People have always been interested in knowing the future. Astrologists, soothsayers, and palm readers exist primarily because of our desire to know the unknown, in part because we believe it can help us plan and organize our lives. On a collective basis, this has given birth to different types of forecasting that benefit the society at large.
Case in point: Weather forecasting has evolved from wise people judging seasonal events to dedicated meteorological departments with sophisticated tools. Their scientific weather forecasts can cascade to farmers, government bodies, and coastal area communities to help them better plan and take actions for the benefit of crops, budget, and safety.
This need for businesses to anticipate the future has given rise to advanced analytics departments, predictive tools, and one of the hottest professions of this century: data scientist. Data scientists are akin to modern-day soothsayers who interpret large amounts of data and identify ways to improve business operations. Business forecasting is essential for corporate planning, growth, and competitive advantage. It helps with estimating sales, expenditures, and most importantly, margins.
Key points for effective business forecasting
If you’re planning to leverage forecasting for your business’ benefits, keep these key points in mind.
- Know your business and industry. Observing the business and ecosystem in detail is crucial to forecasting. It helps you understand trends, seasonality, and factors that impact the business. If you are an outsider or new to the business, you can better understand the organization’s nuances by collaborating across different functions and talking to subject matter experts.
- Clearly identify your objective and the problem. What exactly do you want to forecast? It is important to define a clear problem statement that is relevant to your industry. For example, an oil company may be interested in understanding demand to predict barrels it needs to produce, and a retail or e-commerce player may want to know the appropriate amount of stock on hand. Once the problem is clearly defined, it can be divided into subproblems.
- Get the right data. It is paramount that you get the right data in the context of the problem. Data quality needs to be continuously monitored and measured. The quality of training data can impact projections greatly, and it is good practice to audit data periodically.
- Test different approaches, models, and algorithms. Exploring different models is a key scientific aspect of forecasting. Forecasted results using historic data should be continuously tested with actual data and detailed error analysis.
Even after following the above steps, your forecasting accuracy may vary. It is important to learn about the factors that cause fluctuation in results.
The journey to know the unknown can be long and time-consuming, but it is immensely rewarding.
Big Data is morphing into Vast Data. The next generation of the technology will lead to insights and correlations that reveal new strategies—even new business models. Learn more about gaining deep insights from data lakes.