This month’s meetup was our first since we were rechristened as “Columbus Data & Analytics Wednesday.” In an unintentional twist, the topic for the event was centered around the speaker’s contention that we (the broad, collective “we”) devote too much of our time and energy to the collection and management of data, and not enough effort to actually putting that data to productive and impactful use within our organizations.

Tim started out by calling out that, if we consider any task that has any relationship to data as “data work,” then we can further categorize each of those tasks into one of two buckets:

• Data Collection and Management
• Data Usage

He noted that there is no inherent business value in the collection and management data. There is only the potential for value. To realize that value requires putting the data to meaningful, applicable business use.

All too often, data workers get so caught up in data collection and management tasks, though, that they start to believe that there is inherent business value in those tasks alone. Tim pointed to three reasons for this happening:

• Technology vendors tend to have business models that are high fixed cost and low variable cost, which means they’re incentivized to drive aggressive customer growth. This results in heavy investments in marketing and sales organizations that wind up distilling down their messaging to, “Buy our technology and you will realize business value.” And they spend a lot of time and money promoting that message.
• Consultants have the opposite business model—low fixed costs and high variable costs—which means they grow profitably by selling engagements that use repeatable processes that can tap into a scalable workforce. That pulls them to “technology implementation” work over “deeply engage with the businesses of their clients and all of the complexity therein.” So, they wind up promoting a similar message: “Buy (our partners’) technology, let us help you implement it, and you will then realize business value.
• Human nature within organizations drives us to do tangible “things”—adding new data sources, cleaning up data quality issues, building or augmenting dashboards, etc. This leads us to telling ourselves that these tactical activities, which skew heavily towards data collection and management, bring value to the business in and of themselves.

According to Tim, recognizing and pushing back against this mindset means embracing the messiness and hard work required to actually use data productively. He proposed that organizations need to put the same level of rigor around their data usage processes as they put around their processes for collecting and maintaining data. As an example, he outlined a framework he uses (but was clear that this wasn’t “the only” framework that’s valid for data usage) that pointed to three distinct ways data can be used to provide value:

• Performance measurement—objectively and quantitatively answering the question: “Where are we today relative to where we expected to be today at some point in the past?” He described using “two magic questions” for this: 1) What are we trying to achieve, and 2) How will we know if we’ve done that?
• Hypothesis validation—this is all about improving decision making by reducing uncertainty when determining what to do going forward. For this, he described a 3-part fill-in-the-blank technique: “We believe [some idea] because  [some evidence or observation]. If we are right, we will [take some action].”
• Operational enablement—data when it is actually part of an automated or mostly automated process (for instance, ordering shoes online generates data that is used by the order fulfillment process). He went on to say that every generative AI use case he’s seen put forth falls into operational enablement.

He ended by imploring the crowd to look at the work they and their colleagues do day in and day out through a “data collection & management” vs. “data usage” lens and consider working to shift the balance of their efforts towards the latter!

The slides are available below, as well as at :

And, of course, some pics from the event, which had a large and lively showing!