Nowadays, lots of buzzwords are being regularly tossed around – AI, Data Analytics, Models, Machine Learning, and many others.

The common fact for all those fields is that in order to produce a worthwhile product in each one of them – the data needs to be processed, transformed, and made ready for consumption. Anyone who works with data knows that data preparation consumes a considerable amount of time and effort. The US former chief data scientist, DJ Patil, said in 2016: “80% of all data projects are data cleaning”.

If that’s the case, those who work with data know that data preparation and optimization is a time-consuming and challenging task involving multiple stages and questions that must be answered for the process:

1. In order to understand the dataset, a lot of time is invested in a manual examination–
   –How many records and columns are there?
   –What data types are in the dataset?
   -How many invalid values are there? What should we do with them? etc.
2. How do we want to shape the data? What will be its final form?
3. What needs to be cleaned from the data?
4. How do we enrich the data? What other table and data sources should we join in?
5. How do we ensure that the data is processed in the desired way?
6. How do we run this process?

Even though these stages reoccur in every data preparation process, in order to get the desired outcome, the process will probably include several iterations of trial and error. In addition to that, we must remember – when executing each of these stages, each and every test/repair/process we want to execute in the future will require us to run the process on the data several more times to indicate whether our actions achieved the desired result. Even then, when we execute the final process, we’ll get more than one error that will send us back to go through what we did to understand why it doesn’t work.

Data preparation takes a lot of time and, in many organizations, requires the comprehensive and precise work of data engineers or IT teams, so they become a bottleneck. This process prevents the organization from being flexible and efficient. Another important point is that not once do substantial mistakes happen during the data preparation process, whether it was due to a lack of business knowledge of the IT/data engineers regarding the data they are working on or due to the organization’s business procedures. This can also cause us to go back to the beginning of the process.

This is a slow, tedious process, and only when it’s over is it possible to start thinking about the real goal we want to achieve – gaining insights from the data!

This is where Trifacta comes into play – they developed an artificial intelligence-based self-service data preparation tool, which provides an easy no-code data preparation platform that shortens the preparation work substantially, making it more accurate while narrowing the margin of error.

The company built its tool for business function workers such as analysts, data scientists, and sometimes even data engineers, who understand the data they are working on and its essence. The tool allows them to perform data discovery, cleaning, processing, and enrichment of the data, without the involvement of IT (while the processes are still under IT monitoring), which often becomes a bottleneck.

Trifacta’s strength is in its ability to perform smart data transformations. Trifacta enables us to see a preview of our actions on the data before running them and offers AI-based follow-up steps and transformations, data profiling, and more. The question is not necessarily “Can this be done without Trifacta?” but “How will this process look with it?”.

This means that instead of spending precious time on understanding the data, repeatedly running jobs on it and monitoring the entire process (especially when working with large amounts of data), then examining the right way to perform these data processes with code, and investing both IT and the business users time (since we need both the technical and business knowledge), Trifacta provides the business user the technical abilities in a simple, convenient, and intuitive way while allowing the IT to monitor the process.

Closing the gap between business and IT

According to Aqurate Account Executive, Or Mizrachi, Trifacta is not among the market’s leaders for nothing. It was acknowledged by research companies such as Forrester and G2, and even Google chose Trifacta as its data preparation tool. Trifacta enables the business user who knows their data the ability to work on it independently, with the monitoring of the IT department. By doing so, Trifacta closes the gap between business and IT and enables a unified organizational truth, with no Shadow IT that nowadays happens in organizations. Trifacta uses machine-learning components and studies the way the user and their teamwork. By using all of those, Trifacta enables a quick time to market and shortens the time that is usually wasted in data projects on the stages of organizing, understanding, and preparing the data.