You already know how to collect data and you went through the whole process of gathering the information you needed.

Your next step will be to pre-process (prepare) your data. This will ensure that your models will be fed with quality data securing valuable insights.

Data preparation is an important and complex task that includes data cleansing, labelling, augmentation, aggregation and identification. However, how much time do you usually allocate to such a task? Does it often take away from the time spent to achieve the actual purpose you have set for the data in the first place?

In a recent article, IBM highlighted the severity of the “80/20 Data Science dilemma”, underlining that data scientists spend only 20% of their time building and training models. So where does the rest of their time and efforts go?

Data preparation takes 60 to 80 percent of the whole analytical pipeline in a typical machine learning / deep learning project” says InfoQ. More time spent on pre-processing means less time to generate valuable insights. Why? These are the pain points that more often than not lead to unwanted delays for your projects:

Experts’ Recommendations:

Rather than being limited to working on one model at a time, the goal is to give data scientists the time they need to build and train multiple models simultaneously.” - IBM

As such, here are some escape routes from this “pitfall”:

Leveraging on an ingestion framework or a streaming product is a done deal when choosing to invest into the right data management platform. The market is filled with such products, so choose the right one by following this small checklist:

Data engineers can generate much more value if they focus on high-end tasks and they can do so with the proper tools. Harmonising data can reduce the time needed for the pre-processing stage.

Cleaning the data should not feel like a dreadful task as the right platform will have an elevated ease of use and boundless accessibility. Opt for a low-code/no-code platform that is out of the box and cloud-ready to guarantee a faster deployment of your projects today and tomorrow.

Make the exploration of data an enjoyable journey for everyone, anywhere!