Talend WhitePaper - Dashboards Data Governance

8/9/2019 Talend WhitePaper - Dashboards Data Governance

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Data Quality Dashboards

in Support of Data Governance

White Paper


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alend White Paper Data Quality Dashboards in Support of Data Governance

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Table of contents

New Data Management Trends .......................................................... 3

Data Quality Dashboards .................................................................. 3

Understanding Important Metrics ....................................................... 4

Take a Baseline and Monitor ............................................................................... 5

Build a Business-focused Score Card .............................................................. 5

Monitor Metrics over Time ................................................................................... 6

Market the Importance of Data Quality .......................................................... 7

Tools to communicate ........................................................................................... 7

Data Quality Dashboards ..................................................................................... 8

Talend Data Quality ......................................................................... 8

Data Profiling ............................................................................................................ 9

Reporting .................................................................................................................... 9

Data Quality Portal ................................................................................................. 9

Features .................................................................................................................... 10

Cleansing and Standardization ........................................................................ 10

Unified Data Management Platform ....................................................11


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New Data Management Trends

In past years, you may have sought data quality tools in reaction to

problems stemming from data failures. Your projects might have

included a desire to improve revenue, mitigating a loss in productivity,

or even ensuring the company was in compliance with industry laws and

regulations. Even something as simple loading a target data source may

have sent you off to purchase technical solutions to address the data

quality problem or risk project failure. The responsibility was on IT to

solve the problem and make the target application run right.

Today, a newer trend is emerging when it comes to data management.

More common today is a data governance approach where time and

energy are spent on fixing the processes that allow flawed data to be

introduced into an environment. In this approach, you rather seek to

isolate the cause and eliminate the source of the introduction of flawed

data. In companies where internal and external data is constantly being

merged to form operational data stores and data warehouses, it makes

more sense to not only address data failures, but to proactively monitor

data and report the current state of data quality back to the origin of

the data. This new data governance approach works on cross-functional

problem solving and process improvement, not relegation of data

management to IT.


To this end, the emerging technology of data quality dashboards are

becoming more and more important. Organizations pull together data

governance strategies and these tools to:

Understand what metrics are important to the organization

Take a baseline of DQ metrics

Monitor metrics over time

Report the metrics so that cross-functional teams can

understand the impact


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Measure the impact and market the importance of data

management

You’ll notice that the list did not include fixing data quality issues with

tools, although this often remains a necessity. The emphasis in this

approach is on modifying people’s attitude and improving processes.

The strategy is more about setting up data quality policies and setting

up control processes based on data quality rules. The tactics are to

automate monitoring and reporting of data quality metrics and

communicate knowledge about the value of the data. The tactics

include empowering business users with the ability to determine how

best the data can be used to meet their own business needs.

Understanding Important Metrics

The first step in employing such a strategy is to understand key quality

indicators (KQI) for your organization. Since every organization has

unique data management challenges, you will have to reach out to your

co-workers here and communicate. Communication is key to your

success in data governance. If you were to sit down and work for a day

in the billing center, call center or purchasing agent job, for example,

people there will see that you understand them and care about their

processes. The first, most important task you can do is to understand

critical business processes in your organization.

This will begin to develop your story about “return on investment” for

justifying the funding of an improvement project, necessary data

quality tools, master data management strategies and more.

The most important metrics could revolve around any of the following

key components:

increasing revenue

lowering costs

reducing risks (compliance)


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avoiding data disasters

competing effectively in the market

meeting the organization’s objectives.

Don’t be afraid to ask questions, take notes and understand the data

management challenges of your business associates.

Take a Baseline and Monitor

Having gathered potential data- quality metrics and business impact in

the previous step, next it is time to take a baseline measurement. As

part of the source system analysis, a baseline of each source system

should be captured and stored as well as how multiple systems conform

to expected metrics or business rules. In some cases, it will make sense

to look not only at each source system in isolation, but across systems.

Employ advanced profiling for comprehensive column and attribute

analysis. Identify potential problems within structured data fields such

as dates, postal codes, product codes, customer codes, addresses or any

attributes that should conform to a particular format and structure.

Configure custom data- quality rules, and flag any attributes that do not

conform.

When you’re done with the analysis, you will have a very good idea of

the challenges you face in integrating data and the information

necessary to develop designs that address the challenges proactively.

Build a Business-focused Score Card

It’s important to aggregate the information you get from a data

profiling tool into business metrics. A simple non-aggregate score is

usually rather meaningless. If we show that a certain percentage of all

fields are nulls, for example, the number is of no use – there is no

context. You can’t say whether it is good or bad, and you can’t make


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any decisions based on this information. Practitioners aggregate those

scores into business value metrics. The technical metrics combined with

the business pain observed allow business users and technical users to

collaborate to govern data.

Most data profiling tools and scorecards give you very technical metrics

by default. More and more data profiling tools being developed today

allow you to aggregate the technical scores into business metrics, but

since a tool can’t natively and unthinkingly know your business, it’s up

to you and the business stakeholders in the room to give it meaning.

A common example of this might be to create an ability to contact

score. Evaluate the completeness and validity of e-mail, name, address,

and telephone number. Then create some simple logic around it that

fits your business. It may be that having a name and address is enough

to contact your customer. If an address is invalid, then both phone and

e-mail are required by your data governance committee. Those records

that have invalid addresses, and either an invalid e-mail or phone

cannot be contacted by data governance rule. This “ability to contact”

metric is easier to follow for your team.

Monitor Metrics over Time

Data may be coming into your databases via a call center or data entry.

It may be coming into a centralized location from data feed or third-

party vendors. Data quality dashboards allow you to keep an eye on

data anomalies by constantly checking if the data meets business

specifications. Dashboards offer attractive charts and graphs on the

status of data compliance. It shows your key performance indicators and

trends conformity.

Today’s modern businesses use data quality dashboards to monitor and

report feeds from the company’s many locations. When you want to

ensure that client information was properly being entered into your

database, profiling it and comparing it to the baseline. With


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dashboards, your headquarters could report back to the individual data

stewards and notify them when they get the data right and when they

had allowed anomalies to get into the system. This feedback loop works

to continually improved data quality, train data stewards at new

locations and improve billing efficiency. Dashboards are also a great

way to make your people smarter about data governance; they will help

you achieve cleaner, more useful information.

Market the Importance of Data Quality

It’s unfortunate, but people have the tendency to think that

responsibility for information quality starts with someone else, not

themselves. In truth, we all know that information quality is theresponsibility of everyone in the organization, from the call center

operators to the sales force to IT and beyond.

Tools to communicate

Tools that supplement your in-person meetings can track progress,

promote the power of data governance, help you take on difficult

challenges and keep a record of successes. The good news is that there

are some fantastic software tools that can support communication.

Workflow - With workflow tools, teams can manage the

processes and coordination of the data governance team.

Wikis - In your data governance projects, you can use a Wiki to

document processes in a data governance projects, and have

an open online dialogue about risks and challenges in the

project.

Blogs - A blog allows for one person’s perspective on the data

governance project, but readers can leave comments and links

to their own blogs. Blogs can educate and inform data

governance groups, and they can use them to debate

unresolved issues or to continue discussions between

meetings.


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Knowing where your organization stands with regard to data quality is

essential for running an efficient and profitable business. Talend Data

Quality is designed to allow companies to share the vision of data

quality across the entire company. In addition to its profiling, cleansing,

standardization and matching functions, Talend Data Quality provides

customizable, web-based data quality reports and scorecards as well

the ability to export them in popular formats like Adobe Acrobat,

Microsoft Excel and XML. Business users and IT users can team up in

formulating processes that keeps data clean in the enterprise. It

extends the dynamic reporting capabilities of Talend Open Profiler to

help organizations monitor data quality metrics and support data

governance initiatives.

Talend Data QualityWith Talend Data Quality, organizations have a single, powerful solution

for reporting and monitoring the quality of their data across the

enterprise, allowing them to easily align the data quality facets of a

data governance program. This alignment improves processes and

communication on your valuable data assets. With Talend Data Quality


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data quality scorecards can be distributed to the data source owners to

track data quality improvements over time and drive compliance with

enterprise-wide data quality standards.

Data Profiling

Data profiling provides both an evaluation of your current state of data

quality and measurement of it over time. You can generate informative

reports and share key data quality metrics with your team – the first

step in solving data quality issues.

Reporting

Talend Data Quality includes report generation where data stewards can

leverage the data profiling results to create predefined reports that

watch for the violation of data quality thresholds. The reports can be

generated in Adobe Acrobat and other formats and communicated via e-

mail, wiki or intranet site.

Data Quality Portal

Talend Data Quality delivers customized, key quality indicators to a

web-based portal where teams can collaborate on the process of


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improving data quality across the enterprise. Data Quality Portal

provides an easy-to-use, browser-based view into key data quality

metrics. The portal opens up the issue of data quality to a broader

audience in your organization, fostering positive change around the way

your company manages data.

Features

• Create customizable web-based dashboards that display

critical information on data quality processes needed by

business users

• Provide personalized, default dashboards for different levels

within the organization that include alerts, data views, andlinks

•

Access control so that users and managers only see the

dashboards they need for their job

•

Alerts that update in real time so you’re always on top of data

quality issues

•

Track progress against realistic targets on an attribute-by-

attribute basis

•

Export reports in popular formats like PDF, XLS, XML andHTML.

Cleansing and Standardization

If you need it, Talend Data Quality has powerful tools for repairing and

cleansing data as well. Talend Data Quality allows you to use internal or

external reference data to set the standards for values, regular

expressions to set standards for data shape and size.

Set up cleansing processes using a wide range of dedicated data

integration and quality components. Nicknames or addresses are for

example easily normalized and standardized using the dedicated

components. These dedicated components, such as name & address

cleansing components, fuzzy deduplication components, third-party


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address validation & standardization services are natively available in

Talend Data Quality.

Unified Data Management Platform

Talend is an open source data management company offering complete,

end-to-end data management tools. Data Quality is the foundation for

building your complete data management strategy, but data

management is not limited to Data Quality. Data management requires

that you be able to access the data no matter what source system,

manage metadata and provide the transformations that come with Data

Integration. Data management is also about process change, the kinds

of changes you make as you move toward Master Data Management anda more mature data governance strategy. Talend tools provide an easy

path as your data management needs mature.

Documents

Talend WhitePaper - Dashboards Data Governance