1. Meta Data and Group Decision-Making
Stephen Penn, DM, PMP
Associate Professor
Analytics Program
March 10, 2017

2. Abstract
A survey of 27 questions collected information on group decision- making in regards to organizational characteristics, technology use, social interaction, management involvement, and decision quality. Simple linear regression shows that one technology area, Meta Data Management (MDM) has a linear relationship with social aspects of decision-making, which influences how well group decisions align with organizational goals. This research suggests that when investing in an analytical platform investment in a Meta Data repository should also be considered.

3. Research Question
If an information system is available, what feature or technique helps a team use that system to make informed decisions that align with organizational goals?

4. Method Data Collection – a 27-item questionnaire
Participants – 234 working adults attending professional gatherings, either professional networking opportunities or educational events
Measures – Likert scale, ordinal data, scaled from 0 to 5
Analytical Techniques – Linear regression, boxplots, correlation matrix, cluster analysis

5. Survey Items Used in this Study *Technological*
Item Seven (Q07_DW) asked the respondent to rate the company’s capability in Data Warehousing
Item Eight (Q08_META) asked the respondent to rate the company’s capability in Meta Data Management
Item Nine (Q09_OLAP) asked the respondent to rate the company’s capability in On-Line Analytical Processing
Item Ten (Q10_DASHBRD) asked the respondent to rate the company’s capability in dashboards
Item Eleven (Q11_DTMNG) asked the respondent to rate the company’s capability in data mining

6. Survey Items Used in this Study *Social*
Item Twelve (Q12_CONTRIBUTE) asked about decisions made at the most appropriate management layer
Item Thirteen (Q13_STARTING) asked the ability to start discussions that lead to decisions
Item Fifteen (Q15_PARTICIPATE) asked about the ability to get more people involved in decision making
Item Sixteen (Q16_INVITE) asked about the frequency of having more than one group invited to decision making meetings
Item Seventeen (Q17_CONCERNS) asked how well responsibilities and concerns are defined before decision making

7. Survey Items Used in this Study *Decisions*
Item Twenty-six (Q26_AGRMNT) asked the respondent to rate how well the team’s decisions align with organizational goals.

8. Exploratory Analysis of the Data
The relationship between a company's technological capabilities (data warehousing, OLAP, dashboards, and data mining tools) and the company's decision quality is not a positive linear relationship. When technology is rated high, the relationship to decision quality is linear. However, when the technology capabilities are low, sometimes the decision quality is also judged to be high.

9. Potential Reasons
Several reasons why technology is a poor predictor of decision alignment with organizational goals can easily be postulated. Here are a few...
The company is small enough that there is sufficient social interaction so that technology isn’t needed
The Top Management Team (TMT) involvement is sufficient so that technology doesn't have as great an impact
The quality of the information stored in the data warehouse is so bad that the information provided is misleading and the people think they are making good decisions
Corporate goals are stated poorly and many employees realize their misunderstanding of them.
Could be that anecdotal evidence and gut instinct are good enough for the context (environment and expert personnel)
Could be something else?

10. Graphical Analysis
The following 5 slides are boxplots of each of the technology survey items (Q07_DW through Q11_DTMNG) to decision agreement question (Q26_AGRMNT).
The boxplots of Q07_DW, Q09_OLAP, Q10_DASHBRD, and Q11_DTMNG show erratic relationships.
Q08_META shows a consistent linear relationship.
Why is Q08_META different? If Q08_META is consistent, then is it a critical success factor?

11. Data Warehouse vs. Decision Alignment

12. Meta Data vs. Decision Alignment

13. OLAP Tools vs. Decision Alignment

14. Dashboards vs. Decision Alignment

15. Data Mining vs. Decision Alignment

16. Correlation Analysis
Q07_DW
Q08_META
Q09_OLAP
Q10_DASHBRD
Q11_DTMNG
Q26_AGRMNT
1.00
0.73
0.63
0.54
0.22
0.61
0.59
0.55
0.36
0.62
0.21
0.71
0.20
0.18
Between the five technology capabilities, Q08_META correlates most with Q26_AGRMNT (a value of .36)
The next highest is Q07_DW with a value of .22.
What could be happening here is that the five technological capabilities are having differing levels of influence on discussions surrounding team decision-making.

17. Regression Analysis
I created separate linear regression models to predict decision agreement (Q26_AGRMNT) based on the five technology questions.
Which of the five are the best predictors?

18. Five Regression Models
VARIABLE
INTERCEPT
COEFFICIENT
STD ERROR
T-VALUE
P-VALUE
F STAT
R-squared
Q07_DW
2.84
0.16
0.05
3.31
0.001
Q08_META
2.45
0.28
5.88
0.14
Q09_OLAP
2.94
0.04
3.24
Q10_DASHBRD
2.9
0.15
3.06
0.002
Q11_DTMNG 3
0.13
2.83
0.005
0.03
Each row in the above table is a separate linear regression model
None of the models are accurate due to the low R-squared values
Of the four, Q08_META has
The highest R Squared - .14
The lowest p-value – 0.00
The greatest coefficient – 0.28

19. Multiple Regression
Multiple Linear Regression produced similar results.
Started with all five technology survey items to predict Q26_AGRMNT.
Removed one variable at a time based on highest p- value.
This left only Q08_META, which is the same as simple linear regression above.

20. Cluster Analysis
Since Technology alone is not a good predictor of decision agreement (Q26_AGRMNT), what is?
I decided to cluster the data with the technology and social survey items.
Then determine if these clusters have differences among average Q26_AGRMNT.
I used K-Means with several K values and ended with 5 clusters.

21. Five Clusters

22. ANOVA on Q26_AGRMNT
The differences between the average value of Q26_AGRMNT by cluster is significant.
Cluster 1, the red line, has a significantly higher Q26_AGRMNT than the rest.
Cluster 2, the brown line, has a significantly lower Q26_AGRMNT than the rest.
As the social survey items increase, so does decision agreement.

23. Q08_META predicts respondents’ Q17_CONCERNS
Q15_PARTICIPATE and Q17_CONCERNS can predict Q26_AGRMNT with limited (R-squared = 0.26) accuracy.
Q08_META can predict Q17_CONCERNS with very limited accuracy (R-squared = 0.19).
Remember, Q08_META can predict Q26_AGRMNT with even less accuracy (R-squared = 0.14).

24. Conclusion (part 1)
Meta Data is a relatively consistent predictor of decision quality; more consistent than other technological areas.
Meta Data helps team members address individual concerns by giving everyone the same language and vocabulary.

25. Conclusion (part 2)
If data warehouses help make the data consistent and trustworthy,
And, if OLAP helps employees understand the current operations of the company,
And, if Dashboards help middle managers understand, discuss, and implement strategic goals and strategies,
Then, most importantly, Meta Data helps managers communicate goals more clearly, which stimulates the discussion over the dashboards and OLAP tools, built using data warehouses data.
In order to align decisions with organizational goals, Meta Data is a critical success factor at helping teams use that system to make informed decisions that align with organizational goals.

26. Recommendation to Practitioners
Build a Meta Data repository, and related artifacts such as a Business Glossary, based on Master Data Management and the Data Warehouse.
Test the new Business Glossary against the CEO’s vision and Mission Statement to ensure all terms are clearly defined.
Review the mission statement with Middle Managers with the understanding of the Business Glossary, in order to obtain feedback.
Thus, further discussion, sense-making, and goal-setting are encouraged.