1. IDC Center and DaSy Center
Effective Teams
April 4 &5, 2017
Haidee Bernstein
IDC Center and DaSy Center

2. What is a Data Team
Data Teams are groups of individuals dedicated to data inquiry and the outcomes it supports.
Data Teams
Address challenges
Field questions, and
Collect insights from the community .

3. Data Teams
Data teams promote collaboration around implementing data driven decisions.
I am going to start with the premise that data teams are a good way to promote collaboration around implementing data driven decisions

4. Why Focus on Data?
Data should play a critical role in your planning process
It allows you to know whether you are:
Building the right things
At the right time
In the right way!

5. NO ! HUH? Is Data the Cure-All?
Please use healthy skepticism when reviewing your data.
“Correlation does not mean causation”
HUH?

6. Does it correlate? Does the causation make sense?

7. It is never easy to get to the root cause of an issue!
In Layman’s Terms
It is never easy to get to the root cause of an issue!

8. TEAM Building an Effective
So if we agree that examining data is important when problem solving and teams are an effective source for reviewing data- then the next logical step is how to build an effective team.
Building an Effective
TEAM

9. Which Roles Need to be Involved?
PELICAN Manager
Quality Assurance Representative
Administrators
Parents/ Families
Providers
Service Coordinators
Data Manager
Who Else?
What about a statistician? anyone else? A consultant – either outside or someone from another district? Roles not separate people 9

10. A Few Guidelines for Creating a Great Team
Align your values
Identify your strengths
Define your purpose
Value your process
What else belongs here?
Align your values: What is your vision? Discuss differences in visions and try to find a common vision
Identify your strengths: Each person comes to the table with a different strength. Discuss them and how to use unique strengths.
Define your purpose: Why was this team formed? What do you hope to accomplish
Value your process: Establish meeting practices and rules at the very beginning. An example of a practice might be- take the first 5 minutes of every meeting to write down frustrations, celebrations, and aspirations. Then take a few minutes to share out.

11. Set Meaningful Goals You wouldn’t run a race without a route
So map a plan that incorporates:
Learning from failures
Learning from successes
Collaborating with other team members

12. Team Activity: Your Data Team
Who is on my team (people/roles)?
Who is missing from my team?
What values are important?
What is my purpose?
How can I utilize my Regional Leadership Meetings?
Who is on my dream team?
How can I achieve it?
Who can I talk to from this room?
Activity 10 minutes.
Action Plan
Any Ah Ha moments?

13. Your Data
Your Team

14. Using Data is an Iterative Process!
Premises
Data Use involves:
Working through a Collaborative Team approach.
Engaging Team in a Continuous Improvement Process.
Relating the Data to specific Problem/Issue.
Using Data is an Iterative Process!

15. Action Preparation Inquiry A Model for Data Use
1. Identify relevant data
2. Conduct data analysis to generate hypothesis
3. Test Hypothesis to determine root cause
4. Plan for Improvement
5. Evaluate Progress
Data Analytics
Preparation
Inquiry
Action
Consist of three phases w/ several steps:
Phase 1: Preparation
Identify relevant data
Phase 2: Inquiry
Conduct data analysis
Test hypothesis
Phase 3: Action
Plan for improvement
Evaluate progress 15

16. Infant & Toddler Connection of Virginia Data Analysis for System Improvement
Analytic Model
PREPARATION
Define the purpose and the people
Identify the people and the process
Identify relevant Data
INQUIRY
Analyze the data
Generate and Test Hypotheses
Determine Actionable Causes
ACTION
Develop and Implement Improvement Plans
Evaluate Progress –

17. The key to identifying relevant data is to ensure that you clearly define or select a specific problem or issue.
The key to identifying relevant data is to ensure that the problem or issue is a clearly defined. 17

18. The Problem/Issue Clear, measurable statement: For Example:
Region did not meet State Target for Indicator C- 5 (Percentage of children served birth to one). 18

19. What is Root Cause
A root cause is a factor that caused nonconformance and should be eliminated through process improvement.
Another name for specific problem is root cause

20. Why Determine Root Cause?
Helps
dissolve the problem.
Eliminates
patching.
Conserves
resources.
Facilitates
discussion.
Provides
rationale for strategy selection.
...and therefore enables the creation of effective action to prevent the problem from
re-occurring!

21. Standards and Principles for Using data to inform Your Root Cause Analysis
Use data on a regular basis
Use data for continuous improvement
Verify the accuracy of your data
Make sure you have the right team at each step
Own your data
Use a process to determine how much data is needed

22. Team Activity: Steps to Figure out Root Cause
What problem do I want to focus on?
What evidence identifies the problem?
What data do I have to determine the root cause?
What data will I need and where can I get it?
Who will be on my data team?
What types of analyses would be helpful (bonus)?
How will I know when the problem is solved/improved?
At your table discuss these questions. Report ah Ha moments.

23. Data Analysis
Use the best data available

24. Why Data Analysis? To identify strategies for continual improvement
To be responsive when issues arise
To avoid surprises during monitoring
Script Notes
Data analysis takes time and everyone in early intervention is busy.
It is important we pause a minute and think about why we should take the time to engage in data analysis?
As you see, it gives you the information you need to decide what steps are needed to keep moving forward and progress;
It allows you to respond when issues or problems arise; and
if used on an ongoing basis, it allows you to be continually aware of the status of your program. 24

25. Data Results Roll Uphill
Office of Management & Budget
Department of Education
Office of Special Education & Rehabilitative Services (OSERS)
Office of Special Education Programs (OSEP)
Tell the audience, “Here is the bottom line summary of what we have told you so far! Data rolls uphill! From all the way down at the vendor level to the regional center and then all the way up to the Office of Management & Budget.”
Ask if anybody knows who OMB is and what it does.
State
EI Infant Toddler/ Preschool Programs
Contractors/ Providers

26. Data Accountability Rolls Downhill
Office of Management & Budget
Department of Education
Office of Special Education & Rehabilitative Services (OSERS)
Office of Special Education Programs (OSEP)
Tell the audience, “Here is the bottom line summary of what we have told you so far since we first told you that “data rolls uphill”, all the way to OMB!” “Accountability for the data rolls downhill, all the way to the regional centers and the vendors.
In other words, it begins with you the vendors and Service Coordinators providing accurate and reliable data and ensuring that it gets into the system or on the records correctly.
State
EI Infant Toddler /Preschool Programs
Contractors/ Providers

27. Identify Relevant Data
The Purpose for Identifying Relevant data is:
To clarify and Illuminate the nature/scope of the issue
To gather evidence and answer “why” a problem or issue exists
To assist in designing, targeting and implementing improvement plans or corrective actions.

28. Sources of Data: PELICAN Risk and Reach Local data bases Family Survey
Other

29. Reliability Examples Data Accuracy
(1) The degree of consistency between two measures of the same thing
(2) How stable, dependable, trustworthy and consistent a test is in measuring the same thing each time
Examples
(1) You get on 3 different scales. Is your weight the same?
(2) You administer a developmental scale to the same infant 2 days in a row, is the score consistent?
Mehrens, W. A. & Lehmann, I. J. (1987). Using standardized tests in education. New York: Longman.
Worthen, B. R., Borg, W. R., and White, K. R. (1993). Measurement and evaluation in the school. NY: Longman. 29

30. Data Accuracy Validity Examples
(1) Does the test measure what it purports to measure?
(2) The degree to which the test accomplishes its purpose.
Examples
(1) Content Validity: Does the test cover all the necessary domains and not contain unnecessary questions?
(2) Predictive validity: How well will this test predict future performance?
Mehrens, W. A. & Lehmann, I. J. (1987). Using standardized tests in education. New York: Longman.
Worthen, B. R., Borg, W. R., and White, K. R. (1993). Measurement and evaluation in the school. NY: Longman. 30

31. Data Analysis Examples: Proactive & Reactive
Hold an annual staff data retreat to review locality data to select annual priorities for targeted improvement
A team of staff review child records to define areas needing improvement.
Proactive
A local team is convened to address family outcome data in relation to the state target.
Referral patterns from physicians are down. A local team is convened to analyze why.
Reactive
Script Notes
Take a minute and look at several examples that illustrate both proactive and reactive data analysis.

32. Data Analysis
What does the data mean?

33. The Data Analysis Process
Inquiry
Analyze the data
Generate and Test Hypotheses
Determine Actionable Cause(s)
Script Notes
The Inquiry phase takes you through the major analysis work including reviewing the data to develop hypotheses about the cause of current performance. This includes the gathering and consideration of more data to test these hypotheses that allows you to determine the actionable causes that need to be address for improvement. In other words – you will follow the data where it leads until you identify the major cause or causes of the current performance.

34. a mere assumption or educated guess.
A hypothesis is a proposition set forth as an explanation for the occurrence of a phenomena (problem) to guide investigation;
a mere assumption or educated guess.
Source: Dictionary.com 34

35. Guiding Questions Hypotheses Testing
What actions in our practice might have contributed to these results?
Has there been any information that would lead us to reject the stated hypotheses for our data patterns?
Given our data picture, are there any other possible explanations from our practice that we might pose?

36. Determine If Status Relates to:
Quality of your data
Infrastructure
Policies/procedures
Professional development
Daily Practice
Supervision
THIS DRIVES YOUR PLANNING

37. The Data Analysis Process
Action
Develop and Implement Improvement Plans
Evaluate Progress
Script Notes
The third and final phase is Action in which an improvement plan is developed. This includes designing strategies based on what the data tell you. The implementation plan must include an evaluation process so that you know if the strategies being used are actually working.

38. An Effective Improvement Plan Includes:
A logical link between root cause & improvement activities
Evidence-based practices
Identifying partners
Specific action steps
Personnel to develop, implement, monitor, & evaluate
Short term and long term outcomes
Data that will be collected and used to evaluate the outcome of the improvement activities
Specific timelines for each activity

39. S M A R T SPECIFIC MEASURABLE ATTAINABLE RELEVANT TIMELY
S.M.A.R.T. Goals
SPECIFIC S
MEASURABLE M
ATTAINABLE A
RELEVANT R
TIMELY T

40. Don’t Forget to Evaluate your progress
How can you incorporate this into your QEP Quality Enhancement Plan
How often do you need to evaluate?
How do you know that the planned activities are occurring?
How do you know if the short-term and long- term outcomes are being achieved?
Who is on your evaluation team?
10 mi
(document your findings)–Go back to your local issue. How will you answer these questions.

41. Things to Remember
Its all about improved quality of services to children and families
Hard to let go of traditional improvement planning
Hard to let go of your own sense of what the problem/solution is
Follow the data where it leads you
Ask the difficult questions
Create an environment where solutions are generated

42. Team Activity: Wrap Up
Thinking about what I have learned so far today:
What are my data teaming strengths?
What can I do to become a more effective team member?
What resources, tools or people will be helpful to me in the upcoming year?

43. “I’ll pause for a moment so you can let this information sink in.”

44. Additional Resources IDEA Data Center (IDC) www.IDEAdata.org
DaSy Center
Data Quality Campaign

45. The contents of this presentation were developed under a grant from the U.S. Department of Education, #H373Y However, the contents do not necessarily represent the policy of the Department of Education, and you should not assume endorsement by the Federal Government. Project Officers: Richelle Davis and Meredith Miceli