Applying An Asset Management Approach To Improve Your Marketing Efficiency
By Bill King
The world of water distribution and treatment is becoming more analytical by-the-hour as a variety of converging trends impact the industry. Perhaps the most significant has been the falling cost of computing. As reported by Andy Klein on Backblaze, the cost of a gigabyte has dropped from $500,000 in 1981 to $0.03 today. This has driven the evolution to cloud computing, where storing data has effectively become free. For more on this, read Chris Anderson’s book Free: The Future of a Radical Price. Much of what Anderson identified in 2009 is maturing today in the way utilities of all sizes are operating IT and OT. Concerns over cyber-security and control of data has gone full circle where utilities are now seeing on-site servers and operations as a greater liability than storing data securely at shared data farms thousands of miles away.
The second trend is the pressing need to run operations with less. Utilities are faced with an aging workforce with mass retirement of experienced operators on the horizon. I heard recently that 55,000 operators will retire over the next 10 years in the State of Texas alone. Despite the industry’s best efforts to attract younger workers, it is clear that labor alone will not plug the hole. Utilities are therefore looking to run more efficiently to cover more with less.
To help utilities run more efficiently, software providers are emerging left, right and center to help operators better diagnose, predict and respond to changes in day-to-day flows and water quality. Built to present the voluminous amount of data that utilities have always been regulated to collect, these solutions analyze the data to better inform their users on how to run their plants and distribution systems.
For business development and marketing personnel in the water and wastewater industry, a similar trend has emerged. Giant datasets are being analyzed to better inform prospect funnels. LinkedIn is finally resonating at scale to help professionals build networks online. And publishers are differentiating themselves beyond vendor websites by providing deeper visibility into their audience’s content preferences and behavior.
It will only continue. Just as artificial intelligence becomes more sensitive to predicting our needs correctly, machine learning will begin to identify exactly which format of content and topic of interest should be delivered to a specific reader at a specific time. The human element question will be how to create a robust content portfolio to meet that need.