Digital twins come to life in 2020 and software models from the past become the future

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Over the past decade we’ve seen that being proactive as an organization in a cloud-first era is vital to thriving, and 2020 and the decade ahead will be no different.

We can only imagine what the 2020s will bring, but we anticipate that it will be the year the spotlight shifts from the analytical models themselves to the process, digital twins will take a human form, and a unique software model of the past will finally become possible.

First, the continuation of cloud and artificial intelligence/machine learning as strong technology focus areas for enterprises across the globe will be one of three major trends that will unfold in 2020. Cloud will continue to be “hybrid” and “multi-cloud” in nature, and the effective use of analytical models will continue to be a key differentiator for organizations.

I believe that there will be a broad shift in focus away from the models themselves to the supporting processes that make these models possible.

Second, we can expect the optimization of the “customer journey” to continue to be increasingly important to organizations. We will see growth in emerging technology areas which may be used to support this optimization.

The notion of a “digital twin” for machines is not new, but the approach of applying this concept to people will gain more traction. The ability to model a person or, more specifically, create “twins” such as a virtual customer image or virtual student image, and influence this model via a variety of inputs (e.g. IoT), knowledge of current state/time, emotion analysis/identification, and defined goals/outcomes will allow organizations to produce an experience that is much more timely, contextual, and relevant.

The “belief, desire, intent” software model from the past will start to become more feasible.

Finally, 2020 will be the year that IoT will make inroads with both consumers and enterprises alike. This will lead, however, to broader concerns about security and device “spoofing”, which are two big areas that will need to be addressed for this technology to be broadly accepted. The ability to handle and process IoT data will continue to be pushed out to the edge as organizations look to deal with the vast amounts of data, and reduce overall decision latency.

The journey to the “belief, desire, intent” software model, taking the spotlight away from analytical models themselves and moving it to processes, and the ability to reduce overall decision latency in IoT data will be a treacherous one, but it is possible. Here’s to another year of expecting the unexpected.

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