Businesses have struggled to accurately predict industry trends for years. New data analytics capabilities have seemingly made the process much easier. The global market for data analytics is expected to reach $77.34 billion by 2023. Demand for data analytics is driven by its proven track record for solving business challenges.
However, it is important for brands to ensure they are relying on the right data to forecast these trends appropriately. Decision-makers should not even attempt to analyze trends without leveraging real-time data. There are a number of ways that real-time data can be incorporated into analytics models. Which of these approaches is ideal? The reality is that you can’t pinpoint a single one as being universally best. You might need to use different analytics models, depending on the nature of any given problem. Some of the better report automation tools offer these reporting options.
Real-time data can be utilized with analytics in several important ways. Forbes has discussed these benefits and new tools that address them. You are going to need to identify the best real-time analytics approach for every given situation.
The growing role of real-time data in business forecasting
Influencer Marketing Hub posted a very insightful article on the importance of real-time analytics. The article showed some examples of ways that real-time data can be used. For example, real-time analytics is able to track active social media users on a given platform at any given time. At the time the article was published, there were 2.32 billion users on Facebook.
Decisionmakers can obviously use real-time data to make some very interesting observations. However, these observations are of a little relevance without appropriate context. Historical data provides that context, so longer-term trends can be assessed. At the same time, historical data may not be of any use on its own.
This means that analytics models need to account for both real-time and historical data to assist businesses with decision-making. Here are some challenges that business leaders need to overcome when utilizing real-time data in their trend forecasting.
Real-time data might need to be weeded in any analytics model
Assigning values to data sets is complicated. Many analytics experts attribute the same value to every data point available to them. This is obviously a poor approach.
What is wrong with weighing every data point the same? The biggest issue is that the value of data can degrade over time. Data from 10 years ago might still have some relevance, but it’s not going to be nearly as useful as data that is being collected in real-time. Real-time data might be much more indicative of a long-term trend.
Real-time data might sometimes need to be discounted based on noise
The preceding paragraphs emphasize the fact that more recent data tends to have more value, so it’s usually given greater weight in analytics models. However, there are sometimes going to be exceptions.
Data that is being collected in real-time might deviate significantly from the margin of error that would be expected under the long-term trend. Your analytics model could be compromised if you assume that it has more value than older data that was not collected during an unusual where there was a lot of statistical noise.
Analytics models need to be trained to identify problems that could cause values to fall outside the scope of the long-term trend. This is a complicated challenge to tackle, but modern machine learning programs are adept at figuring it out. You need to use the right neural learning tools and make sure they are constantly looking for statistical anomalies to account for.
Real-time data must be collected from the right sources
Even the most experienced business leaders often become too enamored with the luster of new technology. New technology itself can be very useful. However, technology needs to be applied correctly.
This lesson is just as relevant with real-time data technology as anything else. You don’t want to make the mistake of collecting certain types of data in real-time without appreciating its value in your decision-making model.
Real-time data is very important for business decision making – but it must be used appropriately
Real-time data is the biggest breakthrough in analytics technology in the past decade. It can play a very important role in business decision making. However, there are a number of mistakes that decision-makers and even experienced analytics experts make. You need to pay close attention to avoid them.