top 5 trends disrupting big data discovery

TOP 5 TRENDS THAT ARE DISRUPTING BIG DATA DISCOVERY

The New Approach to Access, Analyze, and Derive Big Data Insights with Speed.

Citizen Data Scientists Rise Up!

Citizen data scientists are becoming critical assets to an organization. They are helping businesses find key big data insights that help them outperform their peers.

Also known as business users with a passion for data, citizen data scientists derive big data

insights without relying on data scientists for data preparation help.

So businesses must empower citizen data scientists to be productive contributors

in their companies.

Gartner predicts that citizen data scientists will grow 5X faster than their highly trained data scientist counterparts between now and 2017.

Understanding Behavior Is the Killer App

Today, businesses analyze billions of visitor segments and device patterns to get deeper insights about customer behavior. They are looking at broader data patterns across people, web visitors, devices, etc.

Creating a customer-first experience is mission-critical for businesses

It starts with understanding behaviors through robust customer segmentation:

• Attribution

• Cohort behavior

• Conversion paths

Companies increasingly want to leverage IoT insights, but are limited by current technology restrictions. The challenge is that most manufacturers hoard their IoT data in silos.

Minding the Gaps in IoT

Companies lack a holistic view of aggregated data sets, which limits their insights.

So businesses must remove the analytics gap by aggregating siloed data from IoT devices into a modern

data lake architecture.

Fact: Spark is getting broader adoption.

Apache Spark Gets Real

Spark testing and early-stage

deployments

Input from the Spark community

Spark needs to visibly deliver on its promise

• Data transformation

• Machine learning

• Streaming analytics

The Spark open source community must roll up its sleeves and address Spark’s rough edges – especially in performance and reliability to continue its adoption.

For Spark to succeed, it must prove its value in:

Self-service, simplified data prep technology is making the data discovery cycle faster and easier for more users.

Data Prep Becomes a Feature of Data Discovery

Your modern data-prep workflow should have the following features:

• Easy-to-browse data catalog

• Notions of lineage tied to data

Data prep is now much easier. Modern, self-service products are lowering the bar – especially as these products are increasingly guided by aspects of machine learning.

CAPITALIZE ON THESE OPPORTUNITIES…

Dive into these top 5 trends to better access, analyze, and derive Big Data insights with speed.

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