https://www.datapine.com/blog/roadmap-to-a-successful-business-intelligence-strategy/

Aman Arora thank you for the link and added few comments below.

I'll add others details in the project and will make a case study.

I designed a Data Roadmap with almost the same logic and phasing but I split the work by data type repository:

• Etablishements / companies
• Legal / natural persons
• Products
• Plot (plot vs. pitch)

And I added other repositories:

• Tracking and sourcing (prospects, sales)
• Logs of applcications (API, servers etc.) to make analysis for several purposes (SEM, SOC, etc.)

1. Go into the process with eyes wide open

Yes, the more you have legacy (technical and functional Legacy) in an organization, the more you will need to make effort.

Adopt change management approach and tools with Agile framework could help.

2. Determine stakeholder objectives

You need to clarify if the Key Business Objectives (KBO) are clearly identified to sort and associate all the use cases per departement or stakeholder with their KPI, KRI, KQI.

And we often have business cases (marketing, sales, M&A strategy) at the beginning but in an non-profil organization it could be different.

This step will help to identify sponsor and have a more change for a better Return on Investment. It will often drive where you will create more knowledge of customer, products, etablishement etc..

3. Choose a sponsor

We started with the Marketing Chief Officer, the Chief Sales Officer, and the Chief Technology Officer too.

4. BI is not just a technology initiative

We can find easily few needs and use cases in an organisation but it's not always easy to have a clear requests or a real project.

so, it's often required to make workshop with a data squad and change management.

5. Employ a Chief Data Officer (CDO)

Currently I play this role but not with a full time an official Data position or title.

You can start with somebody who is interested or has competencies or habitilities to work with data

6. Assess the current situation

It' mandatory to make a good roadmap and integrate projects (data quality, goals etc.) or chosse framework (AGILE, Scrump, etc.). It's a good idea to identify people who could be data stewards to help to go deep in the data, undestand concepts, help to start to clarify terms, to change processes or the culture of the organisaton.

7. Clean the data

I work on it with several processes (data analysis, ETL, etc.) and tools.

8. Develop a "Data Dictionary"

I still made it, continue to maintain and develop it and shared it in a Data Stewards internal social network group propuled by Talkspirit.

But, I'll schedule to add it in our Intelligent Data Hub Seamrchy xDM where we can manage documentation on fields and now a ditionnary available in a REST API. https://www.semarchy.com/intelligent-data-hub/

9. Identify key performance indicators (KPIs)

for example we create a Data Quality Dashboard to have KPI of evolution of the quality.

You can have Key Performance Indicators, Key Risk Indicators, Key Quality Indicators to measure Return on Investment(ROI) of Lots, projects and roadmap.

10. Choose the right tool / partner for your business

It's a good practice to make a tender offer for each tool you need to choose (Data Hub or IAM/CIAM).

You can change you mind due to the knowledge exchange with the companies that you will add in your shortlist in the tender offers.

For example, you could start to think about:

(1) customer management in a repository,

(2) identify that you should use a Master Data Mangement (MDM) pattern and

(3) finally should choose a Muti-repository Data Hub.

11. Pursue a phased approach

You will have several projects in the roadmap.

It's better to start by a Proof of Concept / business / Value (PoC/PoB/PoV) and with the more valuable use case.

If you are ambitious enough try to explore the following framework: https://www.academia.edu/25204851/Engine_for_Virtual_Learning_including_tools_standards_and_new_learning_approach_according_to_Predictable_and_Unpredictable_Events, in case of the following keywords are not motivating please don't waste your time:

Semantics,

Experiential Learning (Active Learning),

Modeling and Simulation,

E-learning 2.0,

Technology-enhanced Learning

I created ediaudit tools to pilot online servers thanks to the upper tools, good luck