Session leader: Jerry Grochow May 10, 2012 EDUCAUSE LIVE!

Enterprise Analytics and the CIO

Session leader: Jerry Grochow

May 10, 2012EDUCAUSE LIVE!

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Today’s Session

GOAL: Share results of research on ITs role in analytics programs and making them successful.

General comments about analytics programs

Review of findings to date

Research sponsored in part by the International Institute for Analytics, Intel Corporation, and SAS Institute, Inc.Some materials copyright 2011 International Institute for Analytics

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Enterprise Business Analytics

Questions to be answered:

DEFINE: What is “analytics”? INTRODUCE: How do you start an analytics

program? WHO: Whose job is it? CSF: What are the critical success factors? ISSUES: Other issues

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What is “analytics”?

Value-focused data analysis Predictive modeling, optimization – not just

statistics Leading to data-driven decision-making A component of “business intelligence”

Collection, management, reporting, analytics Characterized by research and

experimentation

DEFINEINTRODUCEWHOCSFISSUES

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What is “analytics”?

EDUCAUSE definition: Analytics is the use of data, statistical

analysis, and explanatory and predictive models to gain insights and act on complex issues.

Holy Grail: “Dynamic real-time business optimization”


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Defining “analytics” by what questions you want answered

Business question Terminology

What happened in the past? Periodic (regular) reporting, ad hoc reporting, “dashboards”

Tell me what happened that wasn’t expected

Exception reporting

Tell me when something unexpected happens

Alerts (real-time exception reporting)


• All of these are “reporting” but not really “analytics” activities

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Defining “analytics” by what questions you want answered

Business question TerminologyTell me something I don’t know Data miningWhy is this happening? Analysis, including statistical analysis,

on-line analytical processing (OLAP, an older term), modeling

What will happen in the future? Forecasting, predictive modeling, predictive analytics

How can I improve what happens in the future?

Optimization

Show me graphically Visualization techniquesI want to know now how to improve the future, based on what happened in the past and everything I know about what is likely to happen in the future – and I want to know what steps to take.

“Dynamic real-time business optimization” based on predictive analytics – “prescriptive analytics”

Take those steps automatically. “Embedded analytics”

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How do you bring analytics into an organization?

Establish the value/importance of analytics

Set specific business goals and strategy

Develop a plan for analytics activities Staffing plan Data plan Technology plan

Execute the first project Measure the value Communicate


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Where does analytics provide value?

The value of analytics is often stated in terms of “understanding” the organization or the business or the customers

But this translates into:


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Where does analytics provide value?

Improved operations [Operational Analytics] Goal: Reduce costs

Grow the existing business [Product Analytics] Goal: Increase revenues

Improve outcomes of research or academics [Learning Analytics; Research Analytics] Goal: Improve outcomes of research or teaching

Innovation Goal: Create new businesses or sources of revenue


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Questions? / Survey Question 1

What are the goals of your analytics program? Don’t have an analytics program Haven’t determined goals If you do have program goals, what are they?▪ General understanding▪ Reduce costs of operations▪ Improve outcomes of research or teaching▪ Increase revenues from existing business▪ Create new businesses or sources of revenues▪ Other DEFINE

INTRODUCEWHOCSFISSUES

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Activities for On-going Program

Funding Governance Data architecture Technology architecture Operational implementation and

assessment Integration of analytics with operational

systems Communication and education Evolution


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Stages of IT Capability Maturity

Under consideration “Visioning”

Getting Started “Launching”

Under Construction “Implementing”

Mature “Transforming”


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What “IT Analytics Capability” do you have or are you planning?

People Process Governance

Technology

“Under Consideration” (Visioning)

“Getting Started” (Launching)

“Under Construction” (Implementing)

“Mature” (Transforming)

On-board Collaboration between IT and BUs; measuring value

Steering Comm; data and analytics governance “SOP”

Operational DW; ETL tools; analytics integrated into operational systems; measurement tools; evaluation program

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Whose job is it?

Typically started by a business function

Most successful programs come from a business/IT partnership “Creating a marriage…” Working together on people and process

IT has to take responsibility for infrastructure

IT can become a “champion” for analytics


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Organization Structure

How should analytics activity be organized? One or multiple departments?

Where should it report? To IT or not to IT…

Does it matter?


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Does organization placement really matter?

President / Provost / Chancellor

Institutional Analytics

Academic Hierarchy

Administrative Hierarchy

IT

Research Hierarchy


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Academic Hierarchy

Institutional Analytics (?)

Administrative Hierarchy

IT

Institutional Analytics (?)

Research Hierarchy


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Academic Hierarchy Administrative Hierarchy

IT

Institutional Analytics

Research Hierarchy


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Where does the analytics organization report within your institution? [May select multiple] President/Chancellor/Senior Official Provost/Academic Leader CFO/CBO/Administrative Leader Leader of some academic unit Leader of some administrative unit (other than IT) Leader of IT Other


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Critical Success Factors

What determines the likely success of an analytics program? How do you define “success”? Meeting goals [see above] Becoming an “analytical organization”


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Davenport Maturity Stages

Maturity as an “analytic competitor”

Stage 1: “Major Barriers” Stage 2: “Local Activities” Stage 3: “Vision Not Yet Realized” Stage 4: “Almost There” Stage 5: “Analytical Competitor”


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Pressing business need Availability of data / data quality Executive leadership/sponsorship Committed, knowledgeable people Clearly defined objectives Focus on analytics that have value to

the business Choosing the right first problem Communication/education DEFINE


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Many factors are important


ImportanceInitial

ProjectSustaining Program

Committed, knowledgeable people: interested in, knowledgeable about analytics

M H

Executive leadership/sponsorship M H

Clearly defined (and initially limited) objectives H M

Choosing the right problem: find a pressing business need with high value

H M

Communication/education: about the value of analytics, about the importance of the problem being studied

M H

Using the right analytic techniques and software L H

Availability and access to quality data L M

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Which CSF’s are in place in your organization? Pressing business need Availability of data / data quality Executive leadership/sponsorship Committed, knowledgeable people Clearly defined objectives Focus on analytics that have value to the

business Choosing the right first problem Communication/education DEFINE


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Measuring your progress on CSF

Committed people

Educated management

Executive support

Quality data

Defined objectives

Technology

0

1

2

3

4

5

Analytics Critical Success Factors


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Critical “un-success” factors

Focusing excessively on one dimension of analytical capability (e.g. too much technology)

Attempting to do everything at once Investing excessive resources on analytics that have

minimal impact on the business Investing too much or too little in any analytical capability,

compared with demand Choosing the wrong problem, not understanding the

problem sufficiently, using the wrong analytical technique or the wrong analytical software

Automating decision-based applications without carefully monitoring outcomes and external conditions to see whether assumptions need to be modified.”

[Tom Davenport, Competing on Analytics, p. 129]


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Other issues

Change management Introducing analytics isn’t so different from

introducing other new management processes Assessment

of implementation (how will you know when you are an “analytic organization”?)

assessment of value of analytic program vs. goals

Future technology challenges HPC, cloud, anywhere-anytime analysis Unstructured data,“big data”


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CIO “Readiness Assessment” (Business Questions)

1. Can you articulate how analytics will help the organization?

2. Do you have good relationships with the business leaders whose groups will most benefit?

3. Do you know what your peers are doing with analytics and how it is helping their organizations?

4. Are you “passionate about analytics?” [Have you been to an analytics conference or symposium recently?]

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CIO “Readiness Assessment” (IT Questions)

1. Does the IT department understand the key analytics issues for the enterprise?

2. Do you understand what the key analytics issues are for the IT department?

3. Does your department have the most important skill sets necessary for success with analytics?

4. Do you encourage experimentation? Is your development methodology flexible enough to accommodate analytics projects?

5. How good is the organization’s data? Are definitions consistent? Is data “scrubbed”?

6. Do you know who the vendors and integrators are in analytics IT and what they can do for your organization?

7. Are you prepared for “big data,” high performance computing, real-time analytics – i.e. the future?

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CIO “Readiness Assessment” -- How did you score? 1-4: Lots of work to do! 5-8: On the way! 9-11: You’re ready to get moving! 12: You know there are more

questions!

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ECAR Research project on analytics in higher ed

Led by Jackie Bichsel ([email protected])

Survey underway

Presentations at ECAR Symposium in Boulder (June 20)

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THANKS FOR PARTICIPATING

Jerrold M. [email protected]

• Senior Consultant to colleges and universities and the organizations that serve them• Internet2 Interim Vice President for NET+ Services

Documents

Session leader: Jerry Grochow May 10, 2012 EDUCAUSE LIVE!