Over the last 30+ years, I have told many stories like the following to explain the complexities and challenges of managing data that is needed to fuel the analytics engine. I find it interesting that the story stays the same while the words we use in the data and analytics industry change.
Whether it is decision support, business intelligence, analytics, data science, and now artificial intelligence, it is still all about the data.
Imagine this! You are standing in a room with a sea of tables, each table holding a different unsolved jigsaw puzzle and a pile of loose pieces yet to be connected. Each puzzle must be solved simultaneously while new puzzle pieces are streaming into the room from all angles.
As new puzzle pieces arrive, there is no obvious indication of which piece goes with which puzzle and you only have seconds to decide where a piece may fit into every puzzle in the room; some pieces may fit into multiple puzzles.
Moreover, while the pieces swirl around you, the colors and images of unsolved puzzles may change, resulting in a new expectation of what the puzzle should look like when completed, and new pieces may dislodge pieces that were previously a good fit!
You can use the above imagery as an analogy to describe the relationship between data and a retailer’s challenge to craft the desired customer experience and value during every interaction. Every jigsaw puzzle represents a single customer experience.
Each experience is unique and exclusive to a customer. Customer expectations and desires continue to evolve over time and across a spectrum of journeys, therefore making the uniqueness temporal and situational.
At times, the customer may want convenience and at times, discovery may be more important than the speed of the interaction. Regardless, all customers deserve the best possible experience, so the challenge for the retailer is to solve millions of unique customer experience puzzles simultaneously.
The Technology capabilities within any company are what make solving the room full of jigsaw puzzles – or delivering a customer experience that exceeds expectations – simultaneously possible.
A Technology team must be composed of talented associates’ who use customer insights and data to provide customers with a seamless experience where they can have anything, anytime, and anywhere. The common denominator that is the underpinning of an ecosystem that allows the technology, science, and organization to work together successfully is Data.
In the puzzle analogy, new puzzle pieces streaming in represent the constant influx of new data in a retail environment. New data could be a new fact, new assumption, or new derivative of a previous set of data. As new data streams into the environment, each piece of data may be applicable to one or more customer experiences.
New pieces of data such as availability of product on the shelf, wait time at the deli, spill in an aisle, proximity of an open parking space, availability of carts in the lobby, a change in the weather, accessible product information, product recalls, new promotions, and more may impact a single customer or a group of otherwise unrelated customers.
Each new puzzle piece (or piece of data) must be assessed for its impact on the customer experience and whether an action is warranted. If so, these actions must be done at a speed that matters, the speed of the customer experience.
Along the way, a customer’s expectations and objectives for their current shopping experience may change. We must expect that objectives and expectations are dynamic. Therefore, the expected image and color of the jigsaw puzzle in our analogy could change while we are putting it together.
New data creates new options and new decisions for the customer. As a result, the customer may desire and choose to look for a different experience mid-journey. For example, the customer stopped at the store to pick up a few items but then found something interesting and decided to spend additional time to do more discovery, and then they get a phone call again changing the journey.
Forming a solid underpinning of data does not happen by coincidence, rather, it is an intentional process based on a philosophy of “managing data as
an asset a product.” Think of managing data as an asset a product as in an inventory management system.
Like how we use inventory management systems to ensure we have the right product in place, understand the cost of bringing that product to market, understand products more vulnerable to inventory loss, and understand when we have too much product or no longer a need for the product.
Managing data as
an asset a product is an inventory management system for data where we know what data we have, where it is, if it is fit for use for distinct purposes, the risk associated with loss, and when it becomes obsolete.
In the past, data was slow and small – the number of jigsaw puzzles was few. Each puzzle had fewer pieces, new pieces trickled in and the expected solution to the puzzle was deterministic. Perhaps, we thought things were slow, small, and stable because we did not have the data to tell us otherwise.
It was not technically and economically possible to gather the data and derive insight. However, today’s world is much different. It is composed of a sea of jigsaw puzzles and swirling puzzle pieces, data has become fast and large.
A “just in time” data inventory management system is needed to gather the essential data to provide insight into customer value in real time and at the speed of every customer interaction.
That data inventory management system is composed of a complete and connected approach for validating data quality, understanding its providence, defining the valid uses of the data, chronicling the exceptions that occur over time, and maintaining awareness of the impact of external change on data, as well as the risk associated with loss of data.
data science decision support/business intelligence/analytics/data science/artificial intelligence are heralded as a competitive advantage for organizations in every industry, those that are responsible for the maintenance and care of data become more and more vital to every organization.
After all, we will always have data and it will endure without science or software. If data is not understood and managed well then it will be of no value. After all, the world was once flat due to a lack of data.
About the Author:
In semi-retirement, Dan currently serves as an adjunct professor at the University of Cincinnati, teaching a class that exposes graduate students to the traits, challenges, and advice of senior IT leaders.
In 2023, retired from his final role at Kroger as the Senior Director of Kroger R&D Labs at the 1819 Innovation Hub and as the unofficial Mayor of the 1819 Innovation Hub.
Over 40+ years, Dan has served 5 tenures at Kroger Co within IT software development teams, the reengineering department, data and analytics leadership, an internal startup, to R&D and partnering with the University of Cincinnati at the 1819 Innovation Hub.
He has been involved in the community over his career as one of the founders of the Cincinnati Chief Data Officers Roundtable, Chairing the Central Ohio CIO Data Management Group, Editorial Board member and Author for the CDO Magazine, a frequent panelist/speaker/author, and volunteering for the Free Store Food Bank Innovation Committee and an instructor for the local PMI Chapter. In 2023, Dan was the recipient of The Circuit’s Best of Tech Legacy and Social Impact Awards.