Overview of the Data Analytics (AI/ML) ROI 101 series of articles

Why data analytics (AI/ML) ROI do not show up?

I have over 50 years of professional experience in business, data management, data science, information systems and value management consulting. In recent months, I have encountered a large number of articles that report lack of returns on investments (ROI) made by companies in AI and related technologies. But, in reality many organisations are achieving significant return on investments in data analytics (AI/ML) which I encountered during my business value consulting engagements across tier-1 organisations internationally. In my experience I found that the true ROI are not apparent for the following primary reasons:

• shortcomings in the traditional financial accounting principles, practices and standards that are not conducive to modern day treatment of data analytics (AI/ML) as a renewable long-term asset which blindsides C-level executives as to their true value,
• confidentiality and competitive differentiation that prevent companies from publicly sharing value gained from their investments,
• shared infrastructure across business divisions and functions, and agile development methodologies such as DevOps, DataOps, ModelOps, AnalyticsOps that continually change the data analytics ecosystems which make it difficult to measure the before and after states of systems in order to ascertain productivity, efficiency and effectiveness benefits,
• lack of maturity in managing data analytics (AI/ML) programmes and projects within the enterprise, and
• lack of value governance.

What is the value of employees and their skills?

Similar difficulties arise in recognising the value of people and their skills as asset in the enterprise, which poses dilemma for executives whether to invest in employees who possess real intelligence or to substitute them with artificial intelligence technologies for automation and autonomous decision making. Along with Dr Jayanthi Raman, who is an academic researcher and clinical cognitive psychologist with expertise in bridging the worlds of human and artificial intelligence, we recently published the book, “Redefining Value Where People and AI Meet”. This book provides an interdisciplinary framework that blends business, cognitive psychology, data science and value governance along with methods, techniques, tools and industry case studies/use cases for effective management of business value.

How the readers will benefit from this series

Since publication of the book, many people have expressed interest in understanding the foundational concepts behind the various topics covered in the book. This series on Data analytics (AI/ML) ROI 101 is intended for and I hope will be of interest to the following groups of readers who may benefit from the articles.

Data analytics (AI/ML) strategy development teams, and investment oversight and audit teams will benefit from economic theory of value that apply to data analytics along with the narrative on the unique characteristics of data as a renewable resource and asset that illustrate the stark contrast to the traditional treatment of data analytics as physical assets in the accounting books including the balance sheet. They will also find the basis of the benchmark values across business functions and divisions in industry case studies that are covered in the book beneficial. Additionally, the know-how on sweating data analytics assets to do more with less and to grow value will also be very useful.

Business analysts, data scientists, and data analytics architects and engineers will find the business outcome focused concepts such as organisational value chain, digital value chain and data analytics value chain covered in this book very handy to complement and bridge their subject matter and technical expertise with the perspectives and perceptions of business stakeholders. Besides this, the articles may help them in developing their career progression towards the sought after emerging roles in the industry such as Business Value Advisor, Business Value Consultant and Business Value Engineer etc. They will also find the foundational concepts, topics and terminologies related to accounting as well as financial statements useful in transitioning their technical capabilities to align with business outcome.

Students and young professionals will benefit from exposure to the various technical, business and financial aspects of data analytics which may help pave way in their career planning.

Data analytics focused sales or sales support professionals will get an appreciation of the consultative and outcome focused value selling approaches, instead of product centred features and functions selling to differentiate their software solutions.

What is next?

In the next article I will share with you why focus on value, perceptions of value and some of the differences in micro and macro economics and theories of value concepts.