its results as new information arrives. The process is fast, fault-tolerant, and scalable. The fully processed streaming data that users can access as it comes is available for immediate use. Decision-makers can ensure their actions are always relevant, timely, and driven by accurate corporate information.

Datavail’s data management experts help customers and clients identify, structure, and utilize all their data, regardless of its source. Contact them today to harness all of your organization’s information, including its streaming sources.

To learn how we helped a client by developing an innovative process to convert its large unstructured data lakes into usable, analyzable information, download our case study, “Finding Gold: Accessing Your Unstructured Data.”