The Alden ONE Data Lake: What are the Benefits for Utility Companies?

Posted by Ashley Little on July 11, 2023

Part one of a series titled "The Value of the Alden ONE Data Lake for Joint Use Asset Management." Read the second article here.

Scenario: You are presenting to your leadership team in an upcoming meeting, and you’ve just uncovered an interesting anomaly in a report. There is no time to coordinate with staff and wait for new analyses, but you need to dig deeper into the data.

With access to the Alden ONE Data Lake, you can quickly apply customized filters and sort the data for yourself to distill your insights and modify your reporting accordingly. Let's start from the beginning.

What is a data lake?

A data lake is defined as a centralized repository designed to store, process, and secure large amounts of data. It can store data in its native format and process any variety of it, regardless of size limits.

According to Microsoft Azure, “the data can be processed and used as a basis for a variety of analytic needs. Due to its open, scalable architecture, a data lake can accommodate all types of data from any source, from structured (database tables, Excel sheets) to semi-structured (XML files, webpages) to unstructured (images, audio files, tweets...)”

Let’s look deeper into the value of a data lake.

The value, as Forbes reports, is that a data lake holds data in an unstructured way with no hierarchy or organization among the individual pieces of data — which means data is readily available in raw form precisely when it’s ready to be analyzed.

This enables you to get unique questions answered without having to separately collect and compile data sources. For utilities and attaching companies strapped for time and relying on coordination with numerous entities, the data lake can eliminate collaborating with multiple points of contact and, in turn, shave off multiple hours of labor.

Is a Data Lake Worth the Investment?

With a data lake, you can sharpen your analytical capabilities. Here’s how:

Gain greater insights with all your detailed data in one place.

A good data analyst wants access to as much data as possible, which typically means having to reach out to various stakeholders to collect different data sets (operational data, payroll data, contractor payables from A/P, etc.). A data lake pushes everything to a centralized location in near real-time so you can access up-to-the-hour critical information. You can also retrieve attributes at the most granular level to uncover insights that might not show up on standard reports.
Build ad hoc reports and securely share meaningful data.

As Deloitte reports, data lakes are special, in part, because users get direct access to raw data without significant IT involvement. This “self-service” access delivers insights more quickly – find answers to pointed questions, like where to dedicate more workforce and why bottlenecks are happening. You can also use ad hoc reporting to satisfy constantly changing compliance reporting requirements.
Seamlessly integrate with business intelligence applications.

The Alden ONE Data Lake works with your other tools, not in isolation. Easily import Alden ONE data into programs, such as Excel, Power BI, Tableau, or Cognos.
Use historical data for predictive models.

With a more comprehensive view of historical data, like asset details and workflow status changes, you can apply your business intelligence to plan for future scenarios with confidence.

These are just a few ways that utility companies managing joint use assets can use a data lake to eke out more efficiency. But how does it work in practice? Let’s look.

Alden ONE Data Lake Example: Use Cases

Adrian Torres, Product and Business Analyst for Alden ONE, came to Alden after a long career as a utility data analyst, where one of his biggest issues was reporting. Without a data lake, he spent hours communicating with team members about how to comb through data to compile just one report. Here’s what he observes about the value of data lakes:

Data Lake Example #1: Identify New Ways of Analyzing

While standard reporting is great for evaluating key metrics over time, sometimes you want to dig into the data to see what it’s telling you before you perform a more pointed analysis. Torres explains: “I didn't always know what I needed to know. Sometimes all I needed were data dumps...to be able to go through the data, analyze it myself, find trends, and then know what to look for as opposed to predefining.”

He points out that with regulatory reporting in particular, analysts need to set parameters for specific periods of time, number of applications, how long work took, and how much it cost — not something that you can easily get with predefined requirements.

Data Lake Example #2: Iterate Details

The value is not only about getting to the details. It’s also about recognizing that the details you’re assessing may change as you learn more.

For example, in his role as a business analyst, Torres had to quantify the number of utility pole attachments by attachment type, so he requested a report. After looking over the data, he realized that he only needed information on non-power attachments (separating out the communications company). A summary report at the pole level wouldn’t provide these kinds of attachment-level details. But a data lake could easily handle this need.

In another instance, Torres recalls requesting multiple reports based on inquiries that became more apparent as he dug into the data, only to end up without what he needed. So, he requested a full data extract — a large, cumbersome file that took quite a bit of time to load to an FTP site. “If I had a data lake at the time, I would have just connected to that container, showing all my attachments and whatever attributes were available, and then I could have done my calculation however I wanted.”

Data Lake Example #3: Spot Outliers

Sometimes you just want to uncover areas that don’t align with benchmarks, target metrics, or vendor averages. Here’s one use case: utility companies that manage joint use assets often use engineering partners to perform the field design or pole loading analyses. “What we found was that it wasn't always easy to identify outliers,” Torres says. For example: why is one company charging five hours per pole while another vendor is charging two hours per pole for a given application?

Without a data lake, you’d typically take third-party or vendor invoicing and compare it against your reports, and then run your analytics. But invoices don’t often provide the level of detail needed for accurate insights, and you could lose hours tracking down the details.

A data lake lets you immediately access all data points for a more informed conclusion to critical questions like this.