Data “Straightening”: Our Toolbox is Ready to Help
My 6-year journey with Data Delve LLC consulting has had the (very intentional) side effect of building a suite of generic tools. These are useful and cost-effective for helping small to medium sized businesses with what I call “data straightening” applications. I’m now offering a “data tuneup” check at a fixed cost to help small and medium sized businesses assess their opportunities. If you want to move forward, the toolbox and latest AI tools are waiting to help. Reach out to me if you have an application you want to discuss.
The Opportunity
Data expertise is great, but, in this case, it comes with decades of experience working with project owners to tease out what’s needed and create solutions. Often, what people need is a cost-efficient onramp for getting their data organized, analyzed and in a form where they can consider whether to move into further, digital transformation steps.
Do you have such applications? Here are some questions to consider if you run a small to medium-sized business:
* Do you have key data in a collection of spreadsheets stashed on employees’ computers or in small databases?
* Do you have technical, lab data or business data workflows that involve repetitively copy/pasting data to create outputs?
* Do you know of lab or business data calculations that could be automated to make them efficient and less error prone?
And finally, the most current topic these days:
* Do you have data that you would love help getting organized so you can try out asking AI for insights about it?
If the answer is “yes”. I can help, and I bring a consulting-derived toolbox and experience to bear in efficiently creating solutions.
A Toolbox for Efficiency
A core strategy of Data Delve from its founding in 2019 has been creating proprietary solutions for clients but doing so while also developing and bringing along reapplicable “open source” tools for doing the generic parts. Although every project is unique, I noticed that what goes on behind the scenes is oh so common.
* Everyone has rows/columns data tables to import, transform and link. It makes sense to have a world class toolbox for that.
* Spreadsheets are pervasive and make an ideal user-facing “dashboard” for working with many lab and business data situations. (Consulting experience bears out that the president, GM or R&D leader usually wants to see things in Excel….). The example below (Columns B to F) is a toolbox “object” that can be instantly configured onto a template’s Home or Settings sheet that also houses the buttons to make things happen. I call this a Scenario Model object. It’s refreshable with VBA to install preset formulas, and the object takes care of formatting and setting/refreshing both user-facing range names and programmatic way finding so that individual setting values are available for calculations throughout the application. The object can also sense its extent, so adding new settings is a breeze.
Our code base is in two, common languages. We have a VBA toolbox as illustrated above for organizing and curating spreadsheet data and building Excel “applications” and “dashboards”. We also have well-organized, Python language tools for situations where that makes sense (and it often does). Within these, there are premade toolsets for building automated pipelines to bring in data that were formerly copy/pasted or manually downloaded from internal or 3rd party sources. We also have premade tools that can be configured to graph data to visualize what’s going to augment just looking at a sea of numbers.
In parallel to Data Delve’s progress, AI tools have progressed to the point where the AI can understand our code and macros and apply them to new problems. I am comfortable how to feed that “this is how we do things around here” context into the AI development process as copilot-instructions and “skills”. This makes an efficient combination for helping companies with the data/spreadsheet straightening tasks.
A Case Study
A business process case study gives an example of the opportunity inherent in digital transformation. My expertise for putting these pieces together is not new, but the Data Delve toolbox enables this immensely. The images illustrate a case study from our online training course. It is inspired by “big company” P&G experience but is a good illustration of what is possible with even a giant application like a diaper business platform’s R&D work.
The images below represent the before and after. The R&D data system existed initially (first image) as standalone spreadsheets and unstructured lists etc. owned by various functional experts. They had to “query” each other by chat, email and in person meetings to connect the pieces.
We took it to an interconnected system where we could instantly answer basic questions like, “Which new products will use the XYZ raw material?” and “What’s a model for how much total raw material cost our development program will incur based on the number of test products we plan to run?” That’s a great digital transformation that gave immediate benefits to the team and demonstrated the value.
Has P&G progressed beyond what I started for Pampers R&D data? Certainly yes, but that’s the point. We created a low cost on ramp for doing that, and we did it in a practical way that was immediately useful to a team of folks collaborating to get new products and manufacturing systems launched.
