Naturally, one of the biggest topics we spend time thinking about is where it’s going next. The gathering and analysis of data has already made massive impacts to various industries — from manufacturing and financial services to property management and tourism — but it won’t stop there.
In this article, we’re taking a look at what working with data might look like in the not-too-distant future.
Today, for many organisations, there’s a fairly linear process when it comes to using data. The information is gathered across digital platforms, sensors, and machines, analysts review the data and create reports, and leaders make decisions based on those reports. As our data capabilities grow, this process is going to become more automated.
We see a future with fewer analysts, but more analysis. There will likely be expansive data ecosystems where decisions are automated based on information that’s gathered not just from one source, but from various external data collection points.
For example, with an HVAC system, a property manager wouldn’t just see machine performance data to determine when to run maintenance. Instead, they’d have visibility into weather patterns that influenced building temperature requirements, real-time data on how other similar HVAC systems are operating, and other key information that allows for predictive decision making.
While this concept of data metaverses presents seemingly endless possibilities, they shouldn’t exist without limitations. As we build these ecosystems, we need to think about the how and why of the data we’re collecting.
We’ve already seen the impact of companies collecting data for the sake of collecting data, and that can pose a threat to users that would rather keep their personal information private. The path forward here is to find ways to gather insights transparently, without requiring compromising personal information to make actionable decisions.
When we talk about these increasingly automated systems, it might be hard to imagine what role humans might play — if any. The reality is that people will still be indispensable. Algorithms and machines are learning from historical data trends and biases, and that can be problematic if they’re left to run unmonitored.
For instance, many facial recognition programs have been built with primarily white inputs, making it difficult for them to appropriately recognize people of colour. Without the right guide posts in place, systems like these can cause more harm than good.
It’s our role to accurately teach these systems, giving them the information they need to be more agile, equitable, and efficient in their use of data. As such, they are bound to be job postings in the future for data specialists that can learn, interpret, and add value to the data systems we develop.
The future of data is bright, and we’re so excited to be a part of it. To learn about how Intwo partners can help you make the most of your data, get in touch.
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