Predictive Modeling and Data Science are two terms one would only expect to find in academic papers and debates. However, they are currently the buzzwords of the business world as large corporations and businesses use these tools to make economic predictions, based on which they make informed decisions.
A great example of how Data Science and Predictive Modelling can help a business make better decisions comes from Amazon. They use these tools to predict financial performance, product demand, and the type of resources required, and the result is easy to see as Amazon continues to be one of the giants of the e-commerce industry.
But there are other examples that speak of the power of accurate data and well-designed analytics tools. Let’s take the Airbnb Aerosolve model designed to help listers choose the best rate for their rental. This system uses a wide range of factors collected from the field (seasonality, amenities, location, and more) and compiles the results using data analysis algorithms and machine learning technology.
Moreover, both these examples could end up having a lot more applications once the world opens up to the idea of predictive modeling and data analysis. In short, it is safe to say that the use of these technologies won’t die down in the future, but it’s important to understand their effects and implications.
Easy Access for All Businesses
The good news is that businesses can collaborate with a data science consulting service in order to get a predictive analysis of their practices and models. This opens the door to all types of businesses, of all sizes since it’s a lot cheaper to outsource the service than hire an entire team or purchase a BI platform.
As a result, we should notice an increase in data-informed decisions and more products that follow customers’ preferences. Moreover, highly scientific tools will be made into more user-friendly platforms, to increase access from all types of industries and walks of life.
Less Guess Work
Without access to proper data and analytics tools, everyone involved in the business process has to put their intuition to the test, at some point.
Let’s consider the departments of marketing and sales. In marketing, specialists must first identify their ideal customer persona and then seek ways to understand their wishes and desires. Once marketers think they have an idea of the type of products and services a customer might want, salespeople get to work and try to convince real people of the benefits of becoming customers.
Without access to data collected from the field, the activities mentioned above require a lot of guesswork even when performed by highly-trained specialists.
Given the high rate of success when using Data Science and Predictive Modeling in combination with other high-tech tools such as AI and ML, it would be easy to expect more out of the business world.
However, there are a few issues that can bring development and growth to a halt if not considered.
The term data and even Big Data is as general as they come. Moreover, the content of a data set is different from project to project and it’s difficult to perceive by the untrained human mind. Still, even if you’re not a Data Scientist, you should know that the results of predictive analysis are as accurate as the quality of the data used.
For instance, high-quality data needs to be consistent in its format and must reflect a real-world scenario in order to be usable.
Data quality can be influenced by the collection methods and the storage methods. Therefore, without proper data management solutions that allow the processing of humungous amounts of data from a wide range of collection channels, you can’t get useful results in the next steps of the analysis process.
In summary, data collection and processing is a practice that won’t go away anytime soon. However, users also need to understand that these tools aren’t magical and can’t work unless the proper procedures are followed.
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