18 January, 2017
Mastering Data In The Age Of Big Data And Cloud
Everyone’s been talking about Big Data for years now and how data can be used for better decision-making and advanced analytics, but so few companies have actually mastered their data in a way that 1) makes all data easily accessible and 2) increases agility in making decisions with data.
The concept of a data lake is becoming standard for many organizations as they try to create more value out of their data assets. This trend toward a data-driven business approach targets a growing variety of data, and it’s more critical than ever to include all data assets as part of the next-generation data architectures. To truly “master” all of a company’s data in an environment where they can react quickly to business needs, companies must first liberate all enterprise data, especially hard-to-access data from legacy platforms like the mainframe which houses the most critical data assets such as customer data. Organizations must then integrate these core data assets with the emerging new data sets from the Internet of Things (IoT) and cloud. Below are a number of tips companies should consider when embarking on a project to master their data:
– Ensure easy and secure access to all data assets and visibility to data lineage as the data is populated in the data lake.
– Choose a product-based approach to custom coding for repeatability, simplicity, and productivity.
– Make sure the selected tools and products can adapt to a rapidly changing technology stack, and can integrate and keep up with the speed of the Big Data stack, for example as Apache Hadoop and Spark evolves.
– Ensure the tools and products can interoperate and leverage open source frameworks for advanced analytics.
– Create a streamlined, standardized, and secure process to collect, transform, and distribute data.
– Consider compliance and security needs and requirements from day one and not after the fact, especially those in highly regulated industries and those that house sensitive customer information on the mainframe.
– Ensure the selected tools can interoperate with both existing data platforms and next-gen data architecture to allow organizations to adapt at their own pace, not creating new data silos.
– Make data quality a priority. Mastering your data requires ability to explore data assets, create business rules and use these rules to validate, match, and cleanse the data. Automation of these steps and the ability to validate and cleanse as you populate the data lake becomes critical as organizations become more data centric.