In this article, we are going to review the elements of a good Analytics Planning Framework and how Analytics Planning is part of Data Product Ownership in the Data Mesh.
What is Analytics Planning?
As part of any CDO or CDAO role there is both Data and Analytics Governance and a process for ensuring that analytics and insights are generated from the right data to solve a variety of business problems.
To make sure that Data Products (i.e., dashboards, insights, commercialized analyses, etc.) in the Data Mesh are fit for purpose, the business and analytics problem framing must occur to have workable high-impact solutions.
Analytics planning and next-generation analytics are helpful to a variety of stakeholders – Chief Data Analytics officers, Chief Data Scientists, Heads of Marketing Analytics, and Heads of Digital Analytics.
Many times, Data Analytics is a Center of Excellence, and therefore is vital for the professionals in the COE to have a seat at the table whether that is with a Data Product Owner, a tribe lead, or a business person. This linkage and relationship are vital, not only from a relationship management standpoint but to enable the right Data Mesh design by helping to identify the right analytics and data products. The goal is to get the data needed to improve business decision-making and monetization.
What type of meeting or committee does Analytics Planning require?
Analytics Liaisons and Data Stewards from the COE should meet with Data Product Owners and business people in what I call Data Analytics Governance meetings where the types of analytics and data products are discussed. This is a “seat at the table” meeting among business partners to discuss the appropriate types of proactive analytics that would drive problem solutions and business impact.
Data Analytics topics to be discussed include:
These leadership meetings should occur at least quarterly. Monthly (or more frequent) reviews should occur at the project team level. Typically, Data Analytics functions can have hundreds or thousands of projects depending on the number of business partners.
What is the business purpose of these Planning meetings?
For Analytics or Data Products to be fit for purpose, you will want to review the partner’s business strategy as well as any P&L drivers where Analytics might have an impact:
Data Analytics Governance creates a prioritization process.
The prioritization process could include business ROIs, GCOs (Good Customer Outcomes), or other metrics to determine what gets worked on first. Are these projects high priority, medium, low, strategic, or even non-negotiable? (Non-negotiable might mean compliance projects which means the Data Analytics team must carve out bandwidth to create new data pilots/new analytics pilots. Pilots could include identification of new segments or new scoring systems based on transaction data and more.)
Data Analytics Planning – it all goes back to Business Problem Framing.
What is the number one reason analytics fail? We hopefully all know this, but it is worth mentioning again – the #1 reason analytics fail is due to a failure to frame the business problem correctly.
What type of problems may clients mention to the Data Analytics team during the quarterly check-ins?
Analytics Problem Framing: Choosing the type of analytics method to solve the problem.
Let’s review the categories of analytics that may be part of the discussion during the analytics planning meeting with the business and product owners.
So that’s a little bit about how to match the business problem to the type of analytics. The next step would be for the Analytics Leader or Analytics Liaison to work with the data product owner or business lead to provide an endorsed quarterly data analytics plan which would also identify data needs in order to perform the agreed-upon analytics.
What are the elements of the Analytics Plan?
Given the Data Mesh puts a higher degree of quantitative skills on business partners, it is imperative for all stakeholders to have a better understanding of data, analytic methodologies, and execution. Training and knowledge maturity is critical.
I hope this post helps fill in some of the planning gaps in the Data Mesh concept and shows how Analytics Planning can inform what the data product owners can work on and how an ongoing engagement and governance model can be established to benefit both the analytics team as well as the business as a whole.
What has your experience been with data analytics planning in the data mesh? We look forward to hearing your thoughts.
Dr. Tony Branda
Your Bio : usp_custom_field : Anthony Branda (Tony) Tony was most recently the Group Chief Data and Analytics Officer for Ahli United Bank. Previously, he was CDAO for ASB Bank Limited -- a wholly owned subsidiary of Commonwealth Bank of Australia, CAO for Citibank North America and CDO for Embrace Home Loans. Tony was Clinical Professor of Marketing and Customer Intelligence at Pace University’s Lubin Graduate School of Business for 9 years. Published author in the Journal of Marketing Analytics, Journal of Applied Marketing Analytics and CIO/MIT Sloan Magazine. Tony’s business expertise includes Artificial Intelligence (AI), business analytics, data science and engineering, CRM, marketing analytics, and customer intelligence as a business strategy. He holds an M.B.A and a Ph.D. in Marketing Analytics from Pace University. He holds a Certificate from MIT Sloan in AI for Business Applications. Tony co-founded an analytics community called the Analytics Hall of Fame, am award and recognition online community.
Tony was most recently the Group Chief Data and Analytics Officer for Ahli United Bank. Previously, he was CDAO for ASB Bank Limited — a wholly owned subsidiary of Commonwealth Bank of Australia, CAO for Citibank North America, and CDO for Embrace Home Loans. Tony was a Clinical Professor of Marketing and Customer Intelligence at Pace University’s Lubin Graduate School of Business for 9 years. Published author in the Journal of Marketing Analytics, Journal of Applied Marketing Analytics, and CIO/MIT Sloan Magazine. Tony’s business expertise includes Artificial Intelligence (AI), business analytics, data science and engineering, CRM, marketing analytics, and customer intelligence as a business strategy. He holds an M.B.A. and a Ph.D. in Marketing Analytics from Pace University. He holds a Certificate from MIT Sloan in AI for Business Applications. Tony co-founded an analytics community called the Analytics Hall of Fame, an award and recognition online community.
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