When It Comes To Citizen Data Scientists, Where Do They Fit Into The Picture?

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The rise of “citizen data scientists” is due to the huge growth in data technology over the last few years, which has led to their rise (CDS).  This is because of new developments in augmented analytics and artificial intelligence (AI) automation. (data science in Malaysia)
“Who makes or builds models that use advanced diagnostic analytics or predictive and prescriptive capabilities, but whose main job is not in statistics or analytics?”

Gartner says, automated business analytics solutions that use AI and machine learning (ML) are likely to grow CDSs. Augmented analytics is a big word in the global industry right now, and it’s pushing the “unicorns” to high-profile, special projects. If you go to work five years from now, you might see a CDS do all the simple and even some advanced analytics tasks. You might also see the use of ML at a level where mid-sized and small businesses will have access to ML-powered.  You will see augmented analytics platforms that “amplify” human knowledge and skills, such as Google Analytics. This means that humans and computers will work together more often in the future.

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In the current trends in enterprise analytics, who will get the most out of them? Data Scientists, of course, because there are already not enough of them around. With the CDSs taking care of all the simple Data Modeling and Analytics tasks, the data scientists will be able to work on more complicated projects. In one of Forbes’s posts, the author says that data scientists, CDSs, and SMEs work well together in advanced analytics. This is because they all have different skills.

In contrast to SMEs, CDSs use ready-made tools to “create more ‘ah ha’ moments” with data, algorithms, and models. However, CDSs can’t meet the needs of very complex analysis, and data scientists must be present to help with advanced Data Modeling. There are fewer and fewer data scientists, and they’re getting more and more expensive. In the future, only a few will be able to help large teams of SMEs and CDSs.

The Data Scientist vs. the Citizen Data Scientist: Which one is more important? (data science in Malaysia)

In fact, CDSs are growing “five times faster in number” than data scientists. This is a clear sign that automated ML packages and augmented analytics software are working. Gartner has said that the Citizen Data Scientist (CDS) makes models for predictive or prescriptive analytics, but this person’s main job is not in the field of analytics or statistics. Analytics Translator vs. Citizen Data Scientist: What’s the Difference? It doesn’t matter that the CDS isn’t trained to be a computer scientist or data analyst. This allows the CDS to perform routine analytics and BI tasks without the help of a data scientist.

Citizen Data Scientists and Strategic Data Management are combined in this way.

As a good way to think about the collaboration between data scientists and CDSs, it might be helpful to think about how the best Data Management platforms work well with data scientists’ wisdom. Besides, how CDSs know how to do their jobs. As more CDSs learn about data, the less they will need data scientists. The automated data platforms will help CDS learn more about how to do better Data Management tasks. The number of specialised data scientists will go down, and only a very small number will be available to an organisation for “validating and putting into practise models, findings, and apps.” . Gartner says that to make the collaboration between the data scientist and the CDSs work well, you should build the right infrastructures.

A Datanami author says that machine learning should be made more human (ML). This is a new idea that shows how “augmented intelligence” can be use in a human team of business analysts. It’ll be easier for humans to work with ML tools and “explore their data” in a “humanised” ML platform. The author of this post, Nathan Korda, is the Director of Research at the University of Oxford’s ML spin-off, Mind Foundry. In this post, he talks about how humans will be able to “input, cleanse, and visualise data in minutes” for more data exploration. This kind of augmented analytics platform might help business owners connect the ML skills they have to real business value. Small businesses can also benefit from the power of ML thanks to the rise of CDSs, which allow them to use it for profitable business insights.

Business Intelligence is the field where citizen data scientists are the future.

People who work with data have already found that the CDS can help them do their jobs better. An e-book called The History of Business Intelligence and its Evolution shows how organisations are gradually trusting CDSs. It is to use automated BI tools to get data-driven insights from their own data. In other words, the CDSs can do “cool things with data” even though they don’t know a lot about statistics or math. The modern AI-powered BI platforms and tools make it easier to do advanced analytics tasks by automating them.

 

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