Thu. Oct 28th, 2021
Analytics and AI

This new book brings managers up to speed on the business potential of analytics and artificial intelligence (AI), and how to gear their organisations up for change and innovation.

Analytics and AI
Analytics and AI

Analytics and AI- Startups and enterprises looking to harness the power of exponential technologies can find useful checklists, frameworks, and case studies in the new book, AI and Analytics: Accelerating Business Decisions by Sameer Dhanrajani.

“AI is the natural evolution to changing genres of sophisticated analytics, further strengthened by an algorithm economy,” Sameer begins. “AI is rapidly becoming the vehicle to address and solve the most pressing societal problems related to healthcare, security, education, and allied areas. This makes it akin to the next Industrial Revolution itself,” he adds.

Sameer is the Chief Strategy Officer at Fractal Analytics, and was earlier at Cognizant Technology Solutions and Genpact. The book is drawn from his 150 blogposts over five years, but the book could do with a lot more editing to remove repetitive material. The painfully small font makes it difficult to read across the 372 pages, and many figures and charts are illegible.

Here are some of my key takeaways from the book in terms of trends, impacts, organisational change, startup players, and future challenges. See also my reviews of the related books Big Data Revolution, Big Data @ Work, and Life 3.0.


The book begins by identifying key trends in this space: industrialisation of analytics; data collection and monetisation; analytics-led business transformation; and rise of the algorithm economy in areas like behavioural prediction. Market forces drivers for analytics are technological advancement, rise of consumerism, data explosion, and competitive pressure.

Depending on the level of maturity with respect to reactive, tactical, and strategic use of analytics at the local or enterprise-wide level, companies can be classified as laggards, amateurs, practitioners or masters. Analytics in mature organisations is business-user friendly, and grows beyond the purview of CTOs and CIOs. In a data-driven economy, data sits at the core of every business model, leading to real-time insights and agile decision-making.

AI is powering new models of assisted intelligence (in clearly-defined, rules-based, repetitive tasks, for example in Gmail), augmented intelligence (for example, clinical decision support; Netflix recommendations based on individual and group patterns), and autonomous intelligence (for example, facial recognition, driverless cars, automated trading).

Transformation 1: Analytics

The journey to analytics transformation is not a sprint but a marathon, Sameer cautions; it involves technology, process, culture and leadership change. Just gathering lots of data is not enough; many companies have large volumes of “dark data” in silos.

The transformation calls for unification in approaches across divisions and functions, internally and externally. Companies should think big, frame the right questions, craft a data agenda with effective quality and governance, and embed insights at the right touchpoints to deliver immediate value, Sameer advises.

Effective “data horsepower” depends on data quality, storage, assimilation, sanitisation, harmonisation, and visualisation. Data readiness should be followed by technology and business readiness. Security and privacy of consumer data should also be respected.

Close cooperation is needed between the data management team, data modelers, data scientists, visualisation experts, domain experts, and business unit heads. Early initiatives can focus on projects and tactics, then scale up to strategy and partnerships.

In the age of the predictive enterprise, the “data deluge” should be converted to a data monetisation engine. Data is worth monetising if it can predict behaviours, lead to actionable insights, refine understanding of existing and potential customers, and make a decision with precision.

Sameer offers a 2X2 matrix comparing data maturity and analytical maturity to decide whether corporate decision-making is gut-based, reactive, tactical or forward-looking. Speed and quality of response are important if they can prevent fraud or failure, reduce customer waiting time, and improve resource utilisation.

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Article Credit: YS


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