How to Create a Great Report using Google Analytics in 6 Steps

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How to Create a Great Report using Google Analytics in 6 Steps

Just 6 Steps to create a great Analytics report using Google Analytics or any other software tool.

Company teams that want to create a great report are likely to be simultaneously working in one or more of the steps to execute analyties work in any project-especially in globally distributed companies that have matrixed organizations working on multiple concurrent projects. These major 6 steps  support them by providing the input necessary for the next phase to continue.

Step 1: Understanding what to analyze

The first phase requires gathering business requirements and goals, understanding the current data environment and the data within it, and developing a plan to execute work

Step 2 Collecting and verifying data

The second phase involves determining if data is available and accurate and, if not, making it so. This work generally involves data engineering and implementing new data collection, data models, or databases. Data governance and master data management are implemented

Step 3: Dashboarding, reporting, and verifying

The third phase is when dashboards, reports, and other artifacts that show data are created, verified, and made available

Step 4: Analyzing, communicating, and socializing

During the fourth phase, the team analyzes data beyond the creation of reports and dashboards. Business questions are answered. Insights are generated. Analysts talk to stakeholders, meet with them, and ensure they understand the analysis. Narratives and stories are constructed and presented for discussion.

Step 5: Optimizing and predicting

The fifth phase is when the “data science” occurs and advanced analytical methods are applied and used to predict what could happen and recommend what should be done next. Data science is not magic and it’s very hard to apply in some cases.

Step 6: Demonstrating economic value

The sixth phase is when the analytical outcomes are gathered, and the business impact is quantified financially. The value of the analytics team and work is demonstrated by showing how it increased revenue or reduced cost.

 

Achieving successful analytical outcomes requires thinking of
analytics as a value chain. In my book Building a Digital Analytics
Organization, I postulate a phased approach to doing analytics, which
I called “the Analytics Value Chain.” I suggested this concept to
describe, in a simple way, how to think about the nature of analytical
work from a managerial perspective. The value chain suggests a way
to abstract an activity-based categorization of what is generally
required, in most cases but not all, to do professional analytical work
The value chain answers the question, “What activities does my ana
lytics team need to do to ensure quality analytical output and out
comes?” The value chain envisions analytics as a set of phases that
suggest a logical flow for executing analytics work. The phases can be
recursive and you can enter a phase based on your capability level.
These activities in the value chain can occur in a linear sequence or
non-sequentially. Each activity could be carried out by an analyst, the
analytics team, or a supporting team. Companies are likely to be
simultaneously working in one or more of the phases to execute ana
lyties work in any project-especially in globally distributed compa
nies that have matrixed organizations working on multiple concurrent
projects or that have different teams executing analytical activities. As
such, the value chain phases support one another by providing the
input necessary for the next phase to continue. What’s important to
understand about the value chain is it suggests a starting point to
begin or points of entry to continue analytical work. It was created to
help analysts, managers, and leaders who are building or running ana
lytical teams to understand what to do to execute and to increase
maturity when building a team or capability. The Analytics Value
Chain is summarized into the following phases:

Understanding what to analyze: The first phase requires
gathering business requirements and goals, understanding the
current data environment and the data within it, and developing a plan to execute work

Collecting and verifying data: The second phase involvesdetermining if data is available and accurate and, if not, making
it so. This work generally involves data engineering and imple
menting new data collection, data models, or databases. Data
governance and master data management are implemented

Dashboarding, reporting, and verifying: The third phase
is when dashboards, reports, and other artifacts that show data
are created, verified, and made available

Analyzing, communicating, and socializing: During the
fourth phase, the team analyzes data beyond the creation of
reports and dashboards. Business questions are answered.
Insights are generated. Analysts talk to stakeholders, meet with
them, and ensure they understand the analysis. Narratives and stories are constructed and presented for discussion.

Optimizing and predicting: The fifth phase is when the “data
science” occurs and advanced analytical methods are applied
and used to predict what could happen and recommend what should be done next.

Demonstrating economic value: The sixth phase is when
the analytical outcomes are gathered, and the business impact
is quantified financially. The value of the analytics team and
work is demonstrated by showing how it increased revenue or reduced cost.

The value chain represents the specific phases and work per
formed by an analytics team. It suggests the types of activities that
managers want to align with when building organizations. It applies to
ecommerce analysis because it is generalized in nature and at a high
level. For ecommerce analytics, the value chain can be made even
more specific because of the targeted nature of the ecommerce work.
Thus, in this book, I suggest a new Analytics Value Chain: the Ecom
merce Analytics Value Chain, which represents the phases suggested
to deliver analysis in ecommerce environments.

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