Presented by:

Ashley OhmannData Visualization Practice Leader, Kforce Advisory and Solutions Practice

Enterprise Data Governance with Tableau

• This presentation discusses– What Data Governance is– Why it’s important to your organization– The Goals of Data Governance– How it interacts with your Tableau plans– How good Data Governance practices will impact the user experience

and make your business and analytics projects more successful

Objectives

What is Data Governance?

“Data Governance is a set of processes that ensures that important data assets are formally managed throughout the enterprise.”

- Wikipedia

“Data Governance is an emerging discipline with an evolving definition.”

And then, the definition talks about trust, accountability, evolution, and people.

- Wikipedia also says,

“…the specification of decision rights and an accountability framework to encourage desirable behavior in the valuation, creation, storage, use, archival and deletion of data and information. It includes the processes, roles, standards and metrics that ensure the effective and efficient use of data and information in enabling an organization to achieve its goals.”

- Oracle’s definition of Data Governance

Your data is your asset.

If you are providing a service—whether it’s financial services or healthcare, or retail—your data defines and quantifies the relationships between your clients and your products.

Data Governance strategies do not have to answer every question within your organization—you can get as much impact from many smaller, project-based strategies as you can from an enterprise-wide strategy.

What does this mean?

Data Governance is the determination of which data assets are important to your business.

It also is the selection of tools that will help you make the best use of your data—because it’s your asset, and this is where Tableau comes into the discussion.

Data stewardship is also important: it is the process in which we define individual data elements, map their usages, and then define how they will be used and transformed, so that something like Profit means the same thing in one system that it does in another.

What does DG look like to me?

• Not only are all of the transactions and interactions between your products and customers quantified, but the tools and formats used to capture them are different.

• Human beings generate most of the data that we’re now storing, and most of it is unstructured.

• The way that you store, analyze, and share your data determines your abilities to understand your business

• Traditionally, information management has been reactive, rather than proactive. The purpose of Data Governance is to reverse that paradigm.

Everything is quantified.

Why is DG Important?

•Data Governance goals include•Standards—which data elements are important and why•Processes—how are data elements loaded and transformed?•Compliance—Sarbanes-Oxley, BaseI I and Basel II, HIPAA, to

name a few•Security—keeping the data safe without unnecessary restrictions

on use•Rights—who need to see what to do their job•Metrics—measure the performance of your enterprise

•Data Governance is not tactics, like ETL, data cleansing, architecture, or master data management

•Determining what’s important enables the development of stakeholders, metrics, timelines, and communication strategies

Goals of Data Governance

More accuracy and better decisions with less waste

• My job was to report on employee performance for our four different business lines.– We downloaded data from SAP. It was painful. And only quarterly.– These analytics are important because they determine headcount.

• Our IT group attempted to build an analytics application that we scoped very carefully, but in the end, their project failed: – Bad database design– Employed existing, flawed techniques to build a tool for the future– No accountability

My Tableau Story Starts with DGOur data was a mess.All great projects start somewhere!

• Failure actually is a great opportunity to reassess.• We determined what did not work. And that was painful.

And expensive!• The root cause had nothing to do with the technology.

– It was a leadership issue. – Our business executives and our IT executives had not agreed on

how to use, protect, and analyze our data.• We actually didn’t need IT.• We needed ownership and accountability.• And good change management.

We took a long, hard look.

What did we do first?

• “Managerial courage”• We identified stakeholders around our business.

– Then, we spent days on the phone and in person deciding what aspects of our business performance were important to measure

• Once we knew what was important to measure, we decided how to measure, and when.

• We also determined how and when to store our data.• We assigned responsibility and communication plans. These

were critical.• We determined which tools we needed.

Data Governance and analytics are about people.The tools are secondary to the people who will use them to make decisions.

How Did We Fix the Problems?

• One of the challenges of Data Governance is to select tools that will help enable the growth of your organization.

• The tools that you use—and the analytics that you build—should be extensions of your strategy.

• We decided to build a new EDW, because we had serious issues with – Data quality– Data storage– Accuracy

• We selected SQL Server; we already had the licenses, and we had the knowledge that we needed to build the processes to load and transform data from its nearest sources.

Tools should be enablers, not barriers.

The tools followed the people.

• Four business lines and 45 metrics is a lot. We needed– One set of metrics for everyone– A consistent presentation layer for executives– The same level of visibility into detail for our regions

• We wanted to make sure that the 2,000 engineers in our organization—the people who were interacting with—knew on what they were being measured, how, why, and where to get the data to help their peers improve.

• With our EDW, we build an analytics tool with a high level of trust and enabled the people running our business to make decisions and plan for the future.

• Our data governance committee met twice a month, and we discussed on issues, resolutions, and trends.

Tableau was the icing on the cake.

What we did with Tableau

• Trust is one of the most important products of a good Data Governance strategy.

• If people do not trust data, they will not use it.• It doesn’t make sense to spend millions of dollars storing

and aggregating data that people do not think is correct.• We used Tableau as an extension of our Data

Governance strategy by including several metadata elements in our tooltips, which we sources from a dimension of metrics:– Metric definition, both in English and math– Data element sources– Metric thresholds– Data latency

Looks aren’t everything.

Goal: Building Trust

• If you’re building new analytics, you can use Tableau to control the level of detail of granular data that users can see.– This can help them understand your data and even help test it—

and realistically, most applications have some errors when they go into production

– With Tableau and an agile EDW, it’s often easy—and inexpensive—to correct errors. Everyone here has made mistakes--the biggest problem with making a mistake is denying that you made it.

• Transparency is a big part of the change management of a new tool, too. It builds trust and speeds adoption.

Transparency is important.

Goal: Communicate Validity

• There are many people in your organization reporting on the same data. And their analyses all look different.

• Shared data sources are wonderful: if you’re running Tableau Server, find out the highest priority and most used data sources, and create a shared data source that refreshes frequently.

• We used Tableau Server (and our EDW) as an access point for data for other areas of our business.

• Certify your data sources for accuracy.

Democratization of data

Goal: Enable and Control Access

• We solved one big hurdle of analytics—naming conventions—with our EDW.

• What you call your data elements is very important, because people need to know what they’re looking at quickly.

• In our EDW, we added the datestamp that each record was loaded so that we could expose it to our users

• This also fed back into our data quality metrics: we sometimes found out that we had not received a daily load from an upstream source when end-users saw the age.

“Design is empathy.” – Brian Chesky

Goal: Manage Metadata

• People need relevant information to make decisions—they don’t need all of the information.

• Determining access privileges and needs is a bit part of Data Governance strategies.

• Overloading people with information is the same as disabling them.• Tableau Server’s groups are excellent ways to manage who can see

what data, as are row-level permissions

Not all information is useful information.

Goal: Provision Rights

• Most business now are regulated in some way. • The challenge with analytics is making sure that people who should

not get data do not need data.• Considerations for need and legal compliance should be top of mind

when designing new dashboards.• Tableau Server has excellent features for securing data, from row-

level security (which can be applied offline within Reader!) to the ability to hide granular data from end-users.

• The challenge is giving people enough data to do their jobs, but not so much data that you get in trouble.

• Determining how to maintain compliance with government regulations is expensive, but the opportunity cost of failure is even higher.

Lawsuits generally are expensive.

Goal: Maintain Compliance

• Our dashboards served hundreds of users. The 150 key users spend about three hours less a week analyzing their performance than they had before. Good Data Governance strategies caused this.

• You can’t afford not to include your Tableau users in your DG strategies, or to plan for it. The opportunity cost is failure.

• Implementing DG with Tableau, particularly Tableau Server, is very easy, and it has a big payoff, both in terms of level of effort and burden on your infrastructure.

• The most important factors in analytics are relevance, accuracy, and consistency

• Our biggest accomplishment: an informed, strong, vocal, influential group of users.

DG and Tableau = Better, Trusted, Powerful Visualizations

What was the Outcome?

THE DATA GOVERNANCE LIFECYCLEIdentification of Problem

Assessment of the Cause

Identification of Stakeholders

Congregation of Stakeholders and Delegates

Determination of Metrics/Data Elements and Priorities

Development of Processes

Designation of Toolsets

Development of Reporting Criteria

Development of Training Curricula

Frequent Communications!

Thank You

Follow me on twitter @ashleyswainCheck out my blog! www.dataviz.ninjaor email aohmann@kforce.com

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