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:
- Great amount of time and capital wasted on simply generating, preparing and labelling data
- Lack in data maturity can lead to curating unrelated data that proves to be irrelevant to your goals
- Poor data quality, due to lack of consistency and completeness, lengthens the curation process
- Incompatible data formats difficult to fetch especially when the number of sources is high
“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”:
- Better communication between the IT department and the business units to properly contextualise the objective
- Proper mobilisation of resources for the entire organisation with careful planning
- Investing in a modern data management platform that exponentially advances your data maturity process
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:
- Real-time availability of data (offers the ability to generate data-driven decisions with current and historical data)
- Built-in historisation (makes it possible to consider the whole history of change from a dynamic perspective)
- Rich real-time transformations (unification of the data so that you can create your combined data sets from any available source)
- Scalability (ensures a effortless end-to-end journey for your data)
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!