[INFOGRAPHIC] Event-driven Business: How to Handle the Flow of Event Data

Data-driven strategies are
increasingly more important.
Recent EMA research shows a 56%
increase since 2016 in the number of
organizations that identify data-driven
strategies as vital to their business.
Increasing the need for speed. 2018 EMA
research shows that CIOs and other technology
leaders have seen the requirement for true real-time
processing use cases more than double since 2016.
While the ingestion of IoT device and sensor information are the biggest use cases for streaming
implementations, CIOs see over 3 of 4 streaming use cases come from customer- and product-oriented
situations that create and sustain competitive advantage over organizations without streaming initiatives.
The obstacles to implementation include technology constraints and business hurdles. CIOs recently
saw a shift from technology hurdles to cultural and strategic ones. Since 2016, when streaming
frameworks such as Apache Kafka® went mainstream, the focus is less on technical challenges like
connectivity and data quality and more on cultural and business strategy roadblocks.
As data-driven strategies evolve, event-based
business models influence organizations more.
The models are built around the availability of
real-time information on customers, payments,
and supply chains. They help customers pay for
the value they use—no more, no less. Suppliers
can provide just-in-time inventories to retailers
and even take over distribution.
The rise in streaming data use cases helps make
these business models a reality, and lowers
costs by minimizing exposure to risk and
incidents of fraud. Innovated payment models
built with shared resources, cross-sell/up-sell
revenues, and customer sentiment insights all
improve operating margins and establish
competitive advantage.
Event-driven business models power data-driven organizations
Increase in data-driven
strategies since 2016
56%
Streaming for Competitive Advantage
Doubling of real-time processing
requirements since 2016
2018 CIOS , “What are the primary business use cases that your organization’s streaming initiatives address?”
+2X
2018
2016
Technology
Enablers for
Real-Time
Streaming
Overcoming Roadblocks to Streaming
2018 CIOs, “What are the primary drivers for
adopting a real-time processing use case as part
of your company’s big data program?”
Streaming isn’t one size fits all.
CIOs see multiple operational
and analytical drivers for
streaming integration, from
transaction processing to
analytics to machine learning.
The top two focus on the
distribution of customer and
product information across
the organization.
Since 2016, “lack of a business
strategy to make use of data” more
than doubled in importance.
2.1X
Since 2016, “lack of integrated
teams to make use of streaming
data” nearly doubled in importance.
1.7X
Since 2016, “connectivity to
streaming data sources” fell in
importance nearly 35%.
-34.5%
Since 2016, “data quality, accuracy,
consistency, and reliability” fell over
40% in importance.
-40.4%
DECREASING OBSTACLES
INCREASING OBSTACLES
How to handle the flow of event data
Comprehensive visibility
into operational processes
Provide analytical platforms
(e.g., algorithms) with augmented
product information (e.g., data)
Supply real-time transaction processing
platforms (e.g., prepaid billing) with
augmented customer information
N=150
360° view of customer
Faster transactional
processing and analysis
Increasingly complex analytical
models, such as machine learning
New product/services development
Ingestion of IoT device
and sensor information10.7%
10.7%
13.4%
14.2%
13%
13%
24.9%
75%
25%
Customer- and
product-oriented
situations
Streaming as a
Strategic Decision
To support real-time applications,
organizations must adopt a complete
streaming strategy rather than only reacting
to point needs for event streaming.
Organizations adopt strategic streaming:
Leading organizations use strategic
implementations of streaming technologies.
In 2018 EMA research, 8 of 10 technology
leaders are adopting streaming strategies.
Furnish event information
with augmented
customer information
Furnish operational
analytical platforms (e.g.,
automated stock trading)
systems with operational
transaction information
Provide real-time
machine information to
optimization models
(e.g., transportation
logistics optimization)
24.7% 27.4%
17.4%
15.5%
15.2%
8 in 10technology leaders
are adopting
streaming strategies.
Confluent takes that framework and makes it a reality for
enterprise production environments. With Confluent Platform,
organizations have the enterprise features to streamline
deployment and secure their streaming operations. With professional services, training, and 24x7
support, Confluent has resources for organizations to be successful with real-time streaming
initiatives. This enables organizations to focus on the key business and implementation strategies and
less on the minutia of deployment and support.
Data streaming platforms like Apache Kafka and its enterprise-grade counterpart,
Confluent Platform, enable organizations to deploy real-time streaming applications by
acting as the connective tissue of the modern data-driven company.
Hadoop
DATA
INTEGRATION
REAL-TIME
APPLICATIONS
Database
Data Warehouse
CRM
Transformations
Custom Apps
Analytics
Monitoring
Management  Fully Managed Service
Monitoring & Analytics  Control Center
Operations  Replicator | Auto Data Balancer
Data Compatibility  Schema Registry
Development & Connectivity  Clients | Connectors | REST Proxy | KSQL
Apache Kafka®  Connect | Pub-Sub | Streams
• •
• •
• •
• •
• •
• •
• •
• •
Confluent CloudApache Open Source Confluent Open Source Confluent Enterprise
IoT Events Web EventsLog EventsDatabase Changes
For more information on how a streaming platform can drive business
value in your organization, visit: www.confluent.io/contact
Apache, Apache Kafka, Kafka, and associated open-source project names are trademarks of the Apache Software Foundation

[INFOGRAPHIC] Event-driven Business: How to Handle the Flow of Event Data

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[INFOGRAPHIC] Event-driven Business: How to Handle the Flow of Event Data