Getting Smart About Business Intelligence

While more orga­ni­za­tions are starting to unlock the power of business intel­li­gence, there are still some long-held assumptions that have plagued the industry. 

Myth #1: "This isn't doable at my utility." 

Business intel­li­gence isn’t just for the biggest, most complex orga­ni­za­tions, nor does it require a bottomless budget or a boatload of manpower to get a return on your investment. In fact, if you’re already doing data collection and are committed to improving its practices, you’re probably well on your way to full-scale imple­men­ta­tion. 

In case you’re unfamiliar with the process, here’s how to get started. 

• Step 1: Identify your orga­ni­za­tion’s priorities and develop a “Top 10” list that ranks your system’s most important key performance indicators (KPIs) or metrics that are needed to better manage your system.
• Step 2: Determine if you are already capturing any of this data, where it is stored and what condition it's in. Often, this step takes the most time so be sure to factor this into your planning process.
• Step 3: Normalize the data into one platform so it can talk to each other; this is often accom­plished through a business intel­li­gence software program.
• Step 4: Apply the business intel­li­gence technology and begin making better decisions by using customiz­able charts, tables, dials and maps to clearly understand what your data is telling you. 

And while there are dozens of off-the-shelf products that are available to standardize data and run databases, the most effective are the ones that work best for your utility's specific needs. 

Myth #2: “I already have all this data, so why do I need more systems for analysis?” 

Likely, your utility collects mountains of data every day: work order information like condition assessments, maintenance records, and regulatory data as well as operational systems like payroll and customer billing. The data in those systems is being collected and used for a specific purpose and indi­vid­u­ally those systems may be working very well, but siloed systems fail to provide a full picture of performance. What business intel­li­gence allows you to do is make sense of those mountains of data and turn that data into knowledge to support the decision-making process. 

Take, for example, the global utility maintenance and planning program that was recently implemented for the United States Navy. Prior to estab­lish­ing an asset management system, the Navy’s funding process operated with a “squeaky wheel gets the grease” mentality, which meant that the 650 Naval bases encom­pass­ing 250 different utility systems and close to 500,000 individual assets weren’t appro­pri­ately assessed for condition or risk of failure. 

With the help of CDM Smith’s asset management experts, the Navy began incor­po­rat­ing mobile technology tools like GIS databases and dashboards to inventory, catalogue and rank each physical asset based on an unbiased condition assessment rating. Next, the team established “risk scores” for each asset, asking questions like, “Does this power generator fuel a hospital or a fast food restaurant?” to ensure capital funds and maintenance resources were appro­pri­ately distributed. 

Today, the Navy is in full control of their asset management system, utilizing asset management technology to track performance, make more informed decisions, present actionable information and success­fully plan for the future. 

Myth #3: “This will cost too much, and my data isn’t in the right shape to get any ROI.”  

Orga­ni­za­tions that look at business intel­li­gence from a technology perspective will struggle to get any ROI. That’s because this isn’t an engineering project—it’s a business process change. Anyone can buy a piece of software, but you can’t just plug it into your computer and start making dashboards. You are changing the way that your orga­ni­za­tion thinks and lives on a daily basis.  

What is great about this type of technology is that it can be applied to almost any type of process, whether it’s managing a huge program management initiative or just reporting on how many hydrants were flushed this year. Let’s explore CDM Smith’s business intel­li­gence overhaul in Columbia, South Carolina, a highly scalable example of a utility determined to improve its operational structure and become a leader among its peers. 

Hardly strangers to data collection, the city had built up a robust inventory of maintenance information yet lacked a data governance system necessary to turn that data into actionable information. And while other utilities might be embarrassed to let anyone else see the condition of their data, Columbia allowed CDM Smith in the virtual door—a step that experts say is often the hardest to take. Since completing the data normal­iza­tion process, the city has been utilizing dashboards to take care of everything from field crew deployment to KPI management and dramat­i­cally improving the bottom line.

So what’s on the business intel­li­gence horizon? Experts say the boost in machine learning technology and predictive analytics (which help orga­ni­za­tions quickly root through millions of data points, find trends, predict problems and support targeted functions) will likely play a major role in future operations. And as more data-driven orga­ni­za­tions realize the power of these tools, it’s not long before this technology is commonplace.