Bad data has the gravitational pull of Jupiter. It causes problems as massive as a planet for telecommunication networks thanks to errors, missing information, a lack of interoperability, analog delays and red herrings galore. When your data problems are so huge, it’s easy to focus on the wrong things, the little things you believe you have agency over. But here’s the rub: you have to solve the Jupiter-size data problem first. Once you do that, those other smaller problems may resolve on their own, or they’ll be a whole lot simpler to solve. Here’s how you escape the bad data orbit.
Shift your data paradigm
Data can be tricky. Most people get it wrong more often than they get it right. The other thing: data continues to evolve. You can’t tend to it once and be done. Instead, you should think of your data as a living ecosystem that morphs over time. As your data shifts, so will your knowledge along with your actions. You have to embrace dynamism. As the saying goes, change is the only constant. Said another way, good data isn’t a destination, it’s a way of working.
Understand your data baseline
You first need to understand how good or bad your data is. Good data leads to better decisions and it enables innovation. With good data, you can design, test and learn networks before they’ve been built. You can iterate as much as you need to find the most effective and efficient solutions. Once you’re in the build phase, good data allows you to make adjustments more quickly and easily in real time so you can conserve time, money, materials and labor.
Here are some questions to ask yourself to determine whether your data is good or bad:
What are you trying to accomplish with your data?
Does your data enable that to happen?
What data do you have? Geographic? Topographic? Construction-level?
Can you layer the various data sets to get a more accurate understanding of your network situation?
Do the data sets talk to each other? If not, what needs to change for them to better communicate?
Is your data current?
What data are you missing?
Can you identify errors in your data?
Can you efficiently manipulate or transform your data?
Can you field-verify your data?
If you answered no, maybe or not sure to any of these questions, you’ve got bad data. But there’s hope. Learn how you can avoid three common data traps here.
Adhere to best practices
While no magic wand exists to solve your data problems, you can create a better framework from which to approach your data. The tips below give you the building blocks you need to create both a top-down and bottom-up structure for your data.
Use interoperable data schemas. This simply means that your data models are open across systems and shared among all parties.
Complete your data dictionary. Minimize subjectivity so your data is as objective as possible. Build in logic for how your data can transform.
Eliminate paper and automate as much as possible. Automation and digitalization translate to speed and scale. For example, use a tablet to field verify for maximum efficiency.
Weave in regular data-health checks at key milestones. Before network design, confirm your data set works against your schema. During the design process, validate the data in the field. After construction, update your data set with the as-built record.
How Biarri Networks can help
A lot of things sound simple in theory but remain difficult in practice—like data. At Biarri Networks, we’ve been cracking the code on data for years now, which means we have people, products and processes in place to help you solve your biggest data challenges.
Dedicated data prep team and product
Our dedicated data-prep team gets your data set ready. In fact, that’s all they do. They live and breathe your data until it’s fully prepared. Every data set requires this human touch. Our team defines a set of rules and guidelines that becomes the single source of truth for the project and ensures the data schemas are interoperable and truly integrated.
We then run the prepared data through our proprietary algorithms to discover what data is erroneous or missing and determine how we can augment and supplement those gaps and/or errors. From here out, the data drives our networkdesign approach.
Learn how we used data cleaning, quality assurance and validation to connect 75% of New Zealand with high-speed broadbandhere.
During thedesign phase of a project, we iterate to find the best network design solutions for your project goals. We then verify both the data and designs in the field before construction begins. We test our data hypothesis and adjust as needed to get to final designs.
Once construction starts, we leverage our digital construction project management platform to record the as-built record, which is the most important piece of the data set we capture for you and for the network’s future. The smaller the difference between the final design and the as-built record, the better because it validates our human-led, data-driven approach.
Discover how our data-driven approach saved one electric cooperative $5.5 million in capital expensehere.
Your go-to data partner
Ready to radically transform your data and your network deployment projects?Get in touch today.