Slow Data Kills Business eBook - Improve the Customer Experience

SLOW DATA
KILLS BUSINESSImprove customer experience with the right
data management system

We live in an era where customer experience trumps product features and functions. How do you exceed
customer’s expectations every time they interact with your organization? By leveraging more information
and applying insights you have learned over time. Turning data-driven power into delightful experiences will
give you the advantages required to succeed in today’s climate of one-click shopping and crowd-sourced
feedback. Whether you are a retailer, a banker, a care provider, or a policy maker, your organization must
harness the power of growing data volumes, data types, and data sources to foster experiences that matter.
Introduction
76 percent of organizations believe that untimely data has inhibited
business opportunities.1

or most enterprises, gaining a complete view of user experiences and context requires the modernization of legacy and disparate
technologies, many of which have been designed to support siloed applications/functions in individual departments. The difference
between modern and legacy platforms lies in their capabilities.
Holistically support the digital
transformation brought on by massive
cloud, mobile, and social adoption while
simplifying development and support
requirements.
Support both transaction processing and
analytics in real-time.
Empower organizations to deliver modern,
data-rich applications amongst legacy
applications, support on-prem and cloud
deployment options, mitigate risk
between open source and
enterprise-grade software.
Featuring new market data from IDC, this eBook will highlight the motivating factors driving the evolution of the modern data platform
and explain how organizations can benefit from a simplified, yet more powerful, data infrastructure.
MODERN DATA PLATFORMS
F

The digital evolution is driving the push toward the modern enterprise, where transactions and analytics
complement one another to derive new, actionable insights before opportunities are lost. To meet these
rapidly evolving market and consumer demands, organizations must accelerate their path to innovation.
According to the IDC study of more than 500 organizations across the globe, speeding innovation was the top
business priority for more than two-thirds (33.9 percent) of respondents, with streamlining operations (31.9
percent) coming in as the close second.2
Chapter 1: Business Drivers

DATA LATENCY.
Making a decision based on live data requires the ability to
perform analytical queries with transactional data in real-time.
Most companies, however, are basing decisions on data that is
anywhere from 10 minutes to two hours, or even days, old.
These latencies make it impossible for organizations to
capitalize on real-time and near real-time business
opportunities. It is not surprising, then, that 76 percent of
respondents reported that the inability to analyze current
data inhibits their ability to take advantage of business
opportunities and 54 percent claimed that it also inhibits their
ability to improve operational efficiency.3

THE ROLE OF TRANSACTIONAL AND
ANALYTIC DATA PROCESSING IN THE
ENTERPRISE.
Today’s enterprise often consists of two separate data
processing arms: Transactions and Analytics. Understanding
the role and requirements of each arm enables organizations
to better understand their limitations and opportunities when it
comes to processing and analyzing data to positively impact
the business.
Transactions typically involve the processing of data in relation
to regular operations conducted by the business and are
optimized for write, not query, speed. Analytics are optimized
for query performance and provide organizations with insights
based on specific questions.

THE PATH TO REAL-TIME ANALYTICS GOES
THROUGH ETL (EXTRACT/TRANSFORM/LOAD).
Data often needs to move from transactional systems to analytics, increasing
complexity and latency that slows the business down and can lead to missed
opportunities. Transactional data processing is often limited in its ability to
quickly perform analytic queries, while analytics data processing depends on
first moving and pre-processing data from transactional systems, making it
impossible to deliver valuable real-time insights.
According to the IDC study, 86.5 percent of organizations use ETL to move at
least 25 percent of all enterprise data between transactional and analytical
systems. And nearly two-thirds (63.9 percent) of data moved via ETL is at
least five days old by the time it reaches an analytics database.4
This is a
critical obstacle for most organizations that want to deliver the right customer
experience in the moment.
EXTRACT
TRANSFORM
LOAD

CURRENT DATA INFRASTRUCTURE
CHALLENGES FOR APPLICATION
DEVELOPERS.
Delivering the ultimate customer experience demands high
performance and quick response times. But, this is independent
of the many sources streaming data into the application or the
type of mixed workloads the application requires (processing
large volumes of transactions while executing complex
predictive analytical algorithms). Adding to the complexity is
the need to support more data types (structured, unstructured,
etc.), larger data sets, and an accelerated path from analysis to
action introduced by mobile users, IoT/sensor data, and fickle /
constantly emerging trends.

NEW DATA TYPES
✓✓ Relational
✓✓ Internet of Things
✓✓ Streaming sources
✓✓ Sensor data
✓✓ Document
✓✓ Key value
✓✓ Video/audio/image
✓✓ Object
✓✓ Geospatial
Often, to support the different types of data and applications
required, companies utilize several database systems across the
organization, which means the data is saved and stored in
disparate places and formats. Each database may be unique to
the application, data, and workload type.
More than 60 percent of respondents to a recent
IDC survey reported having more than five
analytical databases, and more than 30 percent
have more than 10.5
The majority of respondents have more than five
production transactional databases, while 25
percent had more than 10.6
Companies now have the challenge of
harnessing that data and determining how to
extract value from it by applying it to business
operations – making sense of all the data by
tying all the sources to an individual customer,
patient, citizen, investor, etc.
MULTIPLE DATABASES INCREASE COMPLEXITY

By combining analytic and transactional data processing,
including a range of data types in support of digital
transformation, a modern data platform lies at the heart of a
data-driven business.
A data management platform is a centralized computing
system for collecting, integrating, managing, and analyzing
large sets of structured and unstructured data from
disparate sources at massive scale (distributed as well as
single server) and can support multiple use case scenarios
and workloads (transaction processing and analytics) with
native data and application interoperability.
There are three central pillars to a modern data
platform.
It must support all data types and
workloads in a single architecture.
It must incorporate database management,
interoperability, and analytics.
It must be reliable and provide high
throughput and low latency.
Chapter 2: What is a modern data
platform?

Why do companies need a consolidated data platform? Because disparate systems create a disconnect between
insight and action, resulting in a delay in the feedback loop that drives the ultimate customer experience. A
consolidated data platform helps companies achieve their core (IT-related) business objectives, while simplifying
architecture, reducing cost, speeding innovation, and streamlining operations.
CONNECTING INSIGHT AND ACTION
Managing multiple databases is complex, expensive, and
introduces latency issues. As data itself grows more complex,
deploying a unique database and data integration system for
each business need creates unnecessary complexity. It means
tactical decisions cannot be supported as long as data is
segregated into transactional and analytical databases. Most
users need a broad variety of data type support that goes well
beyond what native relational database management systems
(RDBMS) provide. Lastly, the rising costs of database
management is cost prohibitive to many organizations. The
maintenance of many databases leads to excessive cost and
complexity in the data center.

How do you define the ultimate experience for your customers, partners,
and stakeholders? What data do you need to ensure all of the information
can be accessed for both guiding a decision and executing intelligent,
data-driven actions?
To answer these questions, you must first identify your organization’s data
infrastructure needs. This includes understanding where your data resides,
how often and by what means it is accessed, and how it is to be analyzed.
Companies no longer need to choose between data from different sources
and having real-time access to information through robust analytics. They
can access data how and when they want and have the ability to make the
data actionable in real-time. Modern data platforms can unlock your data
and transform your business.
• Where is your data located?
• How often is it accessed? And by
what applications?
• How/where is new data “routed”
into your organization?
• What data is being analyzed?
Current? Data lakes, etc.?
• Where are the biggest gaps in your
data infrastructure?
Chapter 3: Delivering ultimate
data-driven experiences
Answering these questions
will help you identify your
organization’s data
infrastructure needs.

Primary access to data can deliver significant benefits to company operations and customer
experiences. As more and more data is generated by companies and their customers, it is
important to be able to easily access and analyze this information and use it to inform
business decisions and real-time customer experiences. A modern data platform
simultaneously supports analytical and transactional decisions and streamlines data
infrastructure costs, driving more intelligent insights across the entire organization.
What a consolidated data platform means for your business
Streamlines operations and reduces IT infrastructure costs
Personalizes customer experiences the moment it matters most
Helps IT and Line of Business leaders gain a strategic seat at the table
Conclusion
1-6: IDC InfoBrief, sponsored by InterSystems, Choosing a DBMS to Address the Challenges of the Third Platform, May 2017
© Copyright 2018 InterSystems Corporation. All rights reserved.

Slow Data Kills Business eBook - Improve the Customer Experience

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