Data Management is the #1 Most Overlooked Competitive Advantage.
The information (data) that a company chooses to collect is going to be the most important asset in the next 5-10 years. Why do I think this? We are using artificial intelligence like it’s this highly evolved sentient being that will swoop in and make something of all the information we’ve hoarded within our companies. That’s simply not the case.
AI is really fancy and complicated mathematical models. And what mathematical models need are great datasets to train itself. There are many articles out there on what makes great training datasets, so I’m not going to cover that here. Plus, I’m not an AI expert by any means.
But what I do want you to start thinking about, and thinking about early, is how are you going to leverage this AI explosion to benefit you and your business? Your data management strategy can be one of your most lucrative assets if you plan ahead.
Your data management strategy can be one of your most lucrative assets if you plan ahead.
Your Data Strategy Is Holding You Back
Let’s be honest, most companies treat data management like an afterthought. Files are scattered across desktops, dashboards are built with no long-term plan, and when it is time to analyze trends, someone has to dig through six versions of the same spreadsheet to piece together the full story.
Sound familiar?
Here is the hard truth: Your data is only as valuable as the strategy behind it. And if you are not treating it like a critical asset, you are already falling behind. In this blog, I will break down how to build a scalable, strategic data management system from a scientist’s point of view.
We will cover:
Why your data strategy is a competitive advantage (or a liability)
How AI is only as good as the data it learns from—and why that matters for you
The biggest mistakes companies make when structuring their data
Why human behavior is the real challenge (not the software you pick)
And how to build a system that stacks on itself year after year
This is not about fancy software or trendy buzzwords. It is about real, practical strategies that will make your data work for you—today, tomorrow, and in the future.
Your data is only as valuable as the strategy behind it.
Why Data Strategy Matters
If your data is a mess, your decisions will be too. Collecting random data without a strategy is a waste of time and resources. The types of data you collect and how you organize it will determine your ability to stay competitive.
You cannot prioritize everything, believe me, I've seen companies try. Data collection is expensive and time consuming. The companies that win are the ones that focus on collecting the right data and structuring it properly. Companies that collect strategic, high-quality data will gain a competitive advantage, and those that do not will struggle to extract insights and make poor decisions based on incomplete data.
How to Build a Smarter Data Strategy
Identify what data is critical for decision-making. Do not collect data just because you can.
Ensure data collection methods are consistent and standardized.
Structure data for long-term comparisons. If you cannot track changes over time, the data loses value.
Data as a Future Revenue Stream
The data you collect today could be a revenue stream in the future. AI-based products are only as good as the data used to train them. Many companies are searching for high-quality, niche datasets to improve their models. If you have prioritized a specific set of data points over time, that data can become a valuable asset.
Companies that treat data collection as an investment will have opportunities to monetize it. Those that do not will miss out on a growing market for AI training data. First, identify your mission critical datasets and consistently track it year over year. The narrower the better. Then, ensure the dataset is well-structured and standardized. Unorganized data is not useful for AI training and trying to organize it post hoc take so much time and energy. Lastly, you must maintain data integrity over time. The longer and more consistent your dataset, the more valuable it becomes.
Most companies are not thinking about data as a long-term asset. The ones that do already have an advantage. Set aside an annual budget around data collection and data management.
Companies that treat data collection as an investment will have opportunities to monetize their data in the future.
Build a Data System That Scales
A data management system that does not scale will eventually fail. Most companies start with scattered spreadsheets and disconnected databases. As they grow, data silos form, systems stop communicating, and valuable information gets lost. A forward-thinking data strategy prevents this because you don’t need to figure out after the fact how to connect different datasets.
A scalable system ensures that data remains accessible, structured, and useful as your company grows. Without this, you will waste time rebuilding infrastructure and fixing compatibility issues year after year. But be practical about this, your system should change over time. What you want to avoid is a total system overhaul.
This is what I suggest to help your strategy scale:
Ensure systems integrate. Choose platforms that can share and process data efficiently.
Standardize formats. Avoid collecting data in a way that will be difficult to merge or analyze in the future.
Plan for future growth. You do not need all the answers now, but your system should be flexible enough to handle increasing complexity.
Data Must Stack Over Time
If your data does not build on itself year over year, you are not learning anything. Tracking isolated data points will not reveal long-term trends. You might see high turnover one year and virtually no turnover the next. If you picked either of those years in isolation, you could be telling the wrong story.
And this applies to everything from greenhouse experiments to HR retention data. The only way to gain real insights is by structuring data so it accumulates and remains comparable over time. This isn’t possible without a structured system. So make sure you use consistent formats and variables across years.
Changes in how data is collected or labeled will create gaps. Store data in an accessible, centralized location because scattered files make long-term analysis impossible. Lastly, plan for comparability. Design experiments, business metrics, and tracking methods with future analysis in mind.
Garbage In, Garbage Out
AI will not fix bad data.
AI will not fix bad data.
AI will not fix bad data.
(Do I need to say it again?)
Unstructured, inconsistent, or incomplete data will not generate useful insights, no matter how advanced your analytics tools are. AI models are only as good as the data they are trained on. Companies that fail to standardize and clean their data will get misleading results. Investing in AI without fixing foundational data problems is a waste of time and money.
Here are some suggestions to improve data quality:
Standardize data collection methods. Inconsistencies make data difficult to compare and analyze.
Validate inputs. Incorrect or incomplete data will lead to poor decisions.
Automate where possible. Reduce human error by using structured systems instead of manual entry.
Investing in artificial intelligence without fixing foundational data problems is a waste of time and money.
Excel Is Not an Analytical Tool
If your data strategy relies on Excel, you do not have a data strategy. Excel is great for small tasks, but it is not built for scalable data management. It lacks version control, automation, and integration capabilities.
Relying on spreadsheets leads to inefficiencies and errors. As data grows, spreadsheets become unmanageable. Companies that fail to transition to real data management systems will struggle with scalability, collaboration, and security. Additionally, today's business moves at lightspeed, and therefore your ability to make decisions must also be that fast.
How to Move Beyond Excel
Use databases for structured data storage. SQL, cloud-based platforms, and business intelligence tools provide better functionality.
Automate data entry and analysis whenever possible. Manual updates increase errors and slow down processes.
Implement version control. Tracking changes ensures data integrity over time. Build in quality checks in regular intervals to check for anomalies.
Naming Conventions Are Data Management
Messy file names create messy data systems. If you cannot tell what is in a file without opening it, your naming conventions are a problem. Standardized file and folder names are critical for organizing and retrieving data efficiently. Inconsistent naming leads to lost files, duplicated work, and wasted time.
A structured system makes data accessible and reduces errors. So make sure to use a standard format that includes key details like project name, date, and version. Avoid vague names like “Final_Version_2.” Maintain consistency by using the same rules across all teams and systems. I prefer to start file names with the date, year first, so that all the files stake in chronological order.
Understand Hierarchical Data Structures
If your data has no structure, it has no value, and the gold standard in structure is using the hierarchical method. Hierarchical data structures organize information logically, making it easier to retrieve, analyze, and scale. This applies to everything from databases to file storage and project management.
A hierarchical data structure organizes data in a tree-like format.
Example:
Company
Departments
Teams
Employees
This structure makes data retrieval and analysis more efficient. To do this, use clear parent-child relationships in data organization. Additionally, create categories that are logical and scalable. And remember, a good structure allows for growth without breaking the system.
Metadata is More Important Than Primary Data
If you are not collecting metadata, you are missing half the story. Metadata describes data and answers questions like how, when, and where it was collected. Without it, primary data loses context and becomes difficult to analyze. Furthermore, it removes the ability to compare like variables. Think of the saying 'comparing apples to apples.'
But companies become too focused on primary data which means they will struggle with consistency, tracking, and usability. Metadata determines how similar or different the information is when aggregating.
Just like primary data, you much define your metadata fields. Every dataset should have clear attributes. Then automate metadata collection where possible (again, just like primary data). Systems should track timestamps, sources, and modifications. And finally, my general rule of thumb is to collect more metadata than primary data. Without context, data points lose meaning.
Here are some example of metadata:
Timestamp: When was the data recorded?
Source: Where did it come from?
Format: What type of data is it?
Changes: Has it been modified?
Soil Type: Environmental data impacts results.
Cattle Breed: Different breeds produce different milk quality metrics.
Without metadata, primary data loses context and becomes difficult to analyze.
You won’t be able to compare apples to apples.
Data Strategy Is a Human Behavior Problem
A new data strategy will not fail because of bad metrics. It will fail because people will not use it. Implementing a data strategy is more about human behavior than deciding what to collect, how to organize it, or what analysis formats to use. If people do not see the value or the process is too complicated, they will not adopt it.
To roll out a data strategy that sticks you must account for human behavior. Try to make it as effortless as possible. The fewer steps, the better, and automate where you can. Communicate the “why.” If people do not understand what the company gains from the change, they will not see a reason to adjust their behavior. And people need to see the outcome of their work so make sure to showcase progress. This keeps engagement high and ensures long-term adoption.
And I like to structure the rollout in this way:
Teaser: Let people know something new is coming.
Introduction: Explain the purpose, the benefits, and what success looks like.
Official Rollout: Provide clear expectations and timelines.
Training: Hold at least two to three in-depth training sessions.
Yearly Updates: Show what has been accomplished with the effort people have put in.
Summary: Your Data Strategy Is Your Competitive Edge
Data is not just something you collect. It is an asset that will define your company’s future. A well-executed data management strategy is a competitive advantage and a future revenue stream. The companies that get this right will lead. The ones that do not will be left behind.
But remember, your data is only as good as the strategy behind it. If you leave data management up to individual employees with no structure, you will end up with a fragmented, unusable mess. AI will not fix it. Better software will not fix it. Only a deliberate, well-structured strategy will.
A strong data strategy requires intentional effort. It is not just a set of processes and rules, and it is and, if you do not get user buy-in, it will fail.
Remember:
Form a task force or committee. Data management is too important to be an afterthought. Assign ownership and accountability.
Get buy-in from the people using the system. A top-down mandate will not work. Engage employees early. Make them part of the process.
Think beyond today. Data is a long-term investment. The effort you put in now will determine your ability to scale, remain competitive, and create new revenue streams in the future.
The companies that build structured, scalable data strategies today will be the ones selling their data tomorrow. Do not wait to get this right.
Whitney Rottman is a product development specialist that supports biological companies selling microbial-based products to farmers, ranchers, and dairy producers. She is a physiologists by training and specializes in how to introduce biology into a microbiome to affect a physiological change in the host whether that is a cow or corn plant.
