Data Governance and Analytics Corporate Manager at GRUPO ELCATEX

In this modern and changing world where Data is one of the most valuable asset and not treated with the importance it deserves wide across the organizations, is often to think at first in trends like Advanced Analytics, AI, ML, and many other techniques without the awareness that those techniques are feed with Data at a purest form, but behind that we as a D&A leaders must be aware of some things are needed to solve before seeing the value of implementing this solutions, and in this article I’m going to give the readers a view from my experience in the world of Digital Transformation and Automation.

Build team with the business

“It is dangerous to go alone…” I would like to start with this phrase not because of the tool we need to this journey, however there is no superhero in building a successful Data&Analytics culture, for this, we need a team, a heavy load sponsor, if it is the CEO the best, and an evangelist to spread the culture over the organization, this is more like a team play than a solo play, and in order to build this team, they must be convinced of the value of data and patience for the results that sooner than later will come.

It is important that even if we are not delivering value from day one, all the business areas must be aware that step by step the good results on one business area will impact in a positive way to the other business areas, in the end the Data is an ecosystem across all the Enterprise Architecture, the bet is to build a domino effect.

A few tips I would like to share from my experience in Banking, Retail and Manufacturing:

CIO Review D&A Article 1

Build a common voice for the data terms based on business language and avoid technical word when possible.

Name an evangelist and a steward for data governance over every business unit.

Build a check and balance system to data onboarding over all the Enterprise Architecture.

Keep it simple, do not overcomplicate the process and dictionaries.

Prepare the low hanging fruits

Once you have assemble the team and a very basic governance process over the people around the data it is time to take actionable, for this a process of demand management is needed, You need to build a business case with projects prioritized based on business value created for the initiatives, for this low hanging fruits you can select cookie cutters use cases like market basket analysis, transaction classification, Credit Scoring, or any other of the most common used cases for Advanced Analytics.

Then you can select a process for managing the projects, in my experience I’m familiar and comfortable with CRISP-DM, and Scrum a Multi-disciplinary team is needed to archive success, if you follow my previous advice then you should already have your allies for this venture. Pick a basic set of metrics like time to market, accuracy of the model, build a confusion matrix and measure the impact of your projects, you should have enough numbers to convince the organization that value can be created by using Analytics.

In my case as a Data Governance and Analytics Manager at Elcatex an Textile Manufacturing Company I use Knime for Data modeling and Microsoft Synapse as our Enterprise Datawarehouse, this provide an Hybrid architecture footprint to cover the on premises data bases and expose the result of the models with the power of the Cloud.

A few tips I can provide are:

Based on the dictionary of business terms use it as a Catalog to transform IT related terms to business terms and store them in your EDW/DL in that way.

Select a tool for Data Mining, in an early stage You need to do a lot of fine work on Extracting/Transforming the data to make it useful for Advanced Analytics.

Build a project portfolio based on a business case with a clear metric to deliver value.