What Is Your Analytical Maturity Level?
If your analytical maturity capabilities are falling short, it’s likely due to one of the following common obstacles:
You don’t have
a data strategy
You are not focusing your resources on results
You are blind to easy growth opportunities
You didn’t ask the tough questions of your business
You forgot about
the impact of dirty
You need an analytic coach to move ahead data
It is about time to know where your company stands.
DAECO's analytical maturity model allows you to solve these obstacles
Do you know what stage of
Analytical Maturity you are in?
Do you know what stage of Analytical Maturity you are in?
Nascent
The Nascent stage represents a pre–analytics environment. In this stage, most companies are not utilizing analytics except perhaps for spreadsheets. There is no real support for analytics, although there are pockets of people spread throughout the company who may be interested in the potential value of analytics and who may be trying out some analytics software.
Early
As the company moves out of the Nascent stage, it is starting to do its analytics homework. Staff may be reading up on the topic and perhaps attending webinars or conferences. One or more organizations may have invested in some analytics technology such as single instances of self-service data discovery and BI tooling, a database, a data mart, or a data warehouse for managed reporting.
Stablished
During the Established phase, the company is putting analytics tools and methodologies in place. The enterprise typically has a data warehouse and is utilizing it for reporting or dashboard needs. The company has moved toward self-service but is not necessarily data literate.
Mature
In an analytically mature organization, end users typically get involved, and the analytics transforms how they do business. For instance, users may change how decisions are made by operationalizing analytics in the organization.
Advanced/Visionary
Only a small percent of companies currently have visionary analytics. At this stage, organizations are executing analytics programs against a highly tuned infrastructure with well-established data governance strategies. Well-governed but flexible data access is available for users so they can explore data and develop visualizations in a self-service fashion.
On the face of it, the analytics maturity model is simply the progression of types of analysis an organization focuses its resources on. For example, a single descriptive analysis use case is not as valuable as a single predictive analysis use case.
Knowing what has happened is helpful, but not quite as helpful as predicting the future. This is the progression of analytics maturity. Each level ties directly to the types of questions we are trying to answer.
With DAECO's Analytical
Maturity model you can:
Create and develop a common language.
Establish a complete vision for analytical excellence.
Identify and address obstacles in your data analysis processes.
Understand the actual state of your organization’s level of analytical maturity.
Improve the governance of your data.
Increase the likelihood of action to achieve a competitive advantage.
Produce action plans to close performance gaps and improve maturity.
Determine where the organization stands on your analytical improvement journey.
Set clear objectives for future investments in performance improvement.
Advance to the next level of analytics maturity
Data has value when it answers business questions and triggers actions that generate profitability. We integrate data into the decision-making model and allow information to be an asset with value for your team and your projects.
Do you know which analytics you need?
Descriptive Analysis
Diagnostic Analysis
Predictive Analysis
Prescriptive Analysis
Summary
What
happened?
this happen?
What’s going
to happen?
What should
happen?
Function
It uses data mining and data aggregation to discover historical data
It examines causes of trends to help companies better understand variations in performance and customer behavior
It looks at historical data and analyzes past data trends to predict what could happen
It takes the conclusions gleaned froms descriptive and predictive análisis and recommends the best future course of action
Pros
It’s easy to employ in daily opertations. Little experience is needed
It enables bussiness to make more-informed decisions about how to solve problems and drive continued success
It’s a valuable forecasting tool
It offers critical insights into the best, most informed decisions.
Cons
It offers a limited view, and doesn’t beyond the data’s surface
It focuses on historical data; it can only help understand why events happened in the past
It needs lots of historical data to work. It will never be 100% accurate
It requires a lot of past data and often cannot account for all posible variables
Do you know which analytics you need?
Descriptive Analysis
Summary
What happened?
Function
It uses data mining and data aggregation to discover historical data
Pros
It’s easy to employ in daily opertations. Little experience is needed
Cons
It offers a limited view, and doesn’t beyond the data’s surface
Diagnostic Analysis
Summary
Why did this happen?
Function
It examines causes of trends to help companies better understand variationsin performance and customer behavior
Pros
It enables bussiness to make more-informed decisions about how to solve problems and drive continued success
Cons
It focuses on historical data; it can only help understand why events happened in the past
Predictive Analysis
Summary
What’s going to happen?
Function
It looks at historical data and analyzes past data trends to predict what could happen
Pros
It’s a valuable forecasting tool
Cons
It needs lots of historical data to work. It will never be 100% accurate
Prescriptive Analysis
Summary
What should happend?
Function
It takes the conclusions gleaned froms descriptive and predictive análisis and recommends the best future course of action
Pros
It offers critical insights into the best, most informed decisions
Cons
It requires a lot of past data and often cannot account for all posible variables
