Software used to be a big differentiator in the past. Today, how you manage data is how you differentiate yourself. Because right now, sales teams, product teams, and marketing teams are taking actions according to data at the decision-making stage so that the business can move forward.

Data is getting more complex and these teams need cooperation in data-driven ways.

Companies started to see the real gains from data teams and from real-world machine learning models to large-scale A/B tests to delivering data directly to operational tools. In general, if your company works in B2B, you and every other user probably have a dashboard. In this dashboard, you are showing and analyzing the data. Therefore, there may be situations where you need to proceed with Data-driven approaches.

In order to adapt to this progress, non-technical teams, who will make the company’s next business decisions, need to make decisions faster. As a result, businesses are attempting to blend the two, resulting in a unified experience for all sorts of data analytics, including BI and machine learning. And these teams must either focus on researching their hypothesis or manually investigate all conceivable combinations of their data to achieve an accurate result when the data is complex, vast, and highly dimensional. This method takes a long time and is prone to prejudice. According to Helfert, despite the lengthy process, not one enterprise is focusing enough to ensure data quality in the metadata management, except when it is only necessary (data quality problems in Data Quality Management and proactive Data-Warehouse-Systems).

In order to speed up these processes, most companies are trying to put their data preparation and analysis processes in the pipeline. Thanks to this, companies started planning to use data-driven as much as possible. In a recent post, Benn Stancil said it: from here we can understand that there are a lot of startups popping up this year, and each of them are trying to solve a smaller and smaller part of data analytics stack. This strategy isn’t surprising — startups have to focus on solving small problems well before they can achieve their full vision.

In a conclusion, we will be able to see that a lot of new startups will pop up, and each of them will try to solve smaller and smaller part of the Data Analytics stack with their own niche methods. The use of the Data-Driven method will become more common among companies.

In this process, we will have touched on the point that will reduce the intensity of the data engineer work seen in the workflow. As Sweephy, we are trying to automate and generalize some steps in the data cleaning and preparation steps. For example, we can eliminate noise duplicates in your image dataset without requiring code knowledge, without wasting time, or we can make typo errors (spell correction) in your text data at high accuracy rates.

Disclaimer: I am the Co-founder and CEO of Sweephy, no-code data cleaning and preparing software.

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