How can companies secure Big Data for their analytical workloads?

An insightful whitepaper illustrates how companies can avoid a key security gap in their data architecture

How can companies secure Big Data for their analytical workloads?

In today’s technological climate, there is certainly no shortage of enterprise data. However, a whitepaper from Big Data analytics leader Arcadia Data highlights the absolute importance of securing this valuable data.

Outdated legacy systems

Companies are constantly seeking a balance between data agility with self-service analytics, while reducing the risk of unauthorised data access. As the paper states, enterprises need “the right approach to gain critical business insights without overexposing data.”

Companies cannot typically transplant traditional or legacy best practices to Big Data. However, Arcadia Data notes that “the right best practices and the right technologies will help” to deploy a simplified infrastructure.

Traditionally, developing analytical applications with visualisations and dashboards is labour-intensive. Companies must define the use case, define the required data, extract the data, build and deliver the application to end users, and finally refine the use case.

As a result, the overall process is laborious and is even slower if Big Data is in the picture. “With the many data sources you process, you end up with a complex environment that becomes harder to manage”, which in turn “inhibits the agility of self-service analytics that many organisations need today.”

What is the solution?

“Big Data security is not only about protecting your data”, it is also extracting value from Big Data in a secure manner. Rather than investing in an outdated legacy system, however, companies should consider adopting visualisation of Hadoop data.

“It provides clear, easy-to-use navigation through the analysis, so that users can iterate through insights more quickly”, as the paper outlines. It also applies learning from each successive interaction for future analysis.

In addition to this, the solution also provides greater insights from granular data. As a result, this offers a “level of detail and specificity beyond data summaries and aggregates.”

Tools such as Apache Sentry and Apache Ranger are useful when attempting to simplify data security with a data platform. However, these tools “need to be leveraged across the entire data analytics architecture consistently to manage end user access.”

Arcadia Enterprise is therefore the ideal analytics platform for Big Data. It seamlessly integrates with the administration consoles from various vendors, while providing business analysts and data scientists with the “ability to easily modify, view, and analyse data.”

How can companies build customer intimacy using Big Data? Listen to our podcast with Principle of OmniChannel Danny Flamberg to find out