What is big data?
Big data is data that is either too large or too complex for traditional data-processing methods to handle. In general big data has come to be known for its "three Vs": volume, variety, velocity. Volume refers to the extreme size, variety refers to the wide range of nonstandard formats, and velocity refers to the need to process quickly and efficiently.
Why does big data matter?
Data is valuable, but only if it can be protected, processed, understood, and acted upon. The goal of harnessing big data is to offer real-time information that you can use to improve your business. Real-time information processing is one of the major goals for companies attempting to deliver value to their customers in a consistent and seamless manner and is one of the crucial features of edge computing. Insights from big data could allow you to cut costs, operate more efficiently, and discover new ways to boost profits and reach new customers.
Big data analytics and IT optimization
Big data analytics is the term for the process of taking all of your raw and dark data and making it into something you can understand and use. Dark data is data that organizations collect during normal business activities that they must store and secure for compliance purposes. Dark data is often overlooked but, like the rest of your data, can yield valuable insights that you can use to improve your business.
Big data insights can help you prevent costly problems instead of reacting to them. Analyzing data patterns can help you predict customer behaviors and needs instead of guessing (which can also help you increase revenue).
To be effective, analytics software needs to run on a flexible, comprehensive, and reliable foundation. That’s why IT optimization is key. You need to make sure you can continue to gather, analyze, and use your data as your technology stack changes.
Data lakes, data swamps, and big data storage
A data lake is a repository that stores near-exact or exact copies of your data in a single location. Data lakes are becoming more common in enterprises who want a holistic, large repository to manage their data. They are also less expensive than databases.
Data lakes let you keep an unrefined view of your data so that your top analysts can explore their refinement and analysis techniques outside of traditional data storage (like a data warehouse) and independent of any of the system-of-record (a name for the authoritative data source for a given element of data). If you want your most highly skilled analysts to continue honing their skills and exploring new ways of analyzing the data, you need a data lake.
Data lakes require continual maintenance and a plan for how you will access and use the data. Without this upkeep, you risk letting your data become junk—inaccessible, unwieldy, expensive, and useless. Data lakes that become inaccessible for their users are referred to as "data swamps."
Large organizations have several business units (BUs) each with their own unique data needs. Each of these BUs has to compete in some way to get access to the data and infrastructure in order to analyze it—it’s a problem of resources. Data lakes don’t solve this problem. What you need, instead, is multi-tenant workload isolation with a shared data context. What does that mean?
Basically, instead of making a full copy of your data every single time a new business unit needs access (complete with the admin work of writing scripts to copy the data and make it all work), this solution enables your organization to pair down to just a handful of copies that can be shared across BUs through containerizing or virtualizing the data analytics tools.
The IT challenges of big data integration
Big data is an agile integration challenge. How do you share data amongst multiple business units while maintaining strict service level agreements? How do you get more value out of the data you have?
Mining big data is rewarding but complex. Data scientists are tasked with analyzing the data for insights and recommendations to take to the business. Data engineers need to identify, assemble, and manage the right tools into a data pipeline to best enable the data scientists. Finally, on the infrastructure side, the admin folks have to work deep in the infrastructure to provide the basic services that will be consumed. Looming all along the way are the challenges of integration, storage capacity, and shrinking IT budgets.
As you look for an integration solution, ask:
Are your data sources reliable? Do you have one version of the truth?
Do you have adequate storage capacity? Does your hardware-based storage segregate data, making it hard to find, access, and manage?
Can your architecture adapt to constantly evolving data technology?
Are you taking advantage of the cloud?
Is your data protected? What security plan do you have in place for big data?