Firehoses of data are streaming into enterprises from various sources, putting pressure on old data integration systems to present information faster. This has led to the development of self-service data integration solutions enabled by new storage and processing frameworks. The document discusses the five essential elements that a self-service data integration solution should provide: 1) comprehensive data discovery, 2) big data capacity, 3) user management and access controls, 4) evolution of data governance, and 5) a single platform from one vendor. It then provides an example of how a pet hospital network used a self-service data integration solution to integrate data from multiple systems and locations to facilitate insights and decision making.

1. The Five Essential
Elements of Self-Service
Data Integration
INTRODUCTION
Firehoses of data are blasting the modern enterprise. From every direction they
stream into the data center – from warehouses, marketing systems, spreadsheets,
social media, and the cloud.
Old Data Integration systems are struggling to present information at the speed
required for optimal intelligence. To compound matters, business analysts expect
more comprehensive data sets that incorporate new and disparate sources.
Both of these factors have led to the development of the next-generation of data
preparation solutions: self-service Data Integration. Enabled by the latest storage
and processing frameworks, self-service Data Integration represents a shift toward
instant analysis and real-time navigation for the enterprise.

2. Solution Spotlight | Self-Service Data Integration
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The Five Essential Elements of
Self-Service Data Integration
W h a t y o u n e e d t o c o n s i d e r a s y o u m o v e t o w a r d s e l f - s e r v i c e
a u t o n o m y , a n d h o w o n e s o l u t i o n - U n i f i S o f t w a r e - m a t c h e s
u p t o t h e s e n e e d s .
1. COMPREHENSIVE DATA DISCOVERY
The spirit of self-service is to put powerful tools in the hands of your most perceptive analysts;
allowing them the freedom to identify emerging trends ad hoc. The Unifi platform enables
this through a newly developed automated metadata profiling feature that facilitates data
discovery. The system also normalizes data attributes and allows them to be linked
automatically, greatly reducing manual processing time and enabling easier associations
with legacy attribute naming conventions. Another notable benefit of this Unifi metadata
management and search function is that information about the data is stored together with the actual data.
This puts additional power at the fingertips of business analysts to interpret the data definition more
holistically.
2. BIG DATA CAPACITY
New distributed storage and processing frameworks like Hadoop are flourishing in the
enterprise due to the demand that real-time and streaming data has put on organizations.
No longer is it enough to store data in traditional relational databases, due the pure
volumes of data that now come from embedded sensors, computer applications, social
media activity, and mobile devices.
The Unifi Data Integration platform runs natively on Hadoop, leveraging both Hive and Spark resources and
unlocking top-end processing horsepower. Unifi gathers, normalizes and integrates data from any source:
social, IoT, On-Premise, cloud; wherever data comes from. Unifi provides dozens of native connectors to
ensure you can connect and search through hundreds of data sources right at your fingertips.

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3. USER MANAGEMENT AND ACCESS CONTROLS
The core essence of a successful self-service Data Integration solution is the integrity of the
user management function. Historically, access to the integration process has rested with
a coterie of embedded IT professionals. Accomplishing the shift from traditional cycle-
based Data Integration to the more fluid self-service approach is best facilitated through a
robust user management system offering real controls to ease the transition. Unifi
supports LDAP and Active Directory to enable secure access. Organizations can set up groups,
for example: finance, marketing, manufacturing, data science, and then input users into each group and
decide which data sets each group can access.
4. THE EVOLUTION OF DATA GOVERNANCE
With greater Data Integration comes greater responsibility. Where in the past an organization
may have been pleased to have built data sets and models that could provide straight-
forward R&D, sales, and customer service guidance, the modern customer profile record
with all of its personal identifiable information and deeper reference data demands great
respect and uncompromising security. Perhaps the most fundamental question facing a
self-service Data Integration implementation is the question of governance. To be considered
best-of-breed, any self-service Data Integration solution must provide consideration to the usability, integrity,
and security of the data employed in an enterprise. In the end, Unifi opts to entrust that power with the
traditional IT professional. With the Unifi platform, IT can shift Data Integration responsibilities to business
analysts while maintaining control of the underlying environment, access to data, and visibility of attributes.
5. ONE PLATFORM – ONE VENDOR
The unique selling proposition of the data warehouse was the ability to offer analysts a “single
version of the truth”. The ultimate presentation layer for all the data assembled. With the
realization of modern self-service Data Integration, a similar requirement emerges: One
Platform for All Data. Many solutions are moving toward a self-service approach, but do
not have the ability to deliver full feature capabilities and might require licensing and
maintaining two or more systems. It would be a mistake to begin such a fundamental
organizational transformation by introducing more than one solution. Modern data integration is hard
enough. Asking business users to learn multiple new tools is a non-starter.
With its wide range of data connectors, advanced discovery features, powerful processing capabilities and
thoughtful controls Unifi is a solution that can deliver against the One Platform for All Data standard that is
increasingly becoming a requirement in the enterprise today.

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CASE STUDY – COMPASSION-FIRST PET HOSPITALS
Data Discovery is Core to the Business
Compassion-First Pet Hospitals is a growing network of specialty, emergency and hybrid
animal hospitals located throughout the United States that offers a wide range of
specialties, including integrative oncology, cardiology and ophthalmology – while also
investing in novel technologies rarely found in veterinary medicine. Compassion-First’s growing base of 30
hospitals receives referrals from 15,000+ veterinary clinics annually.
Compassion-First was founded by 40-year industry veteran John Payne. His mission is to create the “Mayo”
clinic of the animal health industry, providing the very best medicine and outcomes to pets and their owners,
while also transforming the financial performance of the hospitals enabling a continuous investment in
innovation and quality.
With each acquisition, Compassion-First inherits a wide variety of information technology systems (practice
management, financial, medical records) and data (business/clinical, structured/unstructured). This presents
a number of challenges as Compassion-First rapidly moves to integrate the new hospitals while pushing to
accelerate the strategic initiatives that are focused on improving evidence-based decision making and cross-
hospital collaboration.
To tackle this problem, Compassion-First turned to Michael Welsh, Vice Chairman of the European Social
Entrepreneur Fund and tech industry investor/veteran. What has transpired is a partnership with Compassion-
First in the funding and creation of a new insights-as-a service organization called xtendCare that Mr. Welsh
has also agreed to guide as Chief Executive. The xtendCare group has been aggressively building a data and
decision science capability that leverages a best-of-breed technology stack. During the “buildout” they paid
particular attention to the successful paths already charted by notable world-class human health initiatives –
such as the big data analytics company Explorys (note: Explorys was a spin out from the Cleveland Clinic and
was then acquired by IBM).
“We have three primary domains where we are developing our data/decision analytics capability and IP:
evidence, insights and outcomes,” says Mr. Welsh. “In the last few months we’ve made significant inroads in
overcoming the data fragmentation, data quality and silos that have existed in our hospitals. We are now
focusing on solutions that extend our reach to more sources of data (evidence), which helps us rapidly deliver
insights and transforms the quality and speed of our decision-making.”
Compassion-First had evaluated a number of data preparation platforms in an attempt to solve their business
issues. However, it was clear that a data preparation platform without integrated discovery was never going to
meet the requirements of the analysts. To tie this solution all together, Welsh turned to Unifi. “To make our
data valuable we need to make it frictionless,” explains Welsh. “This means the metadata describing our data
must be rich, clean, and constantly improving to facilitate the discoverability of data and thus its value.”

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One obvious use case that Welsh highlights is diagnostic tests. When a price change occurs from the supplier
or diagnostic lab it may take several weeks or months for all of the hospitals and individual clinics to reflect
the impact of that change in all the systems. It’s not just about increasing the cost – but being able to assess
the impact of the cost on client access, profitability, etc. “Unlike human healthcare where there is often an
insurance policy, animal care is a cash business,” explains Welsh. “You pay your bill when the services are
performed. Not having real-time insight into the effects of COGS on various treatment protocols can have a
significant financial impact on a hospitals bottom line, veterinary compensation and most importantly the
ability to afford the delivery of the highest quality of medicine. As of now, veterinary hospitals across the
industry do not have the capabilities in place that would allow them near real-time visibility and insights – so
without a doubt profitability is impacted.
By deploying Unifi in their enterprise, Compassion-First will be able to deliver roll-based, self-service analytics.
Because the data is organized, clean and readily available, hospital executives and staff will be able to derive
valuable business and clinical insights more quickly that will facilitate smart “evidence based” decision
making. Even predictive analytics will be more accurate and delivered sooner. For example, accurately
forecasting how many veterinarians to schedule in an emergency room based on various external trends and
historical patterns gleaned from the medical records.
Once all the Unifi’d data is available and readily accessible to the hospitals, Welsh can offer the “Insights as a
Service” capability to other organizations in the animal health and life sciences industries.
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