bihanaeu2016 vallo analyticsendtoend

105
Produced by Wellesley Information Services, LLC, publisher of SAPinsider. © 2016 Wellesley Information Services. All rights reserved. Analytics End-to-End! An In-Depth Examination of the Full Analytics Process and How to Make It Work at Your Organisation Eric Vallo EV Technologies

Upload: aviram-levin

Post on 22-Jan-2018

40 views

Category:

Retail


1 download

TRANSCRIPT

Page 1: Bihanaeu2016 vallo analyticsendtoend

Produced by Wellesley Information Services, LLC, publisher of SAPinsider. © 2016 Wellesley Information Services. All rights reserved.

Analytics End-to-End! An In-Depth Examination of the Full Analytics Process and How to Make It Work at Your Organisation

Eric Vallo EV Technologies

Page 2: Bihanaeu2016 vallo analyticsendtoend

1

In This Session

• An in-depth examination of the full analytics process and how to make it work at your

organization. Attend this session for a comprehensive look at the end-to-end analytics

process, critical steps necessary for successfully deploying the right analytic tools and

process, and specific “gotchas” to look out for that can lead to unwanted results. The

analytics process has many stages, and an increasing number are hidden by modern

tooling.

Page 3: Bihanaeu2016 vallo analyticsendtoend

2

Money

is no Object

Page 4: Bihanaeu2016 vallo analyticsendtoend

3

What We’ll Cover

• BI Maturity in Today’s World

• Data – Garbage In, Garbage Out

• Innovations in BI to Drive Better Outcomes

• Are You Winning Any Fans in Your BI Initiatives?

• Wrap-Up

Page 5: Bihanaeu2016 vallo analyticsendtoend

4

• We own thirty BI tools

• Self-service was a myth we totally bought into

• Users are clamoring for information, not data

• Predictive Analytics are a fantasy, more like

science fiction

• My users just purchased another BI tool (and I

kind of like it)

You Are Here

Source: If you don’t know we can’t be friends.

OK Hitchhiker’s Guide to the Galaxy.

We still can’t be friends if you didn’t know.

Page 6: Bihanaeu2016 vallo analyticsendtoend

5

The BI Maturity Model 2016

Page 7: Bihanaeu2016 vallo analyticsendtoend

6

You Are Here

876,050

All ad hoc

Page 8: Bihanaeu2016 vallo analyticsendtoend

7

The BI Maturity Model

• An exercise in real-time analytics!

• A critique before we begin to self-evaluate

• http://bit.ly/analyticse2e

• What did we miss?

Page 9: Bihanaeu2016 vallo analyticsendtoend

8

According to Gartner, Worldwide IT spending to

decline 5.5%

Everybody Has an Opinion

Source: www.gartner.com/newsroom/id/3084817

Page 10: Bihanaeu2016 vallo analyticsendtoend

9

VentureBeat shows that brands plan to increase

their spending on the category (marketing analytics)

by a whopping 73% over the next three years

Everybody Has an Opinion (cont.)

Source: http://venturebeat.com/2015/08/21/new-research-companies-plan-to-massively-increase-spend-on-marketing-

analytics/

Page 11: Bihanaeu2016 vallo analyticsendtoend

10

According to IDC, the Healthcare analytics

market will grow between 8% and 11%

Everybody Has an Opinion (cont.)

Source: www.idc.com/getdoc.jsp?containerId=HI255139

Page 12: Bihanaeu2016 vallo analyticsendtoend

11

BI Is Everywhere

It is in your stock search

Page 13: Bihanaeu2016 vallo analyticsendtoend

12

BI Is Everywhere (cont.)

It is in your weather search

Page 14: Bihanaeu2016 vallo analyticsendtoend

13

BI Is Everywhere (cont.)

It is in your favorite team’s standings (not mine anyway)

Page 15: Bihanaeu2016 vallo analyticsendtoend

14

BI Is Everywhere (cont.)

It is in your market research

Really

Effective

Competitor

Marketing

Was Here

Page 16: Bihanaeu2016 vallo analyticsendtoend

15

The Challenge Today – Define BI

• Is it …

Analytics?

Predictive?

Enterprise Performance Management?

Governance and Risk Control?

Big Data?

Mobile Apps?

Internet of Things?

Internal or External/Commercial?

“Before you let your head explode figuring out what everyone else is doing. Realize that

analytics is woven into the very fabric of everything we do. Data is turned into information a

billion ways.” — me

Page 17: Bihanaeu2016 vallo analyticsendtoend

16

The Challenge Today – Factory of Sadness

• A cycle of doing things wrong is no excuse to fail, but let’s explore why BI initiatives fail

Wrong tool for the wrong job

Data quality stinks

There are no requirements

Waterfall makes us slow

No business support

Horrible consultants (not it)

Poor design

The wrong team

Poor or no executive sponsorship

No creativity

No competency

No goals = nothing to achieve

No ritual goat sacrifice

Page 18: Bihanaeu2016 vallo analyticsendtoend

17

The Challenge Today – Where We Use BI

• It is our job as BI practitioners to think beyond spreadsheets and reports

• Look for opportunities in BI:

Integrated into operations centers

On shop floors

On mobile devices

Driver of our manufacturing processes

Integrated in call center apps

In the hands of line workers

On the field

In your car

In our cockpit

In your thermostat

In your golf swing

In your distilling process (mmmm)

Page 19: Bihanaeu2016 vallo analyticsendtoend

18

The Challenge Today – Old-School Successful Practices

Analysis

Design

Development Test

Deploy

Maintain

Page 20: Bihanaeu2016 vallo analyticsendtoend

19

The Challenge Today – Agile BI Implications

Analysis

Design

Development Test

Deploy

Maintain

Iterate Here?

Page 21: Bihanaeu2016 vallo analyticsendtoend

20

The Challenge Today – Agile BI Implications (cont.)

Analysis

Design

Development Test

Deploy

Maintain

Or Here?

Page 22: Bihanaeu2016 vallo analyticsendtoend

21

Break the Cycle – Analysis

Analysis

• Identify personas such as consumers, authors, roles

• Define consumption mechanisms

• Storyboard ideas, visualization concepts, and create experience maps

• Understand all of the data from source to target, unstructured or structured, retention

needs, and what turns data into information

• Create your success criteria and goals now

Page 23: Bihanaeu2016 vallo analyticsendtoend

22

Break the Cycle – Design

• Define the methodologies to transform data into consistent reporting structures, whether

using SAP HANA via in-memory views of data, or in ETL process to create persistent

structures

• Construct wireframes, prototypes, and style guides that provide developers all the keys

needed to build a USABLE and BEAUTIFUL user experience

• Define the technical architecture required to support and sustain your user population

now, and in 18 to 24 months

Building for today only ruins your user experience within 12 months

• Create your test cases now

• Consider requirements traceability to ensure no stone is left unturned

• Focus on tool selection now!

Design

Page 24: Bihanaeu2016 vallo analyticsendtoend

23

Development

Break the Cycle – Development

• Go code

• Iterate frequently (agile)

• Review with your end consumer or key stakeholders frequently

• Be a rock star

Page 25: Bihanaeu2016 vallo analyticsendtoend

24

Test

Break the Cycle – Test

• We know. Nobody wants to do it.

• Don’t be this guy

It’s just embarrassing

• Use documented test cases in conjunction with

requirements traceability to avoid misses

• Perform data validation from within the reporting solution

• Validate validation outcomes

Page 26: Bihanaeu2016 vallo analyticsendtoend

25

Deploy

Break the Cycle – Deploy

• Do not be disruptive to user cycles

• Have a solid rollback plan when things go wrong

• Ensure that users are happier after your deployment than before

• Plan for post-implementation support

Page 27: Bihanaeu2016 vallo analyticsendtoend

26

Who Is Your Dream Team?

• There are enough personas in analytics now to man an aircraft carrier

Data Architect

Data Modeler

Database Developer

BI Architect

BI Developer

Predictive Modeler

UX Designer

UX Developer

Mobile Experience Developer

Good Cop/Bad Cop

Project Leader

Page 28: Bihanaeu2016 vallo analyticsendtoend

27

Don’t Make Your Team Unhappy

Page 29: Bihanaeu2016 vallo analyticsendtoend

28

Do Make Your Team Happy

Page 30: Bihanaeu2016 vallo analyticsendtoend

29

Boiling It All Down to Your BI Project

• We understand the SDLC, now how do we apply it within our BI projects to answer

important questions:

What BI are we building?

What landscape will power it?

What data do I need?

How do I build it?

How do I get people to use it?

Am I thinking about how my business will interact with it?

Am I thinking about how my business will make decisions with it?

How do I secure it?

Page 31: Bihanaeu2016 vallo analyticsendtoend

30

Size Really Means Everything

• With requirements in hand we must architect landscapes that fulfill the demand based upon:

Data volumes and frequency

User population and forecasted consumptions

Availability in geographies and time zones

Requirements for on-premise vs. cloud-based landscapes

Redundancy

All in consideration for …

SAP HANA

SAP BW

SAP ERP

SAP BI

SAP EIM

Page 32: Bihanaeu2016 vallo analyticsendtoend

31

• Project usage estimates

• Forecast growth rates

• Exist in documented form

for all major technologies

Sizing Estimators and Quick Sizers

http://service.sap.com/quicksizer *

* Requires login credentials to the SAP Service Marketplace

Page 33: Bihanaeu2016 vallo analyticsendtoend

32

Sizing Guide

Fine-tune the sizing requirements!

www.sap.com/bisizing

Page 34: Bihanaeu2016 vallo analyticsendtoend

33

Security Policies

• With requirements we must also define how to control access

Page 35: Bihanaeu2016 vallo analyticsendtoend

34

Build Planning Is as Important as Your BI Project

• Planning is everything

Agree on target architecture and design

based on the tools to be used in your

landscape

Provision early enough to not obstruct

development teams

Ensure development environments are up

and running before development is ready

to begin

Understand sequence for data, ETL, and

BI to set priorities of builds

Identify all dependencies and milestones

Set reasonable expectations

Out of the box rarely works – design well

and account for customization

Page 36: Bihanaeu2016 vallo analyticsendtoend

35

INTERMISSION

Page 37: Bihanaeu2016 vallo analyticsendtoend

36

What We’ll Cover

• BI Maturity in Today’s World

• Data – Garbage In, Garbage Out

• Innovations in BI to Drive Better Outcomes

• Are You Winning Any Fans in Your BI Initiatives?

• Wrap-Up

Page 38: Bihanaeu2016 vallo analyticsendtoend

37

Thinking Through New Data

• Data Profiling

Paint the picture for what constitutes the data we need

• Data Modeling

State the intended direction for the design of the data

• Data Integration

Acquire the data and transform it into a usable form

• Data Quality

Standardize and make more useable

• Data Validation

Make sure it’s good

• Data Governance and Stewardship (Later)

Not the best way to dump in

data for your users

Page 39: Bihanaeu2016 vallo analyticsendtoend

38

Data Profiling – Why Bother

• Data profile is ultimately a process that leads to significantly better outcomes before we

ever get to data validation

• We thoroughly seek to understand new data’s

Applicability – does it add business value?

Redundancy – has it already been integrated?

Data types – will we represent it properly?

Business rules – how does it relate to existing data?

Meaning – how does it solve business problems?

• It may not be obvious, but Data Profiling actually happens in Data Quality and in Data

Validation? Mic drop.

Page 40: Bihanaeu2016 vallo analyticsendtoend

39

Data Modeling

• Data Modeling expresses the intended format for the data when it reaches its final

location

• As much art as it is science with different perspectives

Conceptual data modeling for semantics

Logical data modeling for organization and relationships

Physical data modeling to physically define the final outcomes

• Are further refined through

Entity relationship diagrams

Various model types such as star schemas, network models, relational models, etc.

Page 41: Bihanaeu2016 vallo analyticsendtoend

40

Data Profiling Tools

• Many tools are capable of supporting your data profiling needs. The application depends

on you (and budget).

Native database SQL tools like SAP HANA Studio, Oracle SQL Developer, Microsoft

SQL Server Studio

You certainly need to be a SQL ninja

ETL tools to explore just about any source

SAP Information Design Tool to explore your existing sources

SAP BusinessObjects Lumira to explore both database and flat file layouts, data types,

and more

Page 42: Bihanaeu2016 vallo analyticsendtoend

41

Data Profiling with the Information Design Tool

• Why?

Rapidly prototype semantic layer structures around new data

Page 43: Bihanaeu2016 vallo analyticsendtoend

42

Data Profiling with SAP BusinessObjects Lumira

• Why?

Leverage database or flat file data together or separately

Quickly infer relationships without column-level data profiling

Page 44: Bihanaeu2016 vallo analyticsendtoend

43

Data Integration

• A giant umbrella that encapsulates many things, but succinctly:

Data integration is the process that generally takes us from point A to point C with a

brief stop at point B along the way

Or, traditional extract, transform, load

Alternatively, extract, load, transform

Alternatively to the alternative, just replicate (next)

• Sophistication levels vary from native bulk loaders to products like SAP Data Services

• Drastically differs depending on structured or unstructured data

• Not to be confused with Data Federation

Page 45: Bihanaeu2016 vallo analyticsendtoend

44

To Transform, or Not to Transform … Data Replication

• SAP Landscape Transformation Replication Server (SLT)

Data Integration presumes we’re going to extract, transform, and load (ETL) in some

shape or form

SLT skips the “T” part

Source Target

That’s it!

Page 46: Bihanaeu2016 vallo analyticsendtoend

45

To Transform, or Not to Transform … ERP/BW

• Classic SAP ERP

• Leverages third-party database

• Requires intensive data modeling and

transformation

• Data is essentially retained in triplicate for

analytics requirements

Source: SAP

Page 47: Bihanaeu2016 vallo analyticsendtoend

46

To Transform, or Not to Transform … ERP/BW (cont.)

• Real-time analytics

• Transactional data is transformed

in memory

• Supports event stream

processing/alerting

• Significant out-of-the-box capability

Source: SAP

Page 48: Bihanaeu2016 vallo analyticsendtoend

47

To Transform, or Not to Transform … Data Federation

Dynamic Tiering Smart Data Access

• Data Federation permits non-materialized views of data as a loosely coupled architecture

to other database platforms to further extend our databases

Source: SAP

Page 49: Bihanaeu2016 vallo analyticsendtoend

48

Data Retention Strategies

• Should your data live on forever?

Consider industry-specific compliance rules

Consider the value to predictive analysis

Data archival strategies to back up and truncation creates accessibility challenges

Hot/Warm/Cold data strategies keep relevant data available with historical data on

cheaper storage

Page 50: Bihanaeu2016 vallo analyticsendtoend

49

ETL Teams – Like the Bass Player in Any Band

• Underappreciated at best, ETL teams get us data in a non-SAP HANA world and make it

usable

• My POV:

Don’t let the ETLer be a dying breed

It’s not the future of the MVS programmer

But diversification in skillset is not a terrible idea

Bass Players Against Absence

In Videos

Page 51: Bihanaeu2016 vallo analyticsendtoend

50

Should the Bass Player Be Worried?

• Let’s review the landscape of data we analyze and what this means to ETLers

SAP HANA SAP BW SAP ERP Agnostic Unstructured

Modeled, transformed, optimized for reporting

Replicated via RDS or SAP HANA Live Views

Replicated via RDS or SAP HANA Live Views

Transactional, modeled in memory

Text analysis, mined, modeled

Page 52: Bihanaeu2016 vallo analyticsendtoend

51

Data Quality

• Without data quality, we face the single easiest source of user confidence erosion

Data integrity issues at the database layer

Incorrect reporting relationships in our universes

Reports that simply do not represent the data correctly

Page 53: Bihanaeu2016 vallo analyticsendtoend

52

Data Validation

• My test case generator:

Page 54: Bihanaeu2016 vallo analyticsendtoend

53

What We’ll Cover

• BI Maturity in Today’s World

• Data – Garbage In, Garbage Out

• Innovations in BI to Drive Better Outcomes

• Are You Winning Any Fans in Your BI Initiatives?

• Wrap-Up

Page 55: Bihanaeu2016 vallo analyticsendtoend

54

Pillars of BI

Data

Discovery

Dashboards

Reporting

Predictive

Page 56: Bihanaeu2016 vallo analyticsendtoend

55

Simplification of BI Portfolio

Source: SAP Run Simple: Convergence of the SAP BusinessObjects BI Product Portfolio

Jayne Landry, Global VP and GM Business Intelligence (SAP, June 2014).

Page 57: Bihanaeu2016 vallo analyticsendtoend

56

Mobile Support for Nearly All SAP BI Content Types

Source: SAP

Page 58: Bihanaeu2016 vallo analyticsendtoend

57

Characteristics – Data Discovery

• A concept whereby we seek patterns or opportunity to turn data into information that can

result in us creating repeatable processes in BI

• Really … in lock step with Data profiling

• Historically done in Excel or in Web Intelligence (improperly)

• Explorer was a tremendously good fit but didn’t see the type of adoption that Web

Intelligence did

Data discovery is a reality that a small but vocal group of your smartest users have been

trying to make happen with Web Intelligence for years ... and have been very vocal.

Page 59: Bihanaeu2016 vallo analyticsendtoend

58

SAP BusinessObjects Lumira Sizing and Deployment

• Don’t guess, do size

Like all sizing guides, focus on current sizing standards

http://scn.sap.com/docs/DOC-53374

• Don’t stack on top of SAP BusinessObjects

This is a new, powerful architecture that wants resources

• Focus on the right user persona

Producing SAP BusinessObjects Lumira content is not for everyone. Consider:

Data Analysts

Business Analysts

• The pace of change is great

Be the enabler, not a hindrance

Custom education/workshops FTW

Page 60: Bihanaeu2016 vallo analyticsendtoend

59

SAP Explorer – What’s the Plan?

• Realize that while no formal end of life is announced, it is no longer strategic

• If you’ve invested in it, there is a solid guess in a 2-3 year usefulness of content that is

supported

• Consider SAP BusinessObjects Lumira as a replacement and rationalize BI Suite adoption as

a positive way to transition

Page 61: Bihanaeu2016 vallo analyticsendtoend

60

Characteristics – Dashboards

• Established, guided analysis that tells an analytic story

• Often thought of as analytic apps (and sadly, with no write-back capabilities)

• Our first real opportunity to mimic Google and create analytic apps that don’t look or feel

like BI!

• Your chance to build a beautiful UX, but let’s talk about that in a bit

Page 62: Bihanaeu2016 vallo analyticsendtoend

61

What Is a Dashboard?

A dashboard is a visual display

of

the most important information needed to achieve one or more objectives

that has been

consolidated on a single computer screen

so it can be

monitored at a glance

Stephen Few, Information Dashboard Design, Second Edition,

(Analytics Press, 2013). ISBN 978-1-938377-00-6

Page 63: Bihanaeu2016 vallo analyticsendtoend

62

What Is a Dashboard? (cont.)

A dashboard is a layered information delivery system

that parcels out

information, insights, and alerts

to users

on demand

so they can

measure, monitor, and manage business performance more effectively

Wayne Eckerson, Performance Dashboards, Second Edition,

(Wiley, 2010). ISBN 978-0-470-58983-0

Page 64: Bihanaeu2016 vallo analyticsendtoend

63

Characteristics – Reporting

• Everyone builds reports, and always will

• The evolution of green bar (who is with me?)

• Operational in nature, repeatable, and meant to be

distributed by the few to the masses

Think BI user pyramid, largely created by the few,

but for larger audiences of people to consume

• A tad confusing: Web Intelligence vs. Crystal

Reports

Page 65: Bihanaeu2016 vallo analyticsendtoend

64

Characteristics – Predictive

• Predictive isn’t just for people that wear pocket protectors any more

• SAP’s acquisition of KXEN puts predictive in the hands of data analysts

• Guided analysis (brilliant) to help guide us to the relevant variables

Page 66: Bihanaeu2016 vallo analyticsendtoend

65

INTERMISSION

Page 67: Bihanaeu2016 vallo analyticsendtoend

66

What We’ll Cover

• BI Maturity in Today’s World

• Data – Garbage In, Garbage Out

• Innovations in BI to Drive Better Outcomes

• Are You Winning Any Fans in Your BI Initiatives?

• Wrap-Up

Page 68: Bihanaeu2016 vallo analyticsendtoend

67

Let’s Be Honest

• Like a good sporting event, your business intelligence team needs fans. User buy-in and,

undoubtedly, future-state user adoption, are two keys to success of your business

intelligence initiative. It doesn’t necessarily matter whether it is reports or dashboards, or

mobile or the cloud, we all need fans to succeed.

Page 69: Bihanaeu2016 vallo analyticsendtoend

68

Let Us Consider …

• How we better understand tool choices and communicate them to the business

community

• The approach to identifying methodologies for incoming work and appropriate strategies

to eliminate cowboy business intelligence

• Creation of a collaborative environment with the business that focuses on inclusion

• Why getting off the desktop is a priority and yes, flashy does sell a project

• Define measures of success from a technological perspective first to help manage your

investment

• You’ve got a BI solution delivered, now what? Set effective goals and measure them.

Page 70: Bihanaeu2016 vallo analyticsendtoend

69

Worldwide

IT Spending

$1.8 Trillion

Orgs

Maintaining or

Increasing BI

75%

Enterprise

Users Actually

Using BI

28%

Number of

Customers

Still Running

DeskI

TOO MANY

Orgs

Planning on

Mobile BI

44%

Page 71: Bihanaeu2016 vallo analyticsendtoend

70

Classifications for IT Projects of Any Kind

• What is the ROI?

How many new customers will we

be able to identify?

How much new revenue will this

generate?

• What is the cost savings?

How much more efficient will

process X be?

How will we be able to retire a

legacy process as a result?

Page 72: Bihanaeu2016 vallo analyticsendtoend

71

Technology Is Not

the Problem

Page 73: Bihanaeu2016 vallo analyticsendtoend

72

Community Growth

Do you understand the fundamental size of your organization’s user footprint and growth

patterns?

Page 74: Bihanaeu2016 vallo analyticsendtoend

73

Grow Out of the

Cowboy Way

Page 75: Bihanaeu2016 vallo analyticsendtoend

74

Mechanisms to enable Agile analytics projects are everywhere and becoming commonplace

in maturing BI organizations.

Page 76: Bihanaeu2016 vallo analyticsendtoend

75

A Community with

Skin in the Game

Page 77: Bihanaeu2016 vallo analyticsendtoend

76

Maturing BI organizations must put unparalleled level of thought into driving decision

making on information, not just data, and fostering a community of users

Page 78: Bihanaeu2016 vallo analyticsendtoend

77

Extend Your Reach with Events

Classroom Training

Internal Webinars

Internal User Group

Lunch and Learns

Vendor Webinars

Promote Local or National

Events

Page 79: Bihanaeu2016 vallo analyticsendtoend

78

Sample Agenda for Internal User Group

• Welcome

• Announcements (New applications, features,

policies)

• Spotlight user

(Executive or power user)

• Tips and Tricks

• Spot Training (Based on help desk calls)

• Q&A

Page 80: Bihanaeu2016 vallo analyticsendtoend

79

Creating Compelling Content

Page 81: Bihanaeu2016 vallo analyticsendtoend

80

BI Branding Strategy

• Flashy is not greater than functional (or is it?)

• Graphic Designers User Experience Designers are awesome

• Style guides are important

• Branding for both BI and for data builds a strong message

Page 82: Bihanaeu2016 vallo analyticsendtoend

81

Brand Consistency

• Consistent branding

brings common

experiences to BI

and Analytic Apps

Page 83: Bihanaeu2016 vallo analyticsendtoend

82

User Experience Guidelines

• We document with

rigor and leverage

templates to

reduce

development time

• We seek to design

content that is

impactful whether

in browser or on

mobile

Page 84: Bihanaeu2016 vallo analyticsendtoend

83

Sample Experience Map Member Experience Map

Guiding Principles

Member Journey

Opportunities

STAGES Planning

Treat me like a pro. Connect me to the right people. Help me nd the right product. Care about my success.

De nition Preparation Execution Post-Completion

DOING

STAGE: Planning STAGE: Planning

STAGE: Planning, De nition

STAGE: Preparation STAGE: Execution

STAGE: Preparation, ExecutionSTAGE: Planning, De nition

STAGE: Planning, De nition

THINKING

FEELING

PLANNING, DEFINITION PREPARATION, EXECUTION POST-COMPLETION

Become a

Member

- Products

- Leads

- Projects

- Store Locater

- Showroom Visit

- Sample Checkout

- Product Catalog

- Recommended

Products

- K&B Review If support is needed

- Projects Completed

- Products Purchased

- Referral History

- Client Reminders

- Project Support

- Product Reviews

- Rebates

- Renew Membership

- Warranty Info

- AM/Member/

Client Match-up

- Create Projects

- Estimate/Measure

Always highlight our value

proposition.

Maximize the Member’s time. Build trust with ProSource.

Create transparency in the

process.

Proactively solve problems. Give opportunities for

referrals.

Empower Members to make

better business decisions.

Promote the bene ts of

membership.

Make payment simple.

Extend Member’s business

services.

Help Members build their

network.

Research Projects,

Resources, & Leads

Initiate Project

Browse Products

Select Products Order

Products

Wait for

Arrival

Schedule

InstallWorking and Unexpected Changes

Receive Referral Bonus

Follow Up with Client

Non-linear, no timePhone ShowroomWeb In-Person Linear Non-linear, with time

• What value does ProSource bring to my

business?

• How do I grow my business?

• Where will I nd my next project?

• How do I keep my skill set relevant?

• How much am I going to make?

• I want to know I'm buying the right

products.

• Do I have the right people for this

project?

• Am I wasting my time?

• I hope my purchase goes smoothly.

• Where are my products?

• Is everything ready for installation?

• Does my client know where we are in the

process?

• Is everything on schedule?

• What will ProSource do if I have an issue?

• I have to collect my payment when I'm

done.

• I wonder if everything worked out for my

client.

• How do I make my next project easier?

• I want my clients to refer me to their

friends.

• Where is my bonus?

• I'm concerned about completing my

current projects.

• I'm inspired by other people's projects.

• I'm worried about nding my next client.

• I'm excited to start this project.

• I'm worried about my other projects.

• I'm relieved to know my Account

Manager will take care of me.

• I'm worried that my products won't

arrive on time.

• I'm anxious about making this project

successful.

• I'm frustrated because of unexpected

issues.

• I'm happy to be done and get paid.

• I feel accomplished from completing this

project.

Browse

Connect

Select

Purchase

Pick-up

PaymentReport

Maintain Follow-up

ResolveIssues

Status Updates

STAGE: Post-Completion STAGE: Post-Completion

STAGE: Post-Completion

• Communicates

interactions within

our reports,

dashboards, and

apps

• Helps us to gain

consensus with our

business users as

we articulate vision

for our work

Source: Integrity STL

Page 85: Bihanaeu2016 vallo analyticsendtoend

84

• Build user experiences that GIVE your users the information they need. Don’t make

them deduce it.

Data to Information

Page 86: Bihanaeu2016 vallo analyticsendtoend

85

Is This Your BI Portal?

OR

Page 87: Bihanaeu2016 vallo analyticsendtoend

86 86

• Quote a guru?

Branding Rationale

Customization demonstrates to users that you’ve

created a solution tailored to their needs.

Imag

e c

ou

rtesy S

tarb

uc

ks/

Page 88: Bihanaeu2016 vallo analyticsendtoend

87

SAP BI Launchpad – Default

Undifferentiated

from thousands

of other customer

installations

Page 89: Bihanaeu2016 vallo analyticsendtoend

88

SAP BI Launchpad – Custom

• InfoView? BI Launchpad? Doesn’t matter

because building your organization’s BI

brand insulates your users from

confusing vendor product name

changes.

• A small gesture that communicates that

a commodity BI product has been

tailored for your organization

Page 90: Bihanaeu2016 vallo analyticsendtoend

89

Build Your Brand Using BICC Intranet

BICC Intranet

Access

Product Links

How to get access?

Help

Newsletter Archive

How-to Videos

Tips and Tricks Blog

Event Calendar

How to open incident?

Documentation

BI on BI KPIs

Vendor Documentation

Universe Documentation

Page 91: Bihanaeu2016 vallo analyticsendtoend

90

Forget About User Adoption

Page 92: Bihanaeu2016 vallo analyticsendtoend

91

Set Goals and

Measure Them

Page 93: Bihanaeu2016 vallo analyticsendtoend

92

Challenging History

• In measuring user adoption of BI tools you will always come up short

• Gartner says user adoption of BI tools is 28%

• How does this differ from any point in history?

• Remember back to the classic

pyramid of users Super Users

Competent

Users

Consumers

Page 94: Bihanaeu2016 vallo analyticsendtoend

93

Pervasive BI > User

Adoption

å pBi = 6.58134( ) òò ¥ luserAdoption 21a

Page 95: Bihanaeu2016 vallo analyticsendtoend

94

Activity

Organizations must take a step back and re-evaluate the definition of growth. In partnership

with the business, we define measures of adoption.

Page 96: Bihanaeu2016 vallo analyticsendtoend

95

Inactivity

Inactivity may be a misnomer. Consider the pyramid, once again. What users produce

content and what users consume content?

Page 97: Bihanaeu2016 vallo analyticsendtoend

96

Inactivity Trend

Standardize on consumption mechanisms and set benchmarks! When we simplify the way

our users access information, we make their adoption, or lack thereof, understandable.

Page 98: Bihanaeu2016 vallo analyticsendtoend

97

Content Growth

We can also leverage the information about our most active producers to understand

demand for information, which is a predictor for growth in licensing and infrastructure.

Page 99: Bihanaeu2016 vallo analyticsendtoend

98

Report Distribution Frequencies

We can also leverage the information about our most active producers to understand

demand for information, which is a predictor for growth in licensing and infrastructure.

Page 100: Bihanaeu2016 vallo analyticsendtoend

99

What We’ll Cover

• BI Maturity in Today’s World

• Data – Garbage In, Garbage Out

• Innovations in BI to Drive Better Outcomes

• Are You Winning Any Fans in Your BI Initiatives?

• Wrap-Up

Page 101: Bihanaeu2016 vallo analyticsendtoend

100

Where to Find More Information

Wayne Eckerson, Performance Dashboards:

Measuring, Monitoring, and Managing Your

Business, Second Edition (Hoboken, NJ: Wiley, 2010)

ISBN 978-0-470-58983-0.

Page 102: Bihanaeu2016 vallo analyticsendtoend

101

7 Key Points to Take Home

• Create a strategy that will align the capability of your business intelligence team with the

needs of your business

• Define a process to be fully armed with matching the right SAP business intelligence tool

to the business need

• Acquiring data isn’t necessarily straightforward. Let’s be smart about it and maximize our

use of technology.

• You’ve got a business intelligence solution delivered, now what?

• Focus on beautiful and usable analytics

• Create a community, not just a technology your business is forced to adopt

• Goals without a way to measure them are useless. Create meaningful metrics to ensure

you maximize adoption.

Page 103: Bihanaeu2016 vallo analyticsendtoend

102

Your Turn!

How to contact me:

Eric Vallo

[email protected]

@ericvallo

Please remember to complete your session evaluation

Page 104: Bihanaeu2016 vallo analyticsendtoend

103

SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of SAP SE (or an SAP affiliate company) in Germany and other

countries. All other product and service names mentioned are the trademarks of their respective companies. Wellesley Information Services is neither owned nor controlled by SAP SE.

Disclaimer

Page 105: Bihanaeu2016 vallo analyticsendtoend

Wellesley Information Services, 20 Carematrix Drive, Dedham, MA 02026 Copyright © 2016 Wellesley Information Services. All rights reserved.