Data-driven enterprises need to tap into these 5 trends that are paving new way to access, analyze, and derive Big Data insights with speed.

1. TOP 5 TRENDS
THAT ARE DISRUPTING
BIG DATA DISCOVERY
The New Approach to Access,
Analyze, and Derive Big Data Insights
with Speed.

2. 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.

3. 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.

4. 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.

5. Creating a customer-first experience is mission-critical
for businesses
It starts with understanding behaviors
through robust customer segmentation:
• Attribution
• Cohort behavior
• Conversion paths

6. 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

7. 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.

8. 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

9. • 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:

10. 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

11. 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.

12. 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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