data management what you need to know 107331

7/25/2019 Data Management What You Need to Know 107331

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PRESENTED BYSPONSORED BY

DATA MANAGEMENT:

What You Need to Know and Why


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Introduction

Data Management: What You Need to Know and Why

It has become common knowledge that an organizations success and ability to compete revolves around its ability

to manage information. But as organizations pivot to embrace this reality, they dont always learn or provide a basic

understanding of what managing information actually calls for.

Data management has been around for a l ong time. Understand the basics

and youll know a lot more about what it will take to overcome any g iven

data project or challenge youre contemplating.

For starters, try not to think about computers or technology. Though

those things are an obvious part of the answer, managing information no

longer centers on the ability to store and process or to create and delete

data. Think instead about the users of and uses for information. Managing

information is most essentially about supporting better decisions. It is

about delivering value from the islands and ecosystems of data that can

support business processes and new opportunities for analysis.

It doesnt mean we can set sail without a compass or anchor. In order to

add value, effectively managed information has always needed to arrive

in context and in a proper form for decision support and analysis. It must

come to the user in a way that is defined and consistent, integrated and

interwoven across multiple sources, aligned and synchronized. These

attributes are the core processes of data management common to just

about every data-driven project.

The essential building blocks of data management that every

business manager ought to know include:

Master Data Management Data Governance

Data Quality

Data Integration and

Data Federation

INTRODUCTION

INTRODUCTION MDM DATA GOVERNANCE DATA INTEGRATION ABOUT USDATA FEDERATIONDATA QUALITY


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You can use the tabs and resources in these mater ials to grasp each of

the terms above. Even if you know or think you know what they mean, see

how your view compares to the explanations within. Use it as a reference,

circulate and share it. Most important, spend a little time with it.

Quiz yourself and ask:

Can I explain aloud in simple terms what each of these things

means?

Do I understand why each of these terms is important in the contextof a data-driven project?

Can I identify who in my organization would provide or be

responsible for these things?

Understand these terms and their implications and youll have a head

start (and be a more valuable voice) in any new data-driven project you

undertake. Even if youre contemplating new layers of opportunity and

complexity in areas like cloud computing, big data and mobile access,

the foundational processes are unchanged.

Introduction

Data Management: What You Need to Know and Why (CONTINUED)

INTRODUCTION


READ MOREDATA MANAGEMENT BACKGROUNDER

What it is and why it matters


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MDMTaking MDM to the next level

Evan Levy, Vice President of Business Consulting, SAS

What does the future hold for master data management?

Find out what those in the trenches are talking about when it comes to MDM.

Ive attended the Gartner Master Data Management conference during

the past few years and always come away learning something new and

feeling intrigued at the issues that are discussed as well as the i ssues that

seem to escape the podiums attention. The Gartner conference is one

of the more popular activities for the Master Data Management (MDM)

world. Theres lots of customer discussion, numerous analyst opinions and

invaluable backroom and between-meeting conversations. I often find the

most valuable learnings come from the discussions that occur during and

after the presentations. This year was no different.

The one area that ref lected the diversity of content between analyst

remarks and between-meeting discussions was the Next Level of MDM.

The presentat ions consistently referred to mu lti-domain MDM as the next

level of MDM (for those of you who arent familiar with the term, multi-

domain MDM is the concept of a single MDM hub platform supporting themastering of multiple subject areas).

The attendee d iscuss ions I participated in ref lected a more advanced

view of the desired direction that the MDM industry should take. The

discussions werent focused on multi-domain MDM; they were focused

on expanding MDM to address the obstacles that most companies are

experiencing in managing and deploying the exploding breadth and

volume of data that exists within their companies.

I spend a fair amount of my professional time meeting with customers to

discuss their views on data strategy and data management. The details

discussed between-meetings at the most recent Gartner conference are

fairly consistent with the customer discussions Ive had over the past few

months. I thought Id share some of the more common discussion topics

regarding future MDM functions and capabilities.

I often find the most valuable learnings come from the discussions that

occur during and after the presentations.

MDMINTRODUCTION

MDM DATA GOVERNANCE DATA INTEGRATION ABOUT USDATA FEDERATIONDATA QUALITY


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Whats in store for MDM

Data directory services

Theres no greater consumer of time than searching for data wi thin the

enterprise. Its not uncommon for developers and end users to spend

40 percent of their time searching for the data they need to analyze or

include in a new application (and that reflects the existence of data marts,

reporting tools, data warehouses and analytic systems). While existing

MDM products do an adequate job o f providing details to the subjec t areamaster elements, they dont support the other 90 percent of the elements

contained across the companys numerous operational and analytical

systems. Expanding the MDM platform to track and identify (but not store)

the various locations of additional subject area details would be invaluable.

Centralized metadata

The motivation for MDM is to provide access to a subject area master

record along with the details of the contributing sources. What folks really

want is a single centralized location for metadata content. Developers

and end-users are desperate to understand the datas origins, definitions,meanings and rules. Most MDM products contain those details for

mastered elements it makes sense to expand the MDM hub to suppor t

metadata services to store, retain and share metadata for all of the

elements associated with a subject area.

A data provisioning platform

Since the MDM hub knows the various source systems where the subject

area details reside, why not position the MDM hub to retrieve the data?

To be fair, the idea isnt to support query processing and joins, just data

provisioning. If an application wants to retrieve all the descriptive details

for a customer, why is database (or system) location and access details

required? The MDM hub knows where the data i s; let it go and retrieve the

data and deliver it to the requestor.

Centralized data security

Most IT organizations (and product vendors) realized years ago thatimplementing user access security for individual applications and

systems was too cumbersome and problematic. Centralized application

and system access control has been a round to address this problem.

Unfortunately, such a capability doesnt exist for data. MDM hubs can

already store and retain CRUD (create, read, update, delete) for their

own purposes why not centralize all data security within the MDM hub?

Rather than every database and application rely upon their own data

security method, centralize data level security details within the MDM hub.

Supporting enterprise data services

Every MDM product relies on web services as an application interface

mechanism. While the most visib le services include subject area CRUD

processing, most MDM hubs require the creation of numerous lower level

services (data correction, value standardization, database access, etc.) to

address the breadth of functionality necessary for production deployment.

These se rvices should be positioned as enterpr ise-level data services

for all IT systems. Unfortuna tely, most MDM products dont include the

necessary tools and functions to integrate into a larger SOA and enterprise

data services paradigm.

MDMTaking MDM to the next level(CONTINUED)

MDMINTRODUCTION



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Master data management has proven to be a highly valuable component

for many IT organizations. The existence of a central platform to manage

subject area master details has dramatically simplified data access and

improved data quality for application systems.

While I wont argue with the merits of multi-domain MDM (Ill save that for

later), the concept of multi-domain hubs has been discussed for nearly

eight years and lots of fo lks have already implemented them. Theres a lot

more we can accomplish with MDM technology.

Weve only scratched the surface when it comes to simplifying data

management and access for our IT and business user stakeholders. In

the era of big data, third party data providers, hundreds of data sources

and exploding data diversit y and volume, maybe its time to evolve MDM

to leverage its strengths to address bigger enterprise data problems. The

next level of MDM should be about leveraging its strengths to address

the data management obstacles that continue to steal 40 percent of

everyones time.

MDMTaking MDM to the next level(CONTINUED)

MDM

WATCH NOW

WHAT IS MASTER DATA MANAGEMENT?

Challenges in the Effective Use of Master Data Management TechniquesMaster data management is an activity that goes beyond the needs of any single business function, so it is

important to finesse any recognized barriers to success. In this paper by data quality and MDM thought leader

David Loshin, we look at how data consolidation (the typical approach to master data management) can fail to

meet data consumption needs. By transitioning from a consolidation approach to a data utilization approach, you

will see how MDM can contribute to a long-term information strategy that uses best practices to take advantage

of shared repurposed enterprise information.

INTRODUCTION


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Data Governance

DATA GOVERNANCE

Best Practices in Enterprise

Data Governance

This best pract ices paper explains where and

how SAScapabilities (such as the business

data network, reference data management, data

federation, data quality, data management and

master data management) can be used to help data

governance initiatives remain successful, continue

to deliver overall return on investment and gainacceptance across the enterprise.

Sustainable Data Governance

Whats the secret to creating a data governance

program that stands the test of time? Dont be so

focused on the here and now. Anticipate change andbuild flexibility into your data governance program

from day one. This white paper exp lores what it

means to be sustainable, the ecosystem for data

governance, sustainable data governance practices

and more.

WATCH NOW

WHAT IS DATA GOVERNANCE?


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Data Quality

DATA QUALITY

Understanding Big Data Quality for

Maximum Information Usability

Its easy to get caught up i n the idea of big data. Youve got massive amounts

of data streaming into your business and it has lots of potential. How can you

apply analytics to reveal key business insights? What can you learn about your

customers and products? How soon can you get started? But wait . There are

things you need to do first reliable data management practices you need to put

in place before fully taking advantage of big data. This paper explains why data

quality and data governance are so important to large-scale analytics. It will helpyou learn how to balance governance with usability so you can come up with a

strategic plan for managing big data, and it includes a checklist of things to look for

when evaluating data management tools for big data.

Building a Data Quality Scorecard for

Operational Data Governance

In this paper, we look at taking the concepts of da ta governance into general

practice as a byproduct of the processes of inspecting and managing data quality

control. By considering how the business is affected by poor data quality and

establishing measurable metrics that correlate data quality to business goals

organizational data quality can be quantified and reported within the context of a

scorecard that describes the level of trustworthiness of enterprise data.


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Data Integration

DATA INTEGRATION

The New Data Integration Landscape

For years, the key to success for any business solution has been data. Selecting

the right tool to bring data from disparate sources and transform it before loading

to a target destination was critical. Many organizations struggled with the process,

adding new tools as limitations in chosen tools became obvious. Departments

often operated in silos when selecting tools, which, coupled with mergers and

acquisitions, resulted in ETL tools that werent integrated. In addition to increases

in maintenance and training costs, using different tools can lead to fragmented

metadata, which turns compliance into a huge chore. It is time to move away from

an ad hoc approach and look for a comprehensive solution that can execute a

variety of data integration programs, including data cleansing and enrichment,

ETL and ELT, data synchronization, migrati on, and master data management. This

paper provides an overview of requisite data integration capabilities and explains

why they are important.

Data Evolution

Most organizations have spent the last decade acquiring data integrati on tools

from different sources to manage, govern and utilize data. This generally means

they now possess a nonintegrated toolbox of technologies. Organizations needa solution that enables employees to focus on managing data better instead of

integrating disparate technologies. A comprehensive data management platform

would address all aspects of data integration, data quality and master data

management, be underpinned by adapters and a federation capability, and share

technical and business metadata. Ultimately, a single user interface should surface

all of the data management capabilities. This white paper describes the evolution of

data integration tools and the benefits that can be achieved with a comprehensive

data management platform.


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Data FederationAn important piece of your data management strategy

By Lane Whatley

Data comes in all shapes and sizes and from various sources and systems. We also know that companies that

efficiently manage data have a distinct advantage in the market: clean, quality data yields better business process and

precise analytics, which drives better, faster decision making.

So, how do they do it? How do companies manage the deluge of data?

There are many processes in place that he lp a company manage data, but

one foundational piece to the data management puzzle is a process calleddata federation.

What is it and why should you care?

Data federation is a process that joins data from heterogeneous data

sources into a single, combined unit. Once federated, the data is presented

in a consistent format.

Take a retail chain, for example. They have transactions from online

purchases and transactions from bri ck-and-mortar stores. Let s say at

the end of the year they want to create a single report of customers that

made online and in-store purchases. To get this type of report, they need to

integrate the disparate data systems.

Traditionally, the chain might create a repor t using a th ird-party operational

data store (ODS) and ETL processes. But, if the chain uses a federation

server instead, they can virtually marry the transactions and create a single

report that shows a 360-degree view of their customers.

By accessing data virtually, the chain doesnt have to move da ta, replicate it

or retrieve it from tables to perform analysis. This virtualized environment, or

layer, has several key advantages:

Data abstraction: The source details and format differences between

the various data sources that make up a virtualized view of data are

masked from the requesting application. As a result, the sources of

the data that make up the view can be changed without the need for

changes to any application that accesses the view.

Data security: Fine-grain security controls allow customers to assign

role-based access, ensuring sensitive information is protected. By

eliminating the need to move the data from its source systems, the

chances that unsecured copies of replicated information will be

accessed by unapproved users is also eliminated.

Data caching: The results of a virtualized view can be cached for

faster, more efficient access. Servicing requests for BI or analytic

access can severely impact the performance of the operational

systems generating the information. By providing access to a cache

of the result instead, operational systems per form more efficiently and

users can access their information more quickly.

DATA FEDERATIONINTRODUCTION MDM DATA GOVERNANCE DATA INTEGRATION ABOUT USDATA FEDERATIONDATA QUALITY


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Data federation in todays organizations

Data abstraction

Problem:A financial services company has more than 100 analysts that

need to access a particular instance of a database that contains data for

analysis. Due to a change in regulations, the source of the information

needs to be changed to a new instance of the database. How can the

queries used by all of these analysts be quickly and easily changed without

disrupting their work?

Solution:By having these analysts all access a materialized view that

relates to the information needed, the back-end source of the view can be

changed without the analysts needing to do anything different.

Data security

Problem:A pharmaceutical company has data spread across several

systems that needs to be combined to deliver a report on drug trials.

Combining the information into a data mart or data warehouse can result

in the exposure of patient information and the potential for a data breach

and regulatory fines. How can this information be accessed in a securemanner without replicating it?

Solution:A materialized view can be queried where the result is a blend of

the information needed from the various source systems. The information

is only joined at the time a request is made, so no data is replicated onto

another system like a mart or warehouse. Additionally, access to the view

itself or to certain information within the view can be restricted to specific

users so that only those users with permissions to access the blended

information can get to it.

Data caching

Problem:A manufacturing company runs its business through three key

operational systems. To track progress on key performance indicators,

multiple data analysts must run analytic models on the data contained in

these systems, but doing so slows down the systems and affects the abili ty

of the manufacturer to get products out the door. How can the models

be run without affecting the operational systems performance and thus

the business?

Solution:Materialized views can be created that access the information

needed for the models. The results of these views can be cached to a

non-operational system. The cache can be refreshed to balance the need

for up-to-date information with the need to minimize the number of times

queries are run against the operational systems. Incoming queries for the

data for the analytical models will be rerouted to the cached result, rather

than to the operational system, resulting in faster model performance and

no impact to the business.

Data FederationAn important piece of your data management strategy (CONTINUED)



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Data Virtualization: Flexible Technology

for Agile Enterprise

Today many business users are spending increasing

amounts of time trying to find and integrate the relevant

pieces of data needed to perform specific tasks and

produce insights. One reason for this is that data has

become distributed across many different data stores,

making it difficult to access integrated, relevant data for

cross-functional use. Data virtualization technology hasemerged to simplify data access. As this paper by Mike

Ferguson of Intelligent Business Strategies explains,

data virtualization software gives the impression that

data is integrated and sto red in a database even

though it is not. Using virtualization software, it is

possible to create multiple virtual views of data and

present data as if it is integrated. Read this paper to

learn how virtualization works and how it can impact

your organization, find out the requirements for using

the technology and see how SAS delivers products

that can help you incorporate virtualization into yourtechnology mix.

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DATA VIRTUALIZATION:

WHAT SHOULD YOU CONSIDER?


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About Us

SAS understands that data drives everything. We want to help you make sure its right. Is your data

easy to access, clean, integrate and store? Do you know which types of data are used by everyone in

the organization? And do you have a system in place for analyzing data as it flows in? Spend less time

maintaining your information and more time running your business with SASData Management. Its

an industry-leading solution built on unified platform and designed with IT and business collaboration

in mind. Its also the fastest, easiest and most comprehensive way to get data under control, with

in-memory and in-database performance improvements helping to deliver trusted information. Whenit comes to master data management, data integration, data qualit y, data governance and data

federation, SAScan help you transform big data into big opportunities.

Learn more and discover our free white papers, webinars and videos: sas.com/data.

SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA

and other countries. indicates USA registration. Other brand and product names are trademarks of their respective companies.Copyright 2014, SAS Institute Inc. All rights reserved. 107331_S130168.0914


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