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Machine learning (ML) depends largely on the availability and quality of data. This is the most crucial aspect that makes ML algorithms possible and it also explains why machine learning has become so popular lately. However, regardless of the actual volumes of data and information available, if you cannot make sense of your data records for ML, it may nearly be useless or even harmful. The actual thing here is that all the datasets may have some flaws.

This is a reason why data preparation is a very important aspect of machine learning projects. Data preparation consists of procedures that help you make datasets more suitable and available for machine learning. In broader terms, a project which may include establishing the right type of data collection mechanism may consume most of the time in machine learning projects. Sometimes it may take even months before your first ML algorithm is made.

Collecting data for machine learning

There is a fine line that divides those ready for machine learning and those who cannot take advantage of it. Some organizations may have tons of records from their decades of great success and now need to load those onto the cloud. For those who are not into data collection for long, there are many ways to turn this disadvantage into an advantage. At the first point, you may rely on the open-source datasets for initiating machine learning execution.

There are huge piles of open data available for machine learning, and some companies like Google are also ready to give it away from their data for your projects. We are talking about public access opportunities. Even when these opportunities exist, usually, the actual value comes from the internal storage of datasets.

Not surprisingly, you have a chance to collect this data the right way from companies that have started data collection processes with paper ledgers or maybe using spreadsheets or .csv files. You may even be likely to have a hard time preparing data for machine learning projects. However, if you know the tasks that machine learning can solve, you can tailor set these types of data also gathered in advance.

When it comes to big data, it also seems like everyone should be doing it now. However, aiming at the big data platforms with a good mindset is not all about having petabytes of data. On the other hand, it is about processing the data in hand in the right manner. It is facts that the larger your data sets are, the harder it may be to make the right use of them and yield actionable insights. Having tons of data is does not necessarily mean that you can convert all these into an actionable data warehouse full of insights. So, beginners’ basic recommendation is to start small and reduce the complexity to build up slowly and steadily. For support in this regard, you may approach providers like

Articulate your problems in advance

Knowing what you need to handle will help you to decide what types of data may be more valuable for you to collect. While formulating your machine learning problems, conduct proper data exploration and try to think in various classifications, regression clustering, and ranking, which are usually talked about. These tasks can be effectively differentiated in the following manner. 

  • Classification– You may want the machine learning algorithms to answer binary yes / no questions, or you may want to make the complex classification. You need the right answers to be labeled so that the ML algorithm may learn from them.
  • Clustering – You may want your algorithms to find rules of classification, number of classes, etc. The major difference from the classification tasks is that you may not know the groups and principles of their divisions. For example, it usually happens when you have to segment your customers and make a specific approach to each segmentation based on the characteristic.
  • Regression– You may want the algorithms to reap some numerical results. For example, suppose you are spending too much time getting the right price for your products as it depends on variable factors for regression algorithms. In that case, it may help estimate the value correctly.
  • Ranking – Machine learning algorithms may also help rank the objects based on various features. The ranking is effectively used to recommend movies or video streaming services or show most of the popular products that customers may purchase with high probability.

With all these types of algorithms in action, likely, your business problems can effectively be solved with this simple segmentation of data sets accordingly. As a rule of thumb, you have to avoid handling any other complicated problems at this stage.

Establishing proper data collection methodologies

To create a data-driven culture in the organization, you need to combat the need for fragmentation. It may not be possible always to convert all the data streams into a central storage system if you have different channels of acquisition, engagement, and retention of data. However, in most cases, it is manageable. Collecting the data is the work of a data engineer, a specialist responsible for data infrastructure creation. However, you may also engage a software engineer or an administrator who has database experience to do this task at the early stages. There are two types of data collection mechanisms.

  1. Data warehouse and ETL

The first approach in data collection is depositing all the data into warehouses. These are storages created for structured data records and can fit into the standard table formats of SQL-based databases. It is very safe to store your sales records, payroll, and CRM data in this category. The traditional attribute dealing with data warehouses is transforming the data before loading it to the DB. However, it means that you know which data you need and how it should look so that all the processes are done before storing. This is called the extract, transform, and load approach or ETL. The major challenge with the ETL approach is that you may not always know which data is useful and not useful. So, the warehouses are normally used to access the data through business intelligence.

  1. Data Lakes and ELT

Data lakes are storage systems that are capable of keeping both structured as well as unstructured data. Data in the form of images, sound records, videos, PDF files can all be included. However, even if the data is structured, it is not transformed before storing. You can load data here as it is and decide how to use it later or process it on demand. This approach is known as extract, load, and then transform as you need later.

While considering the differences between ETL and ELT, you can find that both are fit for machine learning. However, if you are confident in at least some of your data, it is worth getting prepared. You cause to use it for analytics before starting the data science initiatives. Nowadays, the modern cloud data warehouse application support both ETL and ELT approach. So, you can choose among these based on your specific needs and the structure of your data.