Hidden Costs Of Integrating A Bad Forecasting Tool With Your Data Warehouse

Data is everywhere and businesses are increasingly relying on data-driven decision-making. In order to make accurate predictions, companies need to integrate forecasting tools with their data warehouses or data lakes. 

Often times traditional CRM systems form a conduit to these data repositories to get the necessary historical information of customers out of the latter and help reps and managers with the demand forecasts that the historical patterns and trends indicate, but in the new era we have seen many non CRM entities also provided data warehouse suites (eg. Snowflake, Databricks etc.) that host these vast libraries of information that get integrated to forecasting tools similar to CRMs.  

Unfortunately, many companies fall into the trap of selecting a bad forecasting tool that can have hidden costs. These costs can range from inefficiencies in data analysis to missed opportunities and lost revenue. 

A bad forecasting tool is one that lacks accuracy, reliability, and flexibility.  

Accuracy is crucial as it enables businesses to make informed decisions based on realistic projections. Therefore, a tool that consistently generates inaccurate forecasts can lead to poor planning, resource allocation, and financial decisions. 

Reliability is equally important, as a tool that frequently crashes or malfunctions will disrupt the forecasting process and hinder productivity. 

Lastly, a lack of flexibility can restrict the tool's ability to adapt to changing market conditions or incorporate new data sources. 

In this blog post, we will explore the hidden costs of integrating a bad forecasting tool with your data warehouse or data lake. We will investigate what are the different aspects that you need to be abreast of when dealing with evaluating your forecasting performance with your data management processes. 

Trying to fix pipeline actually ends up leaking value from all directions  

Integrating a bad forecasting tool with your data warehouse or data lake can have serious consequences and can lead to hidden costs that you might not be aware of. Here are some of the negative outcomes a business can face when an existing data warehouse is integrated with an inefficient forecasting tool: 

• When the forecasting tool doesn't perform as expected, it can lead to inaccurate predictions, which can impact your business decisions. Making decisions based on inaccurate data can lead to ineffective strategies, missed opportunities, and even financial losses.
• Another hidden cost of integrating a bad forecasting tool with your data warehouse or data lake is the time and resources required to maintain and troubleshoot the system. When the tool doesn't work properly, it can take a significant amount of time and effort to identify the issue and fix it. This can cause delays in your business operations and slow down your decision-making process.
• Moreover, a bad forecasting tool can also impact the performance of your data warehouse or data lake. When the tool is not optimized to work with your system, it can cause data processing and storage issues, leading to slower query response times and performance degradation.

In summary, integrating a bad forecasting tool with your data warehouse or data lake can lead to hidden costs that can impact your bottom line and the overall performance of your system. 

Therefore, it's important to invest in a high-quality forecasting tool that is optimized to work with your data warehouse or data lake and provides accurate predictions to support your business decisions.

In order to dive a little deeper into the hidden cost areas that impact the business output generated from viewing your forecasts and demand cycles inaccurately due to flawed tech, you can consider the following aspects across which this can be attributed -

Let’s dive a little deeper into understanding what these areas are: 

Cost of lost productivity due to inaccurate forecasts

One of the major costs of integrating a bad forecasting tool with your data warehouse or data lake is the cost of lost productivity due to inaccurate forecasts. A bad forecasting tool can lead to inaccurate predictions, which can then result in poor decision-making by teams within your organization. This can have a cascading effect, leading to lost productivity and revenue.

Inaccurate forecasts can result in overproduction or underproduction of goods, which can cause waste and lost sales opportunities. Overproduction can lead to excess inventory that takes up valuable warehouse space and ties up capital that could be used elsewhere. Underproduction, on the other hand, can lead to stockouts, which can cause customers to turn to competitors for their needs.

In addition, when teams are forced to work with inaccurate forecasts, they must spend more time manually adjusting forecasts or making decisions based on incomplete or incorrect data. This can lead to decreased efficiency and productivity, which can ultimately cost your organization money.

To avoid the cost of lost productivity due to inaccurate forecasts, it is important to invest in a high-quality forecasting tool that integrates seamlessly with your data warehouse or data lake. This will ensure that your teams have access to accurate and reliable data that they can use to make informed decisions, ultimately leading to increased productivity and revenue for your organization.

Cost of incorrect business decisions made based on bad forecasting data

One of the biggest hidden costs of integrating a bad forecasting tool with your data warehouse or data lake is the impact on business decisions. Forecasting tools are used to predict future trends and help decision-makers to make informed choices. However, if the forecasting data is inaccurate or unreliable, the decisions made based on it can lead to significant financial losses and missed opportunities for your business.

For example, if a retailer relies on a bad forecasting tool to predict sales of a new product, it may order too much or too little of the product. If the tool overestimates demand, the retailer may stock too much inventory, leading to increased storage and inventory costs. On the other hand, if the tool underestimates demand, the retailer may run out of stock, leading to missed sales opportunities.

Similarly, if a manufacturing company relies on a bad forecasting tool to predict demand for a product, it may overproduce or underproduce, leading to excess inventory or stockouts. This can result in increased costs for storage and disposal of excess inventory, or missed sales opportunities and loss of market share.

Inaccurate forecasting data can also impact strategic decision-making, such as product development, marketing campaigns, and expansion plans. If your business decisions are based on unreliable data, it can lead to significant financial losses and damage your reputation in the market.

Therefore, it is crucial to invest in a high-quality forecasting tool that integrates seamlessly with your data warehouse or data lake. This will help you to make informed decisions that drive growth and profitability for your business.

Cost of underestimating the impact of external factors

One of the biggest mistakes that businesses make when integrating bad forecasting tools with their data warehouse or data lake is underestimating the impact of external factors. These could be anything from changes in the market to natural disasters or pandemics, and failing to take them into account can have serious financial consequences.

For example, a business might have a forecasting tool that works well under normal circumstances, but when faced with an unexpected event like a pandemic, that tool becomes useless. 

Suddenly, the business is left without a reliable way to predict future demand, and may find itself with too much or too little inventory, or unable to meet customer demands.

In a worst-case scenario, this could lead to lost sales, negative customer experiences, and reputational damage. And when you factor in the costs of replacing inventory, lost productivity, and missed opportunities, the true cost of underestimating the impact of external factors becomes clear.

To avoid these costs, it's essential to choose a forecasting tool that can adapt to a range of different scenarios and take external factors into account. 

This might mean investing in a more advanced tool, or working with a partner who can help you integrate your existing tool with other data sources to create a more holistic view of your business. Whatever approach you take, it's important to recognize that forecasting is never a one-size-fits-all solution, and that the right tool for your business will depend on a wide range of internal and external factors.

Cost of poor customer experience due to inaccurate inventory forecasting

One of the biggest hidden costs of integrating a bad forecasting tool with your data warehouse or data lake is the poor customer experience due to inaccurate inventory forecasting. Nothing is more frustrating for customers than seeing a product they want to purchase listed as available, only to find out later that it's out of stock.

When inventory forecasting is inaccurate, it can lead to stockouts or overstocks, both of which have a negative impact on the customer experience. If a product is out of stock, customers may turn to a competitor to make their purchase, potentially resulting in lost revenue and brand loyalty.

On the other hand, if there is excess inventory, it can lead to deep discounts or even write-offs, which can negatively impact profit margins. This is not to mention the potential for storage and handling costs associated with overstocked products.

By integrating a bad forecasting tool with your data warehouse or data lake, you risk inaccurate inventory forecasts, which can impact the customer experience, result in lost revenue, and negatively impact profitability. It's important to invest in a reliable forecasting tool that will provide accurate inventory forecasts to ensure customer satisfaction and minimize hidden costs.

Cost of replacing a bad forecasting tool

Replacing a bad forecasting tool can be a costly and time-consuming process. Not only do you have to invest in a new tool, but you also have to spend time migrating your data and training your team on the new tool.

In addition, if you have made decisions based on inaccurate forecasting data, you may have to deal with the consequences of those decisions. For example, if you wrongly predicted a high demand for a particular product and ordered a large amount of inventory, you may be left with excess inventory that you cannot sell.

This can result in a loss of revenue and profit for your company. Not to mention the time and resources that will be spent dealing with the excess inventory.

Therefore, it's important to invest in a good forecasting tool from the start. This will save you time, money, and resources in the long run. A good forecasting tool will enable you to make accurate predictions, which will help you make informed business decisions and avoid costly mistakes.

Conclusion and Key Takeaways

In summary, if you integrate a bad forecasting tool with your data warehouse or data lake, it could have adverse effects on your business, such as inaccurate predictions, wasted time, and lost revenue.

To prevent these repercussions, it’s crucial to invest in a high-quality forecasting tool that is compatible with your data infrastructure, offers precise and dependable predictions, and saves you time and money in the long haul.

Moreover, before incorporating any forecasting tool with your data infrastructure, conduct a thorough assessment and search for tools with an established history of success that can provide the required features and functionalities for making informed business decisions.

By following these guidelines, you can ensure that your forecasting tool contributes to your business's prosperity, driving revenue and growth through precise predictions.

If you want to talk with Aviso experts on any of the aspects covered in this blog or want to see Aviso in action, kindly contact us to see it live.