Best Practices for Managing Big Data Analytics Programs

Big data has become an integral part of many businesses. Learn about best practices for managing big data analytics programs including data security & management.

Best Practices for Managing Big Data Analytics Programs

Big data has become an integral part of many businesses, and it's no surprise that companies are looking for ways to maximize its potential. According to a recent survey by the Center for Enterprise Applications Research, companies that integrate big data into their processes reported 8% higher revenues and 10% lower costs. It's these types of financial results that drive companies to research big data and explore how they can provide competitive advantage. When it comes to managing a big data analytics program, it's important to start by clearly establishing the business objective.

This means collecting, analyzing and understanding business requirements. Your project should have a business objective, not a technological objective. Additionally, data security, availability, backup and restore, replication, and archiving must be managed on your behalf. Expanding these efforts helps stakeholders get used to the new mindset and gives them first-hand experience with the benefits of analytics. Data management is a fundamental business driver used to ensure that data is acquired, validated, stored and protected in a standardized manner.

Your management processes must be in place to demonstrate that your networks are secure and that your employees understand the critical nature of data privacy. It's often wise to produce multiple levels of documentation that provide complete context for why data exists and how it can be used. Instead of transforming data into a single format, it can be left in its native format and then filtered, transformed, and organized as needed for each new analysis application. Function engineering is one option that employs machine learning tactics to combine an existing data set, domain experience, and intuition into smarter data tuned for analysis. Organizations must also find more intelligent data management approaches that allow them to effectively group and optimize their data. If you don't have this specific, searchable information that enables discovery, you can't trust that you'll be able to use your data years later. When considering these best practices together, it is recommended, if not necessary, that you invest in quality data management software.

In addition, having executive sponsorship and lateral buy-in will allow for stronger data collaboration between teams in your organization. A study by the McKinsey Global Institute estimates that there will be a shortage of 140,000 to 190,000 people with the necessary experience this year, and a shortage of another 1.5 million managers and analysts with the skills to make decisions based on the results of the analysis. An interdisciplinary program that combines engineering, management and design leading to a master's degree in engineering and management or a 12-month program focused on applying the tools of modern data science, optimization, and machine learning to solve real-world business problems are two options available for those looking to gain the necessary skills.