• Understanding processes that should be automated

Before deciding to use machine learning algorithms, one has to understand problems that exist and evaluate the ones that should be solved using this technology from the others. Start by finding the processes that are done manually with no variable output and are repetitive in nature. Complicated processes need to be inspected before automating them. Although ML can help in the automation of some processes in your business, not every process can work well if automated. This is what many people fail to understand before embarking on the automation process.

• Poor quality data

As effective as machine learning can be, lack of quality data is making things harder. Machine learning algorithms require properly sorted data which is essential for quick decision-making. Some of the issues facing data currently are dirt, incompleteness, and noise. All these aspects are the true enemies of the ideal environment for machine learning. The solution to this problem is to evaluate and scope data by enforcing the right data governance policies.

• Inadequacy in infrastructure

Machine learning requires extensive infrastructure to accommodate the vast amounts of data that originate from various sources. Traditional systems have no capacity to handle the massive amounts of data that originate from diverse sources such as social media, call-centers and websites. Some organizations often fail to understand the capabilities of the available infrastructure before embarking on machine learning initiatives. You must understand your infrastructure and their potential to handle machine learning.

• Irrelevant or unwanted features

Machine learning depends on training data for it to be successful. However, if the training data contains irrelevant features or is not accurate enough, the results that were expected will not be given. For machine learning projects to be successful, good features must be selected to train the model.

• Inaccessible data and data security

Availability of data is one of the leading machine learning challenges that is faced by businesses. Raw data availability is important for companies as it gives them data to train their algorithms. For proper training to be conducted, huge chunks of training data are required. Little amount of training data or items will not be sufficient to train models and correctly implement machine learning. While gathering data is one of the problems, it is not the only one. Data are needed to model and process data to suit the algorithms that you need. Secondly, data security is also another issue in machine learning. Once you have the right data, security must be ensured to guard information against unwanted access. This begins with differentiating sensitive data from insensitive ones and understanding how machine learning can be implemented appropriately.