Did you ever wonder whether knowing how to use a trend model is still relevant today?
In this article, you will learn more about trend models and why they are still important.
Table of Contents
Introduction
Business data has the potential to fuel a range of important business decisions. But what about forecasting? Can data scientists help companies leverage the data to predict future trends in the company, markets, or even on an industry level?
This is where knowing how to create a trend model can particularly come in handy. Data scientists can help businesses optimize internal processes, become more competitive, and even future-proof organizations against certain risks with proper knowledge and tools at their disposal.
Should Data Scientists expand their knowledge on a Trend Model and why? Even though it’s clear that data scientists focus on-trend models, let’s see why.
What’s at the core of every trend analysis?
Have you ever heard of trend analysis? A more technical term for trend analysis is technical analysis. A definition of technical analysis is straightforward:
“Technical analysis is a study of historical data.”
Historical data can be any of the business KPIs ranging from departmental productivity to volume of revenue streams. You can also compare two trend analysis reports to spot any significant differences. Historical data helps data scientists identify patterns and make timely decisions.
But what is this historical data that we keep mentioning? Historical data is, in fact, represented through a trend model. Most often, it is a linear trend model, but it’s important to note that more advanced trend analysis may leverage other types of trend models such as exponential, quadratic, and trends with a changing slope.
Trend model as the only way to keep track of time-series data
The thing with variables in any data set is that they change. It makes it tricky to compare different values of one variable without using visual aids. To do a trend analysis, we need to build a trend model. A trend model is a visual representation of a variable in the function of time.
Every data model that you see represents a single data set in the function of time. Simply put, the X-axis represents the time, while the Y-axis represents the value of the variable you are tracking. With a quick glance, you can see how the variable changed over time.
The value of a trend model is not only in helping us understand the current and past trends in business performance but also in forecasting performance in the future. We used a performance here only as an example. It can be literally applied to any data set. For instance, you can build a trend model of market demand and supply and use it to do technical analysis.
That’s precisely why knowing a thing or two about what it is and how to use it will render every data scientist more valuable on the job market. In other words, there is no technical analysis without it. While this might be an obvious benefit of using trend models, here are a couple more.
Benefits of using a trend model
The value of using trend models is huge. Some of the most noteworthy benefits include the following:
> Versatility — a trend model doesn’t differentiate data. You will be able to use it with a plethora of numerical data types. If you need to use traditional data, trend models can be of help. For instance, you can use it to track the changes in your profit or to get insight into your expenses. Or, you can use models with alternative data. For instance, you can track your site performance,
employee turnover, repeat business cases, and more;
> Instant insights — one of the best things about using trend models must be instant insights. Plus, a company doesn’t need a data scientist to build trend models of structured and clean data.
This especially applies to traditional data. Trend models provide instant insights into business performance, and you can use them to ensure that your current strategy delivers expected results;
> Easy pattern recognition — recognizing patterns by going through numbers in a table is borderline impossible. However, when you use those same numbers to build a model, you can easily recognize patterns. Even the most complex data tables can be broken apart into logical entities and visually represented in a model in the function of time.
> Fuel trend analysis — measuring the performance of your business can be hard if you want to ensure the accuracy and validity of data. This is where trend analysis comes in. And, as we’ve previously established, you need to build trend models to do it. This fact alone renders knowing how to use models a must-have skill in today’s market. It can help you forecast developments in
the market, assess the company’s direction or even make informed investment decisions.
It’s important to keep in mind that the value of trend models and technical analysis depends on the information you have. In other words, you have to ensure your data is accurate and on point. This can become challenging if you are working with data streams in real-time. Ensuring that you have enough data and that it is accurate will help you benefit from trend models and technical analysis.
So, should data scientists expand on a trend model? Yes, they should! Knowing what trend models are and their role in forecasting can better help you understand technical analysis. Plus, you’ll understand the importance of recording, storing, and cleaning historical business data.
Conclusion
Historical data can be any of the business KPIs ranging from departmental productivity to volume of revenue streams. You can also compare two trend analysis reports to spot any significant differences. Historical data helps data scientists identify patterns and make timely decisions.
The value of a trend model is not only in helping us understand the current and past trends in business performance but also in forecasting performance in the future.
We hope this article is useful to understand why Data Scientists should expand their knowledge on a Trend Model.
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-Charbel Nemnom-
