A recent HBR article, “Why Do Chief Data Officers Have Such Short Tenures?” by Thomas Davenport, Randy Bean, and Josh King, highlighted the Chief Data Officer’s (CDO) “Data-to-Business Innovation” challenge. The main charter for the CDO is to accelerate the Data-to-Business Innovation flywheel; to guide the business in becoming more effective at leveraging data and analytics to optimize its key business and operational use cases.
Therefore, the golden principle to be at peace with these irritants in your life is simple: Simplify it.
A Business Discipline consists of systematic research, observation, measurement, and experimentation resulting in the assimilation of learnings into laws, theorems, concepts, principles, practices, frameworks, and formulas to enable the consistent application and ongoing enhancements from the real-world application of that discipline.
In Part 1 of the “Building Blocks for Modern Data Management”, I explored two important modern data management concepts: Data Subassemblies and Data Products (Figure… Read More »Data Subassemblies and Data Products Part 2: Economics and Journey Maps
Complexity arises from permutations, and to achieve long-term goals, it is often necessary to determine which permutations are both achievable and desirable.
I believe that there are two key modern data management “products” required to transition data management into a business discipline focused on helping organizations accelerate their data-driven business innovation. One of those “products” – Data Products – is already gaining wide acceptance as a way for organizations to monetize their customer, product, service, and operational insights or predicted behavioral and performance propensities.
On the other hand, studios produced a different kind of product – entertainment. Superficially, the studio model looks more agile than either the factory floor or the corporate floor, with a certain degree of experimentation and iteration, especially early on in the process. Yet there are critical differences as well.
In 1974, two distinct but interestingly similar milestones were achieved that would greatly affect the lives of data engineers: the Rubik’s Cube was invented, and IBM released the first relational database. Since its original rise in the 1980s, the Rubik’s Cube has become the world’s most popular puzzle toy.
Before embarking on the data profiling exercise, an analyst must prepare by going through a data profiling analysis.
I love this infographic recently floating around LinkedIn. Sorry, don’t know to whom to give credit, but it does provide an interesting depiction of how senior management thinks AI works and the realities of what’s required to make AI work.