What is data automation? | jargon buster

From saving time and money to unlocking market insights, real estate agents like to talk about the benefits of automation. But what does it really imply? Coyote’s Rhys Ackery explores the ins and outs of taking humans out of the equation.

What is data automation?

Generally speaking, it is the exchange of data from point A to point B without the direct involvement of individuals.

However, data automation can also extend beyond enriching existing data with contextually relevant data from other sources to create additional value.

How much can you save by optimizing data?

Many commercial real estate teams spend hundreds of hours collecting data from various sources to generate regular reports.

When data collection and aggregation is automated, the number of billable hours can be reduced by more than 90%, saving a lot of time, stress, and money.

When data automation is applied to processes in other areas of the business, it may be more of an upfront investment, but the long-term payoff is huge.

By enabling teams to access key information more easily, it gives them the tools to make faster, smarter decisions based on the speed and depth of insights that data automation brings. In the long run, that puts a company in the best possible position to succeed.

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

You have to start with the context. Understand where your data currently is, who is responsible for it, and be clear about what you would like to achieve with data automation. Before starting any project, set a goal with clear milestones and key results.

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

How can the accuracy of automated data be guaranteed?

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

However, we still recommend that someone in the organization be responsible for the data and that validation rules be implemented to help reduce certain types of errors. It is also important that companies have a clear process for flagging and correcting errors.

How do you extract useful insights from the data?

First, the data must be “clean” and complete. A data audit can help determine the current state of your data.

It is also imperative to have a clear idea of ​​what kind of information you are looking to gain from the data. Defining the skills you need will help determine the path required to get there.

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

How could 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:

Start worrying about the quality of your data now, instead of waiting until you need to automate it.

Evangelize for data literacy within your organization. Take advantage of learning and training opportunities and become an advocate.

Rhys Ackery is head of service delivery at Coyote.


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