Trends prediction is a component of big data that countless fields utilize. It can make patterns trends like retail consumer sales, wellness, climate change, and energy consumption apparent.
The duration of predictions that companies use aren’t limited to five or 10 years. Some large companies look forward 50 to 100 years with the data they have today to see how viable certain subjects will be. They can do so because of the large amount of data that they have.

Take ground water evaluation as an example of one such field that utilizes data science. You have the conditions underground, which drives the way that water changes each year. These conditions can influence how much sulfur, iron, magnesium, and other minerals are found in the water, which in turn affects the quality of water derived from the grind for drinking.

Long-term business trends are no different when it comes to prediction models. The amount of data they must consider can be just as extensive.

Data models exist to put reason behind things that seem out of the ordinary. Things like an increase in more natural disaster might make sense because of global warming, but sudden shifts in climate may seem impossible to explain without considering all the factors behind it. Even things like the correlation between childhood obesity and more healthcare problems seems straightforward, but what if a treatment that decreases childhood obesity is created in the next five years to make the problem of healthcare substantially less pressing?

That means one emerging challenge with data sceince is ensuring that the “shell life” of models can be determined. This creates an actionable date that a model should again be examined for viability.

The job of determining trend viability was considerably simpler when trends could be broken down into short-, medium-, and long-term models. This allowed reports to be made weekly, quarterly, or annually, which in turn made it possible to determine how long the forecast would be viable.

The challenge with big data trends is that they encompass such a long period of time and so much data that these types of old reporting styles are insufficient to properly assess the current variables surrounding a model.

Consider companies that conduct construction for local cities. These periods can’t account for an influx in young population, nor can they assess what type of homes future populations will want. They can’t even determine what spread of young, middle-aged, old, or elderly population will be living in a city between 50 to 100 years from now.

These are just a few of the types of problems that professionals in the field of data sceince are trying to tackle. Adjusting for the hard-to-predict trends will make prediction models more accurate in the long-term, which in turn makes big data models able to give more trustworthy prediction as their time frame increases.

That leaves companies utilizing these types of data models to decide what the expiration date on their data is. Determining this single piece of data will make the models companies use more viable in the long term based on the data they capture.

Determinations like this will be the next big advance in long-term data modeling.

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