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Analytics in the Cloud

1. Executive Summary

Cloud-based applications have been mainstream since Salesforce.com brought customer relationship

management to the masses in the 2000s. Cloud implementations had the advantage of providing

faster time to provisioning and a significantly different cost structure from traditional software

implementations based on on-premises installations. However, analytics and business intelligence

in the cloud were slower to reach widespread acceptance. First, analytical and business intelligence

applications have different data schema implementations from traditional operational applications.

These applications can be uniquely configured for individual organizations and are often more difficult

to implement on a mass basis than they might be for an operational system.

To determine the status of Analytics and Business Intelligence in the Cloud, Enterprise Management

Associates (EMA) embarked on an end-user research study to look at the current state of cloud-

based analytics. For this research, EMA invited pre-qualified business stakeholders and information

technology professionals to complete an extensive web-based survey. As part of the survey, 257 panelists

responded to an invitation to provide their insights on cloud-based analytics and business intelligence

strategies and implementation practices. To offer a neutral enterprise view, the respondent pool was also

balanced. Business stakeholders represented 44% of respondents. Technologists were 56% of the panel.

The survey was executed in November 2014 with respondents from around the world including North

America and Europe.

As part of the study, survey panelists were asked to identify the depth and extent of their participation

in cloud-based strategies for analytics and business intelligence. More than 32% of respondents

indicated that they had adopted cloud-based strategies and those strategies were an important part of

their business. Another 24% of respondents indicated those strategies were adopted and essential to

their business. This places a majority (56%) of the EMA panel into an extensive cloud-based strategy

category or classification.

EMA panelists were asked to share the industry with which they identify. A wide range of industries

was included in the survey panel with eight separate industries representing at least 6% of the panel

respondents, including manufacturing, finance, retail, and health care. Looking at industry segments

based on their self-identification associated with their cloud strategy, the retail industry segment has a

significant percentage associated with an extensive cloud strategy, closely followed by utilities providers

and public services.

Key components of cloud-based analytics and business intelligence strategies are the implementation

attributes of cloud-based analytic projects. These projects are the embodiment of the organization’s

budgets, financial drivers, and technical requirements. Their goal is to meet the objectives of the

business stakeholders and line of business departments who will ultimately be the data consumers of

these analytical applications.

EMA panel respondents were also asked about the depth of their implementation experience with cloud-

based projects. Organizations reporting a limited number of projects are still attempting to understand

how cloud-based solutions for analytics impact their organization and how they can establish and

implement best practices. While a larger number of projects can indicate that an organization fully

realizes the strengths of cloud-based implementations, this level can also indicate that the organization

has established a mature approach to those projects and may have created a center of excellence to

manage and advise on those projects. Approximately 18% of EMA panel respondents indicated that

Analytics in the Cloud

either one or two projects were associated with their cloud-based analytical initiatives. Over 41% of

respondents said they had three to four projects within their organization. The remaining 40% indicated

their organizations had over five projects associated with their cloud-based analytics strategies. In total,

over 800 individual projects were detailed by the 257 respondents, which is an average of just over three

implemented projects per respondent. A scale of implementation maturity was established based on

project implementations with Robust, Maturing, and Early Stage levels.

Looking at the overall project sponsors for the implementations above, information technology

stakeholders are the primary sponsor. The next four sponsors, or line of business stakeholders, by

percentage—Sales (14.2%), Finance (13%), Human Resources (10.3%), and Marketing (10.2%)—

have significant influence on the implementation of cloud-based projects. This finding is reflected

in the type of project goal and objectives associated with individual projects. Sales needs insight into

sales analytics projects. Finance desires to have controls and visibility into risk management projects.

Marketing requires actionable intelligence into the activities associated with cross-sell/up-sell. As

organizations become more mature with their implementations, line of business stakeholders have an

increasing impact on project sponsorship. For organizations at the Robust level of cloud implementation

maturity, corporate executives have the most influence.

Various options for the implementation of a cloud-based analytical environment are available, whether

it be a data warehouse, data mart, discovery environment, or data integration platform. This includes

infrastructure as a service (IaaS), platform as a service (PaaS), software as a service (SaaS), and managed

services. Each of these options has value to an organization implementing cloud-based infrastructure.

IaaS not only allows organizations to maintain control of their infrastructure, but to move the physical

location and administration of the underlying hardware outside the data center. PaaS provides the

opportunity to continue the encapsulation of technical implementation aspects from their development

and implementation teams. SaaS allows for the complete encapsulation of implementation and allows

an organization to focus on operation of the environment. Managed services move all of the operation

and administration elements to a third party and provide an organization with the opportunity to focus

on the value that comes from the functionality being “outsourced” to the service provider.

All of these components come together to provide an excellent view of cloud-based analytics and

business intelligence strategies around the globe in terms of strategy, project implementation, and

horizontal infrastructure.

1.1. Key Findings

• Cloud-Based Strategies Are Important – 56% of respondents have identified their organization

as having cloud-based analytics as Currently Adopted and Essential or Currently Adopted and

Important in their organization.

• Not Just A Single Project – Over 40% of organizations indicated they had over five projects

associated with their cloud-based analytics strategies.

• Locking Data Down – Security was the single most critical component (54.5% of respondents) to

cloud-based analytics implementations, according to panel respondents.

• Speed and Dependability – Outside of Security, respondents ranked Reliability, Performance, and

Costs as the most critical components for the cloud-based analytics implementations. Developer

Support, Manageability, and Self-service and Vendor Brand were, relatively, the least critical

components.

Analytics in the Cloud

• Cost Certainty and Length of Engagement – Organizations prefer to utilize annual or multi-year

subscription agreements with their cloud service providers. Most often vice presidents will approve

this type of expense, but approval is moving “downstream” to lower levels of the organization.

• Budgets are Expanding – Over 56% of the respondents indicated that their budgets fell within

a band of $1 million to $25 million on an annual basis for 2014. Over a quarter of respondents

indicated that this was an increase of 10-25% over 2013.

• Line Of Business Is Bringing The Checkbook – Over half of organizations indicated that they

were receiving funding from sources outside of the IT department budget. These line of business

contributors were most likely to contribute 21-25% of the cloud-based analytics and business

intelligence budget.

• Businesses Want Speed to Value, Not Time to Heartbeat – The primary business driver is to

decrease the time to delivery of analytical and business intelligence. Most important is Improved

Speed to Implementation on Analytical Projects (16.5%). The second is Adaptable/Flexible

Implementations (15.7%) associated with cloud-based analytical initiatives.

• Technical Agility Drives Requirements – Aside from Data Security, the most important Technical

Drivers are time-to-value for cloud-based analytical initiatives. Improved Technical Agility (15.2%)

and Improved Software Availability (13.4%).

• Leading Project Objectives – Sales Analytics (19.3%) was the leading Project Goal for organizations

implementing cloud-based analytics and business intelligence. Risk Management (15.1%) and

Marketing Analysis (13.1%) are ranked second and third.

• Who Is Sponsoring Projects – Line of business departments, Sales (14.2%), Finance (13.0%),

Human Resources (10.3%), and Marketing (10.2%), all have significant influence on the cloud-

based analytics projects implemented by the survey panel.

2. Business Intelligence and Analytics in the Cloud

Cloud-based applications have been mainstream since Salesforce.com brought customer relationship

management (CRM) and sales operations to the masses in the early 2000s. Cloud implementations had

the advantage of providing faster time to implementation and a significantly different cost structure

from traditional software implementations based on on-premises data center installations.

However, analytical and business intelligence installations in the cloud were slower to reach widespread

implementation and acceptance due to several factors. First, analytical and business intelligence

applications have vastly different data model implementations from traditional operational applications

such as CRM or enterprise resource planning (ERP). These applications can be uniquely configured for

individual organizations and are often difficult to implement on a mass basis than they might be for an

operational platform.

Next, the configuration of the “front end” of business intelligence platforms such as reports, dashboards,

and self-service data discovery components often do not follow a standard process. Each organization and

department within the organization may have individual configurations based on their business model

and/or individual analytical requirements. Again, this type of individualized configuration does not lend

itself easily to implementation on a mass customization basis favored in cloud-based infrastructures.

Analytics in the Cloud

Finally, the amount of information passing between source systems and analytical platforms makes

security of information in transit to a cloud-based infrastructure and within that cloud-based

infrastructure a much larger issue than those of strictly operationally-based applications. The high

volume of data comes from the fact that analytical applications generally have much larger datasets than

those associated with operational platforms. The increase in overall data usage increases the likelihood

that a security issue may occur.

2.1. Cloud-based Strategy and Maturity

How organizations look at cloud-based strategies is important. For organizations that embrace

cloud-based approaches, there are a number of opportunities to expand their processing, storage, and

distribution options beyond their on-premises data center. For those that do not adopt cloud-based

strategies, there are fewer options.

As part of the 2014 EMA Cloud-Based Analytics and Business Intelligence study, survey panelists

were asked to identify the depth of their strategies on cloud-based strategies for analytics and business

intelligence. More than 31% of respondents indicated that they had adopted cloud-based strategies

and those strategies were an important part (Currently Adopted and Important) of their business.

Another 24% of respondents indicated those strategies were Currently Adopted and Essential to their

businesses, placing 56% of the EMA panel into an extensive cloud-based strategy.

Cloud Strategy

0% 5% 10% 15% 20% 25% 30% 35%

Percentage of Respondents

Cloud Strategy

Scattered Clouds

Partly Cloudy

Full Cloud Coverage

The remaining 44% of EMA panel respondents were distributed into the Currently Adopted and

Supplemental, Planned for Adoption, and Being Researched categories. These categories are banded

into the following cloud strategy segments.

• Full Cloud Coverage – This category encompasses the Currently Adopted and Essential and

Currently Adopted and Important strategy categories and is meant to identify those organizations

that have fully embraced cloud-based strategies as part of their business.

• Partly Cloudy – This category encompasses the Currently Adopted and Supplemental and

Planned for Adoption strategy categories. The Partly Cloudy strategy represents organizations

that have made the initial steps toward the implementation of a cloud-based strategy.

Analytics in the Cloud

• Scattered Clouds – This category encompasses the Being Researched and Not Planned for

Adoption¹ strategy categories. Scattered Clouds denotes organizations that are still in the midst of

making a decision on their implementation of cloud strategies.

Associated with the vision associated with cloud-based analytical and business intelligence strategies is

the actual implementation of those strategies. EMA panelists were asked about their individual cloud-

based projects for analytics and business intelligence. Through these projects, you can see the maturity

of cloud-based implementations as an extension of the panelists’ cloud-based strategies.

Organizations reporting a limited number of cloud-based analytical or business intelligence projects

show that they are still attempting to understand how cloud-based solutions for analytics impact their

organization and how they can establish and implement best practices. A large number of projects can

indicate that an organization fully realizes the strengths of cloud-based implementations. This level can

also indicate that the organization has established a mature approach to those projects and may have

created a center of excellence to manage and advise on those projects.

Number of Projects

1 2.6%

2 15.5%

3 21.0%

4 20.5%

5 - 7 17.8%

8 - 10 12.5%

11+ projects 10.1%

0% 2% 4% 6% 8% 10% 12% 14% 16% 18% 20% 22%

Percentage of Respondents

Cloud Maturity

Robust

Early Stage

Maturing

Approximately 18% of EMA panel respondents indicated that either one or two projects were associated

with their cloud-based analytical initiatives. Over 41% of respondents said they had three to four

projects within their organization. The remaining 40% indicated their organizations had over five

projects associated with their cloud-based analytics strategies.

These individual project indicators are banded into the following Cloud Maturity segments.

• Robust – This segment incorporates the 5-7 Projects, 8-10 Projects, and the 11+ Projects

categories. It designates those organizations that have fully embraced cloud-based implementations

just as an organization whose strategies implement the Full Cloud Coverage strategy category.

• Maturing – This includes the 3 Projects and 4 Projects categories. The Maturing segment and the

Partly Cloudy strategy characterize organizations that have cloud-based strategy that is developing

and sets the stage for entry into the Robust maturity segment.

It should be noted that EMA panel respondents who did not plan to adopt cloud-based strategies (Not Planned for

Adoption) were not included in the overall survey panel as a qualification requirement. Those panelists are not

represented in this research or the associated results.

Analytics in the Cloud

• Early Stage – This segment contains the 1 Project and 2 Projects categories. Scattered Clouds

and Early Stage indicate an organization that is working through the initial components of cloud-

based strategy and implementation.

2.2. Mature Features and Functions

There was a time when utilizing a cloud-based platform meant that compromises relating to functionality

and features were required. However, that time is behind us. Cloud-based implementations of analytical

and business intelligence platforms have matured to the point where both in terms of feature/function

lists and end-user sentiment they are on a par with their on-premises licensed counterparts.

In terms of platform value and high level architecture, members of the EMA survey panel indicated that

cloud-based platforms had a significant advantage over on-premises solutions. Cloud-based platforms

lead in all areas of these core components.

On-Premises vs Cloud-based

Total Cost of Ownership

Technical Distribution

Time to Implementation

Functionality

Ease of Adoption

Percentage of Respondents

Implementation Preference

On-Premises is Better

They are the Same

Cloud-based is Better

EMA survey respondents indicated there is parity between On-Premises platforms and Cloud-based

implementations for Total Cost of Ownership and Technical Distribution. This parity of platform

types is even stronger than the end-user opinion about the value of on-premises platforms in these areas.

With cloud-based platforms starting to be a favored implementation strategy for high-level platform

value, the question becomes:

How do end-users view individual components of platforms as part of their importance to a cloud-based

platform?

The following graph shows the overall importance of individual features to a cloud-based implementation.

The bars that trend to the right side of the graph indicate a higher importance to end-users of the

implementation of features for cloud-based solutions.