1.
Unified Approach to Analytics

2.
Objective
1
Current challenges with
“Data Analytics” at Scale
2
Gaps to be addressed to
meet future needs
3
Understand why we
need:
“Unified Approach to
Analytics”

3.
Problem Context
Challenges of Data Analysis (around growing data)

4.
Past
Data analysis was achieved with Relational
DB+RealTime+Spreadsheets.

5.
Future
Speed of innovation been
more rapid.
01
The goal of business agility
more attainable
02

6.
Gaps/Challenges to meet the Future needs
• Data is increasing in vertical trajectory, is becoming highly distributed, and can come in a variety of formats.
• IDC Estimates ~ 180 ZB (by 2025)
• Capturing and unlocking this growing data for innovation is becoming a big challenge.
Growing data:
• Move to cloud, setting up a big-data infrastructure, maintenance and over reliance on Dev-ops.Infrastructure Complexity:
• Open source projects such as Hive, Presto, Kafka, MapReduce, Impala, and Apache Spark. All these
technologies comes with different release cycles, lack institutional support mechanisms, and have
varying performance deliverables
Disparate Technologies:
• A lack of automation between the various steps of data ingestion, ETL, exploration, modeling and
presentation of data create massive inefficiencies. This greatly reduces the speed of innovation that
is the promise of big data, data science, and a move to the cloud.
Disjointed Analytics Workflows:
• By viewing data through separate lenses, collaboration is very difficult, trust in the analytics can be
difficult resulting in slower speed of innovation.
Team Structure (Data engineers, scientists
and analysts (within Data Organization)):
• Challenges are around building a consolidated data security, legal compliance and financial
liabilities.Protecting the data:

7.
Key Challenges: Desperate Technologies and Growing Data

8.
What is
needed to
succeed?
Comprehensive, unified
approach to analytics.
Hosted Solution / Cloud Service
Easier onboarding: Self-Serve (in minutes).
End to End workflow (streamlined):
• (Personas) Data Engineers: focused on data preparation and
productionizing the models that the data scientists build through a
common, unified framework.
• (Personas) Data Scientists: can leverage the same platform to explore
and visualize data real-time. Building machine learning models is
simple with collaborative notebooks allows machine learning models
at scale. Interactive dashboards enables publishing insights across the
company to business analysts.
Security + Operability Cost
(Unified)
(Secured)

9.
Goal:
• No longer have to wait for an infrastructure team
to provision and configure hardware for them,
Self-serve in minutes, and focused on building
models and finding patterns in data that fuel
innovation and accelerate go to market for
transformative business outcomes.
1
Success Metrics:
• Performing Data Analysis, Building a model and
testing a prototype in hours, versus weeks or
months.
2