From spreadsheets to speed

MainPower distributes electricity to over 37,000 customers via 4873 kilometres of overhead lines and underground cables across an area of almost 12,000 square kilometres in New Zealand’s Canterbury region.

Sustainability savvy, the company constantly seeks to take advantage of renewable resources and is focused on expanding and upgrading its distribution network to keep up with the demand.

“We’re relatively small but like to think we’re powerful,” said Sean Fahey, MainPower’s database administrator. “We’ve got around 180 employees including the field staff out on the road servicing the poles and wires.”

To maintain its standards for excellence in customer service, MainPower analyses a wide scope of data from varying sources including information sourced through a GIS system to map all assets with coordinates and an ERP system, Tech One. It recognised limitations in the way it collected, viewed and analysed data.

The type of information collected is crucial to operations and spans from maintenance history and reports to power ratings, outages and voltages and customer records. Flora and fauna also feature, as trees are often responsible for power outages. Each unique dataset could include up to 40 attributes and the data was often siloed — it couldn’t ‘speak’ to other sets.

“The information we were gathering tended to be quite scattered,” said Stuart Wilson, MainPower’s network manager of development.

“We wanted to reduce conflicts within the organisation because at that stage we had everyone looking at datasets from different perspectives; there was no unity of information, methodology or centralised depository. We also feared that important connections between sets were being overlooked. We simply had no way of compiling and easily viewing datasets collected across different departments and maintenance history,” said Wilson.

Double-handling specific sets also hindered business efficiency and diluted employees’ capacity, with several sets of eyes viewing the same spreadsheets but interpreting them to suit individual needs.

MainPower’s system didn’t present and understand trends and patterns and was leaving information static and siloed, slowing analysis and impacting efficiency. The company decided to build a data warehouse.

“We attended a series of seminars held by independent consultancy Montage to look into business intelligence solutions,” said Fahey.

“At one of these events we saw six BI solutions and the standout was certainly Tableau. We entered into discussions that very night, keen to learn whether Tableau could improve our operations,” he said.

Montage worked with MainPower to create a pilot program, which was then complemented by a further targeted statement of work, using best-of-breed solutions (which could be built on later).

“The use of MainPower’s real-time data in that initial presentation opened our eyes to the true power of the software,” said Fahey.

After successful trials using Tableau, Montage worked with internal teams to integrate the software into the organisation’s pre-existing environment.

Once the initial process of connecting existing data collection points to flow through Tableau was complete, staff showed enthusiasm for sinking their teeth into the program.

“We found that no matter what the department, our employees are able to pick up Tableau quickly and effectively, with a little assistance from Montage. We now have four or five staff regularly using the software,” said Fahey.

After 12 months of using Tableau, the company can see the value of a cohesive solution, including speed-to-insight and efficient data compilation.

“We’ve been able to cut down on the hours spent analysing data; our employees are no longer doubling up to look at the same information because it’s centralised.The input doesn’t seem so much like an avalanche anymore. It’s certainly a refreshing change that several sets of eyes can easily explore datasets that relate to them,” said Wilson.

“We’ve uncovered some unexpected insights through the new software, simply because now we’re able to join data from different sources, linking it together to ask questions we weren’t previously able to,” said Fahey.

When mapping maintenance issues, information can now be displayed geographically, allowing MainPower to reorganise and optimise maintenance schedules for improved physical infrastructure.

“Through this, we were able to see problem areas on a map and prioritise which jobs need to be attended first. It also meant we could group work in one particular area to make sure it all happens in the same work package in the same year, saving us time and money,” said Wilson.

Finally, Fahey applauds the user experience and ease of implementation Tableau has provided.

“It’s something that is easily understood, everyone can implement it in their work. Rather than buying separate tools, we’re able to use software that works for all employees within the company. Tableau has been more helpful than we could have imagined,” he said.