1. Creating Analytic Teams
Julie Hyman

2. The drive toward self-service
So with so much data out there and so many business folks wanting to use it, we have come a choice.
Do we want to wait in long lines and pay the high price for full service – where IT completely regulates and runs all data access and availability…
or as business users are we willing to put in a little more effort and get our hands a little more dirty to get what we want, when we want it for a much lower overall cost?

3. Successful self-service is planned self-service
But like many things in life, with great power comes great responsibility.
And so if business is going to actively participate in developing good BI (and not just passively consuming it) they need to share the responsibility behind good data governance and not live up the bad reputation that IT has been secretly suspecting of them all along.
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4. There are a lot of aspects consider when you want to build a successful self-service environment and specifically when you want to have the flexibility of self-service but the accountability of a regulated system.
But the one were to focus on today the one aspect to a successful self-service system is teamwork so it's talk about how teamwork is an important aspect of a self-service environment

5. Share High Level Environment Info
How to connect to various data systems
How to relate tables together
Basic Query templates
One of the simplest but most powerful ways that your self-service environment can benefit from teamwork is just a general knowledge sharing.
In a lot of the data environments that we see – that our customers are working work – data analysts are querying from data systems that are not well documented.
Understanding where data is, how to connect to it, how to relate different data elements together, is very often something that the analyst learns over time through trial and error.
A successful self-service system will have some mechanism where the users of that system have a good starting point of knowledge that can be easily built upon over time

6. Standardize Your Granular Data Solutions
How do we calculate certain data elements
How do we remove duplicates
How do we group and categorize data
One of the simplest but most powerful ways that your self-service environment can benefit from teamwork is just a general knowledge sharing.
In a lot of the data environments that we see – that our customers are working work – data analysts are querying from data systems that are not well documented.
Understanding where data is, how to connect to it, how to relate different data elements together, is very often something that the analyst learns over time through trial and error.
A successful self-service system will have some mechanism where the users of that system have a good starting point of knowledge that can be easily built upon over time

7. Define Common Datasets
Define common data elements
Create an ‘analyst-curated’ data layer
One of the simplest but most powerful ways that your self-service environment can benefit from teamwork is just a general knowledge sharing.
In a lot of the data environments that we see – that our customers are working work – data analysts are querying from data systems that are not well documented.
Understanding where data is, how to connect to it, how to relate different data elements together, is very often something that the analyst learns over time through trial and error.
A successful self-service system will have some mechanism where the users of that system have a good starting point of knowledge that can be easily built upon over time

8. Centralize your workflows
Get your work off of your personal drives and into a common environment
Provides visibility
Shares the management responsibilities
One of the simplest but most powerful ways that your self-service environment can benefit from teamwork is just a general knowledge sharing.
In a lot of the data environments that we see – that our customers are working work – data analysts are querying from data systems that are not well documented.
Understanding where data is, how to connect to it, how to relate different data elements together, is very often something that the analyst learns over time through trial and error.
A successful self-service system will have some mechanism where the users of that system have a good starting point of knowledge that can be easily built upon over time

9. Who is on the Successful Analytic Team?
Business Experts – know the business and can identify problems to solve, hypotheses to test. Knows what information will make a difference.
Data Analysts– knows the data.. How to get to it, how to successfully combine it and prep it for a solid data foundation
Data Scientists – can take your analysis into new and exciting areas. Can help you move from traditional analysis (what have our customers been doing, etc.) to ‘predictive analysis’ (what will our customers be doing)

10. So we defined 4 areas we want to promote teamwork:
Sharing of high level information
Sharing of granular solutions
Defining common datasets
Sharing the management of our workflows (centralized workflows)
So how do we foster teamwork in these areas?

11. Teamwork comes from good habits and good tools
Its really a marriage of two areas – the right habits and the right tools to support our habits

12. Good habits
You need to clearly define what you are trying to achieve and get buy-in
You need regular communication channel
You need to be willing to adopt what’s working and iterate on what is not working … until it works

13. How can our tool support our habits?
Should be a single tool
Switching tools wastes time and makes it hard to share/intermingle work and data
Connectivity
Needs to be able to easily connect to any and all data sources. RDBMS, NoSQL, spreadsheets, BI sources, etc.
Should have an centralized collaboration layer
Allows for sharing and collaboration – from high level to granular
Allows for curating datasets
Does not require large IT time investment (easy for the business to manage)
Should be quick to setup and get running and affordable

14. Toad Data Point + Toad Intelligence Central

15. Resources
Toad Data Point & Toad Intelligence Central
Free 30-day trial available on or

16. Questions or Feedback? Julie Hyman – Product Manager