Data by Itself May Not Be Enough

I recently checked a local "big box" hardware store's website to see if an item I needed was in stock. When I received a positive confirmation, I drove to the store, only to discover that I could not find it. I asked a salesperson in the appropriate department to help me locate it. She could not find it either but remembered seeing it on a specific shelf the prior week. She bragged that if she couldn't locate it, no one else in the store could either, and it would be a waste of my time to ask anyone else as they would only then ask her. Consequently, I left the store without the item and ordered it from another vendor's website.

For Further Reading:

Location Intelligence and the Conquest of "Inside Space"

Small Is Beautiful: The Value of Structured Data

Using Data to Drive Innovation

I later mentioned this to an acquaintance familiar with the store's systems and associated capabilities. She said that the store's website includes functionality that indicates not only how much inventory for an item is in a given store but also its in-store aisle and shelf location. I didn't realize that I could have accessed the location when I first checked the store's website from home. However, once I was at the store, I could have simply gone to the customer service desk where they would have told me where to find the item. I subsequently went back to the store and verified that this is true.

As I thought about my experience, I realized that although the location data was readily available, the data by itself was not sufficient to resolve my problem. Data, process, and education were all required. The salesperson I spoke to may not have known that the item's location could easily have been looked up -- if so, that is a failure of the training process. I wasn't aware that I could find the information on my computer or smartphone, albeit with an additional and non-obvious click. Improved website design or a sign in the store that educated me about this feature might have saved the sale.

Processes Need Data, but Data Also Needs Processes

As a self-confessed data bigot, I have long argued that most analytics and operational processes require data to be effective. In my view, an organization's data is analogous to a human body's blood and its processes are analogous to a body's organs. Furthermore, just as a body cannot thrive with contaminated blood, our data warehouses and operations suffer with contaminated data. Additionally, without proper user education and suitable business processes, even the best systems may not be correctly deployed -- if at all.

Over the past several decades, data warehousing has evolved from initially collecting summary snapshots of historical data to collecting detail data, often in real time. This has greatly expanded the potential benefits that our data warehouses can now provide for both analytics and operations.

Our user communities often solicit our advice on analytics issues, but we should also offer suggestions about how our data can be used to enhance operations, perhaps by collecting a few additional data elements. We must also recognize that although data may be a primary ingredient, suitable processes and education must be in place for our organizations to effectively use it.

About the Author

Michael A. Schiff is founder and principal analyst of MAS Strategies, which specializes in formulating effective data warehousing strategies. With more than four decades of industry experience as a developer, user, consultant, vendor, and industry analyst, Mike is an expert in developing, marketing, and implementing solutions that transform operational data into useful decision-enabling information.

His prior experience as an IT director and systems and programming manager provide him with a thorough understanding of the technical, business, and political issues that must be addressed for any successful implementation. With Bachelor and Master of Science degrees from MIT's Sloan School of Management and as a certified financial planner, Mike can address both the technical and financial aspects of data warehousing and business intelligence.


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