May 16, 2014

Analytics Short Discussion

ECAR Analytics Maturity Index

Source: ECAR Analytics Maturity Index, 2012. http://www.educause.edu/ecar/research-publications/ecar-analytics-maturity-index-higher-education

The Maturity Index is freely available for you to take to learn your own maturity levels.

5 – Transforming4 – Implementing3 – Launching2 – Visioning1 – Starting

BI Maturity – Challenges/Opportunities

3

• Organizational Structure(s)• Cornell’s Office of Data Architecture and Analytics• Central IT, IR, other?

• Data Governance – • Getting common set of data definitions/measures• Data access/sharing

• Data Reporting/Tools• Managing BI Assets

• Investment Levels

• Expertise• BI Skill Acquisition

• Culture• Coordination and cooperation across campus

• ProcessWorking with customers

Source: Higher Education’s Top-10 Strategic Technologies for 2014, Susan Grajek, ECAR, February 2014

Technologies estimated to be deployed in approximately half or more of institutions by 2016/17: Data warehouse BI reporting dashboards Learning analytics: degree advising Administrative/business performance analytics

…and in about one in three or four institutions: Learning analytics: course level Big data Predictive analytics

Projections for analytics technologies

5

Appendix

Analytics Maturity Index contentDimension 1: Data/Reporting/Tools1. Our data are of the right quality/are clean.2. We have the right kinds of data.3. Our data are standardized to support comparisons

across areas within the institution.4. Our data are accessible to those who need it.5. Our data are collected for a purpose.6. Our data, reports, and processes are repeatable;

we don’t have to reinvent the wheel to address questions and problems that come up regularly.

7. Reports are in the right format and show the right data to inform decisions.

8. We have a process for eliminating, phasing out, or updating reports that are no longer used or of value.

9. We have the right tools/software for analytics. 

 

Dimension 2: Governance/Infrastructure1. Our information security policies and practices are

sufficiently robust to safeguard uses of data for analytics.

2. We have policies that specify rights and privileges regarding access to institutional and individual data.

3. Our Institutional Review Board (IRB) has policies and practices for handling proposals involving analytics data collection procedures.

4. We have sufficient capacity to store, manage, and analyze increasingly large volumes of data.

5. Our data are “siloed”; we have pockets of individuals who protect their data.

6. We have IT professionals who know how to support analytics.

Analytics Maturity Index content, continuedDimension 3: Investment1. Our funding level for analytics is sufficient to meet our

current needs.2. Funding for analytics is viewed as an investment,

rather than an expense.3. We have an appropriate number of data analysts.4. We invest in analytics training.5. Our analysts are too overwhelmed with routine

reporting demands to use analytics to address strategic initiatives. (scored opposite)

Dimension 4: Expertise6. We have a sufficient number of professionals who

know how to support analytics .7. We have dedicated professionals who have specialized

analytics training .8. We have business professionals who know how to

apply analytics to their areas .9. Our analysts know how to present processes and

findings to stakeholders and to the broader institutional community in a way that is visually intuitive and understandable .

Dimension 5: Culture1. Our senior leaders are publicly committed to the use of

analytics and data-driven decision-making.2. Our administration largely accepts the use of analytics.3. We have a culture that accepts the use of data to make

decisions.4. Our faculty largely accept the use of analytics for

institutional decision-making.

Dimension 6: Process5. There is effective communication between our IT and IR

departments.6. Our senior-most institutional research leader is involved in

the planning process for addressing high- level strategic initiatives or questions.

7. We have identified the key institutional outcomes we are trying to improve with better use of data.

8. Use of data is part of our strategic plan.9. We have a process from moving from what the data say to

making changes/decisions.10.We have demonstrated with at least one high-profile “win”

that analytics can lead to improved decision-making, planning, or outcomes.

BI Business Drivers – Weighted Scores

8

Have data that is consistent/reliable across the organi-

zation

Make better data-informed deci-

sions

Make it easier to access data

Provide ability to share data across the organization

Analytics tools that are easier to

use

Reduce time it takes to get

needed informa-tion, spend more

time analyzing data

Provide data in a timelier fashion

Provide more ways to

visualize/interact with data

Determine opera-tional effective-ness of my orga-

nization

0

10

20

30

40

50

60

70

For you, what are the most important business drivers behind BI? (by weighted score)

BI Business Challenges – Weighted Scores

9

OthersTop Lack of BI tools; currently in an RFP process to acquire BI systemTop Freeing up staff from other projects to work on BIFourth Some data we need doesn’t exist in our systemsFifth Data access - silos don't want other silos to see their data

Cooperation an

d coord

ination ac

ross

depts

No common so

urce of d

ata

No consis

tency

in definitions o

r term

s

Customers

knowing w

hat they

wan

t in a

BI solution

Lack o

f BI sk

ills or e

xperti

se

Costs as

socia

ted w

ith Busin

ess In

tellig

ence

BI tools a

re too hard

to use

Leaders

hip not enga

ged or s

upportive

Quality

of the d

ata is

poorOther

User ad

option is low

05

101520253035404550

The biggest barriers to using BI in your organization? (by weighted score)