by Eric Dycus
TDWI Analytics Maturity Model: The Context for Benchmark Scores
To help determine how you are doing at Analytics, consider the following. Analytics is not just about using tools to help you gather and analyze the data. To be successful and repeatable you need to consider the administrative aspects of the process, like governance, culture, and trust. Below is a model from TDWI that are some key elements to help you measure if you have these areas in place in your organization.
The world of analytics is becoming more and more complex. You see business decisions happening all around you and you are wondering how I can compete in the market. Data maturity takes years to develop. Where do I get started?
First, let’s discuss the maturity model.
As organizations mature, they should be getting more value from their investment
This is the spreadsheet era and people make decisions based on past experience and instincts.
Someone or a small group of people are pushing to use data and analytics to make decisions. investment decisions are being made and people are starting to learn and get trained. Popularity is growing.
Initial investments have been made. Warehouses are being used for reporting and dashboards. Business decisions are being made de the data.
Organizations have a hard time going from Established to Mature. They get stuck in a loop of just staying in established. Items that keep you from reaching maturity are getting the right skills, cultural resistance to change, and complexity. When you reach this stage, you have to fully commit or stay at established. More investments and vision are needed to move to maturity.
End users are involved, and analytics has transformed how they do business. You are not only collecting the data, but you have wisdom on how to apply the data.
Only a small percentage get to this stage of visionary analytics. Your data is well-governed but flexible enough for users can create their own visualizations. There is excitement and energy about analytics.
This model will help you provide a way to help you assess where you are in your analytics journey. It will hopefully give you some ideas on what you can do for the next step of your journey. The full article by TDWI has 52 questions across five categories including scoring. I want to encourage you to read it if you want more detail.
Sources: TDWI: Fern Halper PH.D.
Some other items you might find interesting
Trends:
Self-Service across the analytics life cycle. All areas on the analytics lifecycle are becoming more and more self-service.
Artificial Intelligence and Other modern analytics. - Machine Learning and Natural Language Processing. Machine learning is starting to be used more for image classification and diagnostics. Natural Language Processing and Machine learning are being used together to understand customers’ sentiments and intentions.
Augmented Intelligence. Applications are coming preconfigured with Machine Learning and or Natural Language Processing so business users can utilize them.
MLOPS. A team that is responsible for deploying models and monitoring them. Skilled with newer open-source technologies such as Spark and Phyton.
Open Source. R and Python are the technologies being used for big analytics projects.
Unifying Data Platform. Organizations are looking for ways to unify their data. Some approaches include cloud data warehouse, data fabric with data virtualization. The key is to develop a high-quality source of data like a data catalog.
Sources: TDWI: Fern Halper PH.D.
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