Following regulator approvals in the UK on Monday the 21st of August 2023 and effective approval in the US, Broadcom confirmed that it plans to officially acquire VMware for $61bn on October 30th, 2023.
The sector of “no-code” software is seeing unprecedented growth. It is becoming more and more popular for users to interact with applications that once required a high technical skill-set but are nowadays finding a bridge to the final users through AI & Machine Learning running interface. Wix made everyone a site developer, Canva made everyone a graphic designer. Now, it’s time to make everyone a data scientist.
In the Internet’s early days, building a site required highly technical skills. Now, tools like WordPress, or Bubble enable anyone to quickly create and launch a site, leading to 5 billion web pages today compared to less than 5 million in 2000. In the same way, data science is curbed today by a highly technical entry barrier but this field is being democratised with the emergence of “no-code” analytics tools. The goal of these tools, like Apteo or Upsolver, is to make everyone a data scientist, letting teams of all sizes and skill levels take advantage of this technology, from visualisation to predictive analytics.
The point of friction in Big Data and Data Analytics is the link between the storage of the raw data and the creation of valuable insights from using it. This link involves setting-up various layers of software between the storage of the raw data and its exploitation. This is a very complex process that requires a large range of technical tools to be set up, and a team of well-trained Data Scientists to manage these tools. Therefore, it is very expensive & extremely time-consuming.
“No-code” analytics tools provide a simple way to rapidly develop and maintain an efficient flow of analytics for companies, without the need to build complex data pipelines managed by teams of engineers. Instead, it enables easy control of all streaming data operations, providing organisations with a simple, cost-effective, and scalable way to generate insights from high-volume streaming data.
According to the O’Reilly Data Scientist Survey, half of the data scientists spend their time doing basic operations such as necessary ETL (Exchange-Transfer-Load), data cleaning, and basic data exploration rather than real analytics or data modelling, which reduces the efficiency of the process. By using no-code data science tools, businesses can take advantage of the same powerful technology that industry leaders like Amazon or Walmart use.
Data science and AI are quite complex fields to apply a “no-code” approach but various companies and start-ups have been able to utilise the power of Artificial Intelligence to push the boundaries of technology so far that the tedious work of cleaning data and making predictions from data can be automated and done without having to write a single line of code. You can work with large bits of data in seconds and automate some tasks while working around the data to save you so much time. This is to say that “no-code” machine learning enables businesses to focus on making decisions and taking action, while seamlessly providing valuable insights and predictive analytics without building large system infrastructure.
Many companies try to build products through AI and machine learning that help analysts generate new answers to business problems without requiring any code. Typically, a data scientist looking to solve a problem may be able to generate and test 10 or more hypotheses a day. With software like Sparkbeyond or Upsolver, millions of hypotheses can be generated per minute from the data leveraged from the open web and a client’s internal data.
Such a revolution will lead to a complete revision of many companies’ Business Model but in the end, the revolution through AI, Machine learning, and “no-code” tools will widely benefit companies as they will gain incredible efficiency and better use of one of their most important resources: their talents. As manual processes for basic tasks will become less time-consuming, employees will have more time to spend on interesting projects and focus on the bigger picture rather than spending half their day doing computational processes.
Using a no-code AI platform, users can drag and drop a spreadsheet of data about sales prospects into the interface, make a few selections from a drop-down menu, click on a couple of buttons and the platforms will build a model and return a spreadsheet with leads sorted, from the hottest to the coldest, enabling salespeople to maximise revenue by focusing on the prospects that are most likely to buy.
This allows anyone to effortlessly use machine learning, regardless of technical background. Forecast revenue, optimise the supply chain, personalise marketing, build personas. You can now know what happens next.
Removing friction from adoption will help unleash the power of AI through “no-code” tools across all industries and allow non-specialists to generate crucial insights into their businesses. In time, “no-code” AI platforms will be as democratised as word-processing or spreadsheet software is today.
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