You may think that the road to optimized analytics and design only needs data, but it’s a bit more complex than that. Data certainly is the core of data visualization and analytics in many applications, but you need efficient data ingestion in order to extract value. Data ingestion refers to the way incoming data is properly consumed, validated, and made visible. Most businesses will need a validation process to ensure that all their big data sets are clean and reliable. In the age of the IoT and infinite digital data, the need for proper data ingestion is growing.
What is data ingestion?
When large volumes of data make their way into a system, it needs to be identified and moved to its appropriate destination. How is it identified and how does it get to its destination? On the surface, it can be described as the flow of data through specially developed infrastructure. Think about it this way. Imagine waves of people continuously entering a city. In order to run the city, the right people must be qualified and placed into a system that will organize them. Incoming people will be quickly and accurately judged to either wait in designated areas or work within specified roles. Once assigned, people are transported where they need to be and fulfill specific jobs. People who already lived within the city occupy large neighborhoods and can be called for work at any time.
Data ingestion is the process and system that handles endless data streams before it’s even visible to you. The system refines raw data so you don’t have to, making it easier to access and analyze data sets.
Any data you have and will have has to come from somewhere. Geographic qualifiers can certainly be a metric, as can SKU’s, prices, time stamps, and anything else that describes the data. Does it come from customers and products? Or an always-changing stream of weather patterns and flight paths? Or a social media platform with millions of users at any given time? Whatever the case, you’ll have to identify all variations and destinations before you build a system.
Twitter has 25,000+ queries every minute and stores 1.5 petabytes (1 petabyte is a million gigabytes) of data. That’s more live and historic data than any human team can handle with just a few tools. You need the expertise of developers to help build a system capable of building the process. It won’t be easy, but it’s more difficult to go without it.
Without proper data validation or storage, you’ll always waste time and resources to manually accommodate data. That’s every time there’s an influx of new data, increase in historic data, and changes to the product, you’ll regularly spend money and manpower. Additionally, there’s a bigger risk of having unrefined or misplaced data when extracting data sets for analysis.
Improper data ingestion will hinder product performance, success, ROI, finances, and more because the data won’t be clean.
What does successful data ingestion look like? You’ll be able to make decisions based on the data you’ve already collected and live incoming data, knowing it’s been properly validated. A well-organized data ingestion system will maintain a true model by building accurate and refined data sets that are helpful to the product while enabling future data acquisition.