What is data automation? Jargon buster with PlaceTech

This article originally appeared in PlaceTech on 21 September.

From saving time and money to unlocking market insights, real estate likes to talk about the benefits of automation. But what does it actually entail? Rhys Ackery of Coyote explores the ins and outs of taking humans out of the equation.

What is data automation?

Generally speaking, it’s the exchange of data from point A to point B without direct input from people.

However, data automation can also extend beyond that to the enrichment of existing data, with contextually relevant data from other sources in order to create additional value.

How much can you save by streamlining data?

Many commercial real estate teams spend hundreds of hours collating data from various sources for periodic reporting.

When data collection and aggregation is automated, it can reduce the number of billable hours by more than 90%, saving serious time, stress and money.

When data automation is applied to processes in other areas of the business, it can be more of an initial investment up front, but the long-term payoff is enormous.

By enabling teams to access key information more easily, you are giving them the tools to make quicker and smarter decisions based on the speed and depth of insight that data automation provides. Long term, that puts a business in the best possible position to succeed.

How does someone in real estate get started in data automation?

You have to start with context. Understand where your data currently sits, who is responsible for it and be clear about what you’d like to achieve with data automation. Before you start any project, set an objective with clear milestones and key results.

From there, our strong recommendation is to work with a trusted partner or adviser to review, define or even challenge your plan and then put it into action.

How can you ensure the accuracy of automated data?

From our experience, data automation helps clients to spot errors, and the source of errors, in existing processes, where they otherwise go unnoticed. When data automation is properly implemented in certain processes, it can help dramatically reduce the overall error rate instead of contributing to it.

However, we would still recommend that someone in the organisation has responsibility for the data and that validation rules are put in place to help reduce certain types of errors. It’s also important that companies have a clear process for flagging and fixing errors.

How do you extract useful insights out of data?

First, data needs to be ‘clean’ and complete. A data audit can help determine the current state of your data.

It’s also imperative to have a clear idea of what types of insights you’re looking to get from data. Defining the insights you need will help determine the pathway required to get there.

From there, companies need to challenge the status quo. Put tried and tested systems in place, like our own, which can help make sense of data and display it in a way that is easy to consume, and which provides better insights from your existing data.

How might data automation evolve in the coming years?

Data automation is likely to become more prevalent, particularly with an increased focus on data democracy and data sharing. Here are two tips to stay ahead of the curve:

Start being concerned with the quality of your data now, rather than waiting until you need to automate it.

Evangelise for data literacy within your organisation. Consume learning and training opportunities and become an advocate.

This article originally appeared in PlaceTech on 21 September

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