1. Nationwide IT Enabling Self-Service Analytics
9/10/2018
Nationwide IT Enabling Self-Service Analytics
Jason Harmer
16 May 2017

2. Agenda Introduction Use case: IT enabling Commercial and Agribusiness
9/10/2018
Agenda
Introduction
Use case: IT enabling Commercial and Agribusiness
Using Self Service Visual Analytics to drive the business of IT
Lessons Learned & Successes
Welcome – intro and agenda overview

3. IT Enabling, not Preventing
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IT Enabling, not Preventing

4. What is Self Service Analytics?
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What is Self Service Analytics?
A form of business intelligence (BI) in which line-of-business professionals are enabled and encouraged to perform queries and generate reports on their own, with nominal IT support. Self-service analytics is often characterized by simple-to-use BI tools with basic analytic capabilities and an underlying data model that has been simplified or scaled down for ease of understanding and straightforward data access.
First, let’s take a step back and define self service analytics

5. IT / Business Relationship - Historical
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IT / Business Relationship - Historical
Request for Report
Business IT
Business:
Receiving old data
New questions arise
Query incorrect
 non-value add reports
Historical – endless loop
Another option – business does their own reporting in old, traditional BI tools that require a lot of technical knowledge. What happens when the one guy or gal is OOO? Report is not updateable.
IT:
Report writers?
Understand business needs?
Governing data + reporting
Report created / delivered

6. IT / Business Relationship – Current
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IT / Business Relationship – Current
Business IT
Supplies governed data sources
IT:
Governs the data
Creates vetted data sources (one source of the truth)
Provides flexibility and agility
Empowers business
Business:
Receives vetted data sources
Customized reporting
Business-driven insights
 Better informed data-driven decisions
IT changes from producer to enabler.
Cannot cut out IT completely – volume/variety of data is too large for Business on their own to solve. IT will govern and secure the data. One source of the truth.

7. Gartner Magic Quadrant
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Gartner Magic Quadrant
By 2020….
90% of modern BI platforms will feature natural language generation/AI
50% of analytic queries will be generated using search or natural language processing
2x business value for self-service organizations
So we looked at historical and current state, but what about he future for BI?
The next 3-4 years – transitioning from traditional BI tools (Business Objects, Excel) to modern BI.
This will allow for natural language processing, AI, voice querying . What does this mean? We don’t need IT nerds to build reports or to gain insights – let the business do it. Gaining/increasing value. Faster decisions.

8. IT – Producer to Enabler
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IT – Producer to Enabler
For decades, IT departments remained mired in the endless churn of building reports to support data requests from the business. Now, it’s finally IT’s time to break the cycle and evolve from producer to enabler. IT is at the helm of the transformation to self-service analytics at scale. In high-performing organizations, analytics teams are “working as a trusted partner with the business,” according to Gartner. IT is providing the flexibility and agility the business needs to innovate all while balancing governance, data security, and compliance. And by empowering the organization to make data-driven decisions at the speed of business, IT will emerge as the data hero who helps shape the future of the business.

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Self Service Visual Analytics with Modern BI: Driving the business of IT
Next, we’ll talk about another use case, how IT uses self service analytics to help run the business. These reports used to be built by another IT team, but it was a painful process, similar to what I described earlier. Painful to update or make changes, sometimes they wouldn’t even pursue it. Now with Tableau, we’re able to not only have self service analytics but self service visual analytics to make consuming the data to make decisions quicker and easier.

10. 3 Reasons to Use Visual Analytics
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3 Reasons to Use Visual Analytics
Quick
Tableau enables me to be
Quick – fast to connect, fast to publish reports. No programming required. Easy drag and drop functionality to turn data into action fast. Global filters. Dashboard actions. Easy, clickable process.
Average lines? In Excel, we’re creating new rows of data with average formulas, then adding them back into the graph. Then the line doesn’t appear in the right spot or it’s not the right color. IN Tableau, it’s literally 3-4 clicks. Done.

11. 3 Reasons to Use Visual Analytics
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3 Reasons to Use Visual Analytics
Scalable
Who is familiar with this lovely error?
Scalable – it can connect to all kinds of data, compress the data so it’s easier to handle. Excel? Crossing fingers, hoping it won’t crash.
Old Time Series data – everyone loves 13 months of historical data….excel file was over 100 MB. Pivot tables, charts….refreshing any was nerve-racking. That same data in tableau – file size less than 20 MB, extracted. No worires about updating data, graphs. Go from business unit to enterprise level data. Increase your scope.

12. 3 Reasons to Use Visual Analytics
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3 Reasons to Use Visual Analytics
Visual
Tableau enables me to be
Visual – at it’s core, tableau is a data viz solution.
People forget the importance of this feature
getting away from data tables with numbers and color highlights. Quick and easy to make decisions based on good data visualization.
Childrens books – still can tell a story
Capacity and demand is huge in IT portfolio. We want to make sure we identify the load for each of our dev lines to see if they can take on more work as it’s identified on the business side. So we can go from

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This, to

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Lift and shift approach

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Now we have glanceable, simple to read data that we can easily make a decision and take action within seconds, not having to worry about reading every single line and number.
Define capacity and demand

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Self Service Analytics for the IT Enterprise
One reporting portal
Governed data sources
Come to the reports on demand – updated daily

17. Self Service Analytics – Successes and Lessons Learned
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Self Service Analytics – Successes and Lessons Learned

18. Better metrics  better decisions More agile + customizability
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Success!
Better metrics  better decisions
More agile + customizability
Speed to market
Some results I’ve personally had using Tableau after 1 year in my current business unit.

19. Self-Service  More users  Need for reporting governance
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Oops!
Self-Service  More users  Need for reporting governance
Identify reporting framework early
Dashboard design acumen
Using IT as a training tool
Continued partnership with Tableau. Expect to see another increase of licensed users this year. In Des Moines alone we have new users to our monthly meetings each month.
Marketing
Makes sense- insurance companies want data on customers, they understand the need to data mining / data viz

20. Closing Remarks harmerj@nationwide.com 9/10/2018
Using Tableau – more data savvy – what else can we do?
Data governance
Data strategy
Consistency, one version of the truth
Improved ETL processes
Breaking reporting siloes