Data is at the heart of modern enterprises. The sheer amount of data enterprises collect, managers, store and use is mind-blowing. Businesses that know how to extract actionable insights from huge data sets and use it to make data driven decisions are the ones that succeed.

If you want to put all that data to good use then you will have to use artificial intelligence and machine learning to your advantage. Machine learning can help you identify patterns and trends from multi-dimensional data. If that encourages you to start a machine learning project or you have already worked on some machine learning projects then this article is for you.

Instead of learning the lessons the hard way and failing multiple times with machine learning projects, it is better to learn from the mistakes other people have made which has led their machine learning projects towards failure. This article will highlight some of the common pitfalls of machine learning projects and how you can overcome them.

Here are five common reasons why machine learning projects fail and what you can do to overcome them.

1. Not Having a Clearly Defined Use Case

Just because machine learning is red hot does not mean you should adopt it? Just because your competitors are adopting machine learning does not mean that you should also follow in their footsteps? Most businesses end up making this mistake and pay a hefty price for it later.

Instead of following the crowd or every shiny new technology, you are better off creating a feasibility report. When evaluating machine learning projects, you should ask yourself whether a particular machine learning project is feasible for your business or not? Does it bring value to your business? 

You should have a clearly defined use case and goal for the project. Start off by identifying a pain point or business problem that you can solve with a machine learning project. If there is one, develop a use case for it then start the project. If the problem can be effectively resolved by adopting machine learning, you should definitely go ahead with it. Otherwise, you can also consider other alternative solutions. 

For instance, if your business goal is to improve customer satisfaction, you can develop a machine learning algorithm to achieve this target. With the help of machine learning algorithms, you can add personalization to the overall user experience and add sentiment analysis to improve your product and service offering. This allows you to deliver a much better user experience, which would in turn increase customer satisfaction. You can also use a ready made machine learning algorithm for this purpose instead of creating one from scratch.

2. Lacking Access to Relevant Data

As mentioned before, businesses collect and process tons of data these days. Despite this, they are unable to extract useful information from huge data sets. Since all that data is scattered, unstructured, sto0red in different locations (on-premise and cloud), and accessible to a particular department, consolidating and analyzing that data is not easy. As a result businesses could not pull out actionable insights from it.

The best way to get over this problem is to create and automate a data pipeline. It can help you collect, process and analyze data from multiple sources. You also need a top of the line technology infrastructure such as a low latency network, unlimited bandwidth and tons of storage to hold all that data. 

There are tons of data management analytics tools that can help you manage your data more efficiently. You can integrate all these tools to get a consolidated view of the data and make sense of it. Most importantly, you can separate out relevant data from irrelevant one. This makes it much easier to use the data to your advantage and make data driven decisions that can give your business a competitive edge over the rivals.

3. Business Teams and Data Scientists Are Not On The Same Page

Let’s say, you have found or created a use case for your machine learning project and even hired some of the best data scientists to help you out. The data scientists will play a crucial role in training your machine learning models.The only mistake businesses make is that they isolate these data scientists from business people and teams. 

Since they rarely interact with each other, this creates confusion. Due to this, many business professionals don’t seem to be on the same page as data scientists. Similarly, business teams do not know much about machine learning models they are developing and  applications they are deploying. This creates a huge disconnect, which leads to more problems.

The best way to break these silos is to embrace the MLOPs. It will not only improve lifecycle management of the project but can also improve collaboration and scalability. Since, MLOPs forces you to create diverse teams by combining people with different skill sets and making them work towards a common objective, it increases the chances of your project success.

4. Rigid Infrastructure

As I alluded to earlier in the article, machine learning models require state of the art technology infrastructure to run on. Whether it is machine learning models, related software or applications, a rigid, underpowered technological infrastructure could not support it and crumbles under its load.

If you want consistency in machine learning implementation then it is highly recommended that you adopt a hybrid cloud. It allows you to combine on premises data with cloud, which can increase your agility and offers you the best of both worlds. Most importantly, a hybrid cloud approach gives you the flexibility machine learning models demand.

5. Complicated Software Stack Management

Machine learning development environments are complex and no one can deny that. The problem with complexity is that it can lead to inconsistencies. You can use containers or applications that allow mobile app development companies to create applications in a sandbox environment to fix this problem. This can help you improve collaboration but it can also assist you in moving applications from one environment to another.

Which is the most common reason behind machine learning project failure? Share it with us in the comments section below.