Developing a Governed Self-Service Analytics Strategy Unleash the power of data with trusted, relevant and timely business insights

Contents

Introduction 03

Part One: The Way Businesses Use DataHas Changed 04

Part Two: Accelerate Business Insightswith Data Cataloging and Discovery for Self-Service Analytics 07

Part Three: Drive Trusted Self-ServiceAnalytics with Cloud Agility and Scale 10

Part Four: Accelerate Next Generation Analytics with an Enterprise Data Lake 13

Part Five: Deliver Trusted Data ‘On Demand’ with an Integration Hub 16

Part Six: Be the Self-Service Analytics Hero in Your Enterprise Today 18

Further Reading 20

About Informatica® and Tableau 21

Tip: Click on parts to jump to the particular section you want.

02

Digital transformation is reshaping enterprise expectations from their BI and analytics solutions and changing the way people, processes, and technology are organized. To remain competitive, enterprises must evolve their analytics stack to be both extremely agile as well as founded on a governed, unified, enterprise-wide, intelligent data foundation.

Introduction

Not long ago, data lived in specific databases, and IT was its gatekeeper—if the business needed a report, only IT had the skills to prepare that data and build the report. Today, to support business agility, IT and business must collaborate to deliver governed self-service analytics. IT takes on the role of ‘data enabler’, delivering trusted, timely, accessible data for the business to conduct analysis and visualization and build reports and dashboards. Business users are best positioned to understand the meaning and context of data and IT to deliver the data for self-service analysis.

Many organizations are looking for ways to manage this transformation—and data is at the center of it all. In this eBook, you’ll learn how IT and business can work together to meet the data needs of the business while ensuring consistency, trustworthiness and compliance. The end result is improved productivity of analysts and data scientists to deliver timely and trusted data insights needed to win in today’s business climate.

03

Part One

The Way Businesses Use Data Has Changed

04

One thing is clear. As self-service analytics has worked itself into organizations, it has prompted a conversation around productivity, agility and efficiency for both IT and the business.

Organizations are leveraging data in new ways. IT and the business are collaborating as they never have before, and IT is providing a scalable and governed way to empower the business with self-service data.

Freedom for Data Exploration

Business users embrace Tableau because it helps them find important answers to move their business forward. It is in this self-guided exploration that they find answers they would not find in traditional reporting. And they can leverage data from modern sources, like social media metrics or other cloud sources like Marketo or Salesforce. When IT enables these explorers to self-serve data, the speed of business value delivery increases significantly.

IT can then become a coach to the business user to get the most out of their reporting. When the business users discover a pattern or report worth repeating during their ad hoc explorations, they can recommend it to IT to operationalize those for ongoing use.

Scale Governed Exploration

Once the business users and IT agree on data and reports that should be shared more broadly, organizations need to figure out how to operationalize and govern that data. By building in processes that allow for governance and by using tools that make sure the data is fresh and up to date, IT can take its eyes off building reports and get into higher-ROI data-centric initiatives that provide greater value to the company.

The Way BusinessesUse Data Has Changed

05

Here are three new ways organizations have shifted in the use of data:

Embracing new data sources: Organizations are quickly adopting a data centric approach to making decisions. Many new data sources—from cloud, SaaS, social, IoT and more —are being used in analysis. For example, social media can provide information about what customers are saying, but obviously that is not available on traditional on-premises systems. IT and the business must collaborate to systematically and efficiently onboard and incorporate new types of data, for business decision making.

Merging cloud and on-premises sources: Today business users have gained new skills, namely data wrangling. You can often find business users trying to tie together data sources for analysis. Maybe they are using sensor data against sales data found on an internal system. It is important for IT to help with this. It provides IT a window into what the business user is trying to accomplish so IT can ensure the best data is used. IT tends to be the expert on that topic.

Empowering all employees with self-service data sets and analytics: Finally, providing broad access to the data is a substantial key to success. One of the biggest challenges Tableau users face, for example, is discovering enterprise-wide data for analysis and understanding its metadata, such as data lineage, relationships and business definitions. IT can collaborate with business users to provide them with the means to easily discover and understand relevant and trusted data assets for analysis. This exemplifies how an agile organization operates. IT is putting the right data in the hands of the people who are on the front lines of the business. Business users can then quickly ask questions and get answers, encouraging self-reliance and productivity.

To create this type of a data-centric agile organization, businesses must adopt a new data management approach. The approach should provide:

– Flexible scaling of data pipelines to support changing data volumes

– Secured connectivity to rapidly integrate cloud and on-premises data sources

– Discoverability of trusted and relevant data assets for self-service analysis and a means to understand data that underlies reports and dashboards

The Way Businesses Use Data Has Changed

06

Part Two

Accelerate Business Insights with Data Cataloging and Discovery for Self-Service Analytics

07

Accelerate Business Insights with Data Cataloging & Discovery for Self-service AnalyticsAs enterprise data volumes increase and the number of data assets grows, discovering relevant data assets is increasingly like looking for a needle in a haystack. Business analysts and other consumers of data may not have complete knowledge of all available data assets because the details may be scattered across the enterprise in multiple locations.

For a modern self-service analytics experience, business users need to seamlessly discover and understand all relevant and trusted data and metadata across the enterprise, eliminating hidden data silos. An enterprise data catalog, embedded within their self-service analytics environment, is designed to meet this need. It also helps business and IT collaborate and fuels self-service analytics with IT-trusted data assets across multi-cloud and on-premises environments.

These new self-service cataloging and discovery capabilities complement and extend self-service analytics tools’ capabilities to rapidly and intuitively prepare and share data among users.

Bridging the Business and IT Divide for More Successful Analytics Projects

An end to end governed self-service analytics solution, incorporating a data catalog, provide users with easy-to-use visual tools to discover and understand enterprise-wide data assets, lineage, and relationships, blend and cleanse data from multiple IT-trusted sources and to analyze and visualize these datasets in their analytics environment. Data cataloging also helps business users understand reports, dashboards and visualizations better by viewing data and metadata side by side. All these capabilities bridge the business and IT gap, enabling both groups to collaborate and accelerate the adoption and governance of enterprise-wide analytics.

Making the Most of Both Multi-cloud and On-premises Worlds

As organizations look to nimbly adapt to changes in their business, there is still the need to continue with on-premises applications and systems along with cloud-based applications. Innovative, flexible solutions, such as an enterprise data catalog, help ensure the effectiveness of on-premises and multi-cloud environments by helping organizations leverage the cloud benefits of speed, agility, and scale while continuing to derive maximum value from their traditional information frameworks.

08

Accelerate Business Insights with Data Cataloging & Discovery for Self-service Analytics

An enterprise data catalog delivers:

– Google-like search and machine learning-based data discovery

– Profiling statistics

– Holistic view of data

– End-to-end summary and detailed data lineage

– Integrated with policy driven governance

– Crowdsourced data enrichment

– Recommendations based on machine learning

– AI-driven data classification and labeling

– Built for big data scale

– Broadest metadata connectivity

– Extensible and embeddable with open API’s

� Google for enterprise data assets� Data Lineage, holistic relationship view� Trust with data profile� Access to data

Self Service Analytics[Data Analysts, Data Scientists]

� Associate business glossary to technical objects � Verify business to technical lineage� Track key data elements for compliance

Data Governance[Data Stewards]

� Analyze column-level lineage & change impact � View transformation logic� Data asset and BI usage

Data Asset Management[Architects, Developers]

� Technical� Operational� Usage

Broad MetadataSources

� Glossary� Policies� Process

Business Context

� Comments� Ratings� Behavior

Wisdom of Crowd

Knowledge Graph

Enterprise Data Catalog

Structure Discovery,Profiling and

Domain Discovery,Similarity Clustering,Recommendations

AI Curated Catalog

AI Engine

Business Glossary Associations,Business Classifications, Annotations, Comments

Business and Crowd Sourced

Curation

09

Part Three

Drive Trusted Self-Service Analytics with Cloud Agility and Scale

10

Access to data and analytics has become essential for every employee—especially business users—to perform daily jobs.

An Integration Platform as a Service (iPaaS) solution helps rapidly build, scale and maintain data and application integrations across multi-cloud and on-premises environments delivering trusted, timely, secure data for agile cloud self-service analytics.

Empower any user, in business or IT, with the right role-based tools, so they can execute quick integrations, transformations, and aggregation of data for self-service analytics. To maximize productivity for all users, your iPaaS solution should include capabilities like guided development with data wizards, out of the box popular mappings, templates, and configurations and pre-built connectors to any cloud and on-premises application and data sources and targets.

Make sure that your iPaaS solution can inherently support scale and agility required in today’s modern cloud analytics solutions, while ensuring data security and privacy across your enterprise analytics solution.

To support your multi-cloud and on-premises environment, your governed self-service analytics solution should include an iPaaS with a broad ecosystem, offering pre-built connectors for rapid integration of leading systems and applications. Of special importance to this modern cloud analytics solution is comprehensive connectivity to popular self-service analytics solutions such as Tableau, Cloud data warehouses such as Amazon Redshift and Microsoft Azure SQL Data Warehouse and a variety of SaaS and on-premises applications, including Salesforce, NetSuite, Marketo, SAP, Workday, Microsoft Dynamics, Oracle and more. This enables enterprises to achieve scale, agility, and modernization with cloud-based, cost-effective data integration and data management solutions for any type and volume of data.

Drive Trusted Self-Service Analytics with Cloud Agility and Scale

11

Drive Trusted Self-Service Analytics with Cloud Agility and Scale

With iPaaS for a modern self-service analytics solution, you can:

– Fuel dashboards with relevant, timely, and trusted data

– Build and scale agile self-service analytics with cloud data warehouse

– Empower business users with self-service tools

– Establish innovative best practices driving company-wide analytics adoption

Social Media

Cloud applications & databases

ERP, On-premises Apps Legacy RDBMS Traditional Data Warehouse(Terradata, MS, Oracle, etc.)

Cloud Data WarehouseiPaaS

Self-ServiceAnalytics

Email, Files

IoT Logs

12

Part Four

Accelerate Next Generation Analytics with an Enterprise Data Lake

13

Accelerate Next Generation Analytics with an Enterprise Data LakeSelf-service data exploration, experimentation and ad hoc analytics, inclusive of raw data analysis, are a key aspect of a data analyst’s role. With the proliferation of data sources, and especially unstructured data such as social data, web logs, and IoT, as well as the growing pace of business, a systematic and repeatable approach to data management for self-service experimentation is more important than ever.

A systematic and repeatable approach to enterprise data lakes enables raw big data to be automatically discovered so that data analysts are empowered to turn these data

sets into trusted information on a self-service basis. Analysts can quickly find the data they’re looking for using semantic and faceted search, while automatically understanding data lineage and data relationships. Data analyst teams can also easily collaborate with one another and share results in project workspaces. As they add data sets to their project workspace, machine-learning algorithms work in the background to recommend even more data sets of interest. Easy-to-use Excel-like tools, aided by machine learning, empower analysts to prepare data for accurate and consistent visualizations, dashboards and reports without waiting for IT.

This approach facilitates agile self-service analytics, whereby the analysts are able to rapidly experiment and learn through the help of machine learning. It also allows business analysts to collaborate with IT, so that proven analysis, reports and dashboard can then be operationalized and shared with the rest of the organization in a repeatable and automated manner.

14

Accelerate Next Generation Analytics with an Enterprise Data Lake

INGEST

Intelligent Data Lake Management

Multi-latency data ingestion & edge computing

STREAM

Streaming analytics &

event processing

INTEGRATE

Integrate all types of data of any volume at

scale

ENRICH

Cleanse and enrich trusted

data

PREPARE

CATALOG SEARCH PARSE MATCH LINEAGE RECOMMENDATIONS

Prepare data for analytics &

collaborate on projects

DEFINE

Define and verify data

governance policies

CATALOG

Discover, catalog, and

curate all enterprise data

RELATE

Match and relate identities

& entities at scale

PROTECT

Data security intelligence to

Detect & protect sensitive data

DELIVER

Orchestrate data flows and provision data

to the enterprise

ClouderaAltus

AI Engine

15

Part Five

Deliver Trusted Data ‘On Demand’ with anIntegration Hub

16

Deliver Trusted Data ‘On Demand’ with an Integration HubSelf-Service Access for Self-Service Analytics Users to Certified Hub Data

With an integration hub for self-service analytics, users can self-subscribe to curated data feeds. The Hub distributes processed data via publications that are available for use by analysts throughout the company, without requiring them to search for the fresh data they need themselves. No more waiting on IT to make data available through new data integrations.

Self-Service for Distributed Teams

Self-service tools reduce the time to access high quality, fresh data for analysis in Tableau. An integration hub should have an easy-to-use, highly productive user interface (including wizards) that automates processes and subscription to data by analysts and line of business IT teams. Integration Hubs can facilitate more agile collaboration between analysts and IT, leading to greater productivity and faster, better quality insights.

Centralization for Consistency and Governance

With a centrally managed hub, data consistency can be boosted to benefit all your analytics instances. An integration hub should offer the full breadth of connectivity and processing power that you need so you can connect to virtually anything and execute advanced data processing, including complex data transformations, data quality, master data management, contact data validation and enrichment, and data masking. This can be done centrally, for the benefit of all users.

Integration Hub

Self-ServiceAnalytics

Database Finance

Application

SaaS Apps

ERP

Big DataAnalytics

Sales

Marketing

Publishers Subscribers

With efficient hub-based data integration centralization, users are empowered while complexity, redundant data workflows and their associated data transfer and API costs, are all reduced.

17

Part Six

Be the Self-Service Analytics Hero in Your Enterprise Today

18

Be the Self-Service Analytics Hero in Your Enterprise TodayIT is the front line of providing data to the business. It is your job to ensure the data is rock solid— integrated and governed so it’s timely, accurate and can be accessed and shared securely both internally and externally with customers and partners.

Siloed data is the biggest impediment to true insights. Glean new insights for better business outcomes and competitive advantage by integrating data across lines of business and multi-cloud and on-premises data sources. And enable your Tableau users to easily discover, understand and catalog data assets for analysis.

Look for your comprehensive governed self-service analytics solution to drive the business impact with trusted data-driven insights.

19

To learn more about how you can benefit from an end-to-end governed self-service analytic solution, we recommend our solution brief: “Informatica for TableauInformatica for Tableau”

Further Reading20

READ MORE

IN18-1120-03276

© Copyright Informatica LLC 2018. Informatica and the Informatica logo are trademarks or registered trademarks of Informatica LLC in the United States and other countries

About Informatica About Tableau

Informatica is the only Enterprise Cloud Data Management leader that accelerates data-driven digital transformation. Informatica enables companies to unleash the power of data to fuel innovation, become more agile and realize new growth opportunities, resulting in intelligent market disruptions. With over 7,000 customers worldwide, Informatica is the trusted leader in Enterprise Cloud Data Management.

For more information, call +1 650-385-5000 (1-800-653-3871 in the U.S.), or visit www.informatica.comwww.informatica.com. Connect with Informatica on LinkedInLinkedIn, TwitterTwitter and FacebookFacebook.

Learn more at www.informatica.com/Tableauwww.informatica.com/Tableau.

Tableau is the enterprise analytics platform that enables your organization to explore trusted data in a secure and scalable environment. Give people access to intuitive visual analytics, interactive dashboards, and limitless ad-hoc analyses that reveal hidden opportunities and eureka moments alike. Get the security, governance, and management you require to confidently integrate Tableau into your business—on-premises or in the cloud—and deliver the power of true self-service analytics at scale.

For more information, visit www.tableau.comwww.tableau.com. Connect with Tableau on LinkedInLinkedIn, TwitterTwitter and FacebookFacebook.

Learn more about Tableau for the EnterpriseTableau for the Enterprise.