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INPUT by Alteryx Blog

Machine Learning in Data Analytics

As more and more businesses are accelerating their cloud as well as digital transformation, it is necessary now, more than ever, to make use of the best available technologies to stay ahead in the game.

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Data, data everywhere

The amount of data that companies collect and analyze today is unimaginably huge. Big brands like Netflix collect information about when users pause, fast-forward, or rewind an episode, what time of the day or week you watch the most content, what makes you stop watching, and so on. They use this data not only to curate your recommendations but also to create new shows. A famous example is the hit series, House of Cards, created by Netflix using big data.
But with the amount of data being generated everyday way more than the amount that you can process, how do you catch up?

Machine Learning can do wonders for your analytics processes

In 2022, there is hardly a soul who hasn’t heard the terms AI or ML. Machine learning is a type of artificial intelligence that performs complex (or even simple) tasks in a way that we humans generally would – but only faster, and perhaps even better.
With machine learning techniques, there is no limitation on sources of information — data in all forms, such as images or PDFs can be analyzed easily. Using neural networks, it is possible to recognize and classify objects from pictures even when photographed from weird angles and in different kinds of surroundings.
You've seen Instagram ads pop up on the very same item you searched for on your laptop last night, right? That is possible because of Google's ability to leverage ML and AI processing to turn your search (and even speech!) history into useful data for targeted advertising. Most of this data is unstructured and extremely tedious for employees to sift through manually, despite using Python scripts or tools like MS-Excel. In addition to this, the investment that needs to be made up-front, in terms of computing and storage infrastructure, is quite high.
The applications of ML in data analysis are vast. It makes possible the analysis of millions of combinations of different chunks of data, using algorithms that not only make the process more efficient, but also learn and adapt to the data sets constantly. And as more and more businesses are accelerating their cloud as well as digital transformation, it is necessary now, more than ever, to make use of the best available technologies to stay ahead in the game.
But is there enough skilled manpower to do good analysis, on such a large scale? Given that it is the highest employable skill in 2022, you would think that there is a huge demand for Data Scientists and Data Analysts. Perhaps you need to hire the best ones before some other company can chase you to it?
That’s no problem at all!
Although Data Scientists are a big asset to any company, it is not necessary that your business needs a highly qualified data scientist for every data-related position. What you need, actually, are Citizen Data Scientists. They are custom-trained employees who have data science skills like statistics, but not necessarily as advanced as that of a Data Scientist. Any gaps in your business processes can be easily filled by them.

Do Citizen Data Scientists need to know coding?

With data science transitioning from something used solely for research into one that has use cases across all industries and business purposes, there has been an increasing need for easily understandable data analysis models. This has led to an adoption of low-code (or even no-code) platforms, which can be used by you and me (potential Citizen Data Scientists!) to build advanced data analysis models using AI/ML.

Low-code platforms can be used to integrate APIs for data collection from a plethora of sources on the web. This makes data scraping virtually effortless with no necessity for additional infrastructure. No development of serves or databases - simple sit back, and use some pre-built connectors to get your hands on all available data sources! Once that is done, these platforms can cleanse the data to make it human-readable, via automation.
By using simple drag and drop features, Citizen Data Scientists can then visualize the data and customize app development. As simple as that.
In a way, it’s actually better for your business!
Since CDSs are from a specific department, they have a great understanding of the needs of their department and therefore add more value to the organization than a conventional data analyst. Given the right tools, they would know exactly where and how to press to identify pain points in their business processes, and put collected data to optimal use.
Low-code platforms for data analysis gives CDSs easily accessible field data in an aggregate manner, visualizes uptake of the field application in real-time, and allows them to drill down the specifics in as many ways they want to.

Alright, what else can low-code platforms do?

Well, they can help you deploy ML solutions for your data analysis problems with zero knowledge of development. Be it to analyze emerging trends in the industry or to setup a chatbot that interacts with clients for you, you can do it all without writing (or even understanding) a piece of code.
You can also use them to generate business insights through trained ML models. The results from these models allow you to build recommendation systems, akin to those from Netflix or Youtube, that suggest users to something to watch next based on their preferences. With a tad bit of mathematical understanding, you can easily develop such recommendation systems on low-code platforms.

Will I need an actual data scientist, then?

You can always use an expert data scientist when you run into errors that does excessive debugging, need to heavily customize your algorithms for complex problems, or optimize your solutions on large scales. But for everything else, a CDS working on a low-code platform would be more than sufficient to make your data-driven business run effectively.

How do I get started?

Follow the lead, from influencer Bernard Marr, who reskilled their existing staff at Sears, to perform above average Excel stuff, without hiring data scientists, in order to optimize their customer segmentation.
However, data analysis doesn’t stop with Excel. As the CEO of Noogata rightly said, data consumers should not be expected to have the skills to develop complex algorithms by themselves or apply ML tools efficiently. Instead, they should be supplied a simple way in which they can integrate advanced analytics into existing processes.
And Alteryx provides that, and a lot more.
Alteryx Machine Learning and other supporting parts of our platform come with powerful models than can unlock different patterns in your data. Its explainable AI provides results in a form that anybody can understand, and options to share your insights with stakeholders. Alteryx believes that open-source technologies are imminent in innovation, and most methods that it uses for ML are based on its open source packages that are available on GitHub.
Be it regression models for prediction of sales in product companies, detecting fraud in insurance companies, identify risk factors in patients in healthcare centers, or prediction of stock prices – Alteryx makes it all super easy, by providing you with one Unified platform for analytics, data science as well as insights driven by AI. It is a dream come true: building, training, and deploying ML models – all without leaving the user interface!
One of the best things about Alteryx Machine Learning is that it is in Education mode, which means you can learn to use it as you go. Your employees wouldn’t have to spend a significant amount of time on getting trained before they start using it.
With Alteryx, building your own models is easier than ever
Alteryx Machine Learning and other supporting parts of our platform come with powerful models than can unlock different patterns in your data. Its explainable AI provides results in a form that anybody can understand, and options to share your insights with stakeholders. Alteryx believes that open-source technologies are imminent in innovation, and most methods that it uses for ML are based on its open source packages that are available on GitHub.
Be it regression models for prediction of sales in product companies, detecting fraud in insurance companies, identify risk factors in patients in healthcare centers, or prediction of stock prices – Alteryx makes it all super easy, by providing you with one Unified platform for analytics, data science as well as insights driven by AI. It is a dream come true: building, training, and deploying ML models – all without leaving the user interface!
One of the best things about Alteryx Machine Learning is that it is in Education mode, which means you can learn to use it as you go. Your employees wouldn’t have to spend a significant amount of time on getting trained before they start using it.
Machine Learning Education Mode

To learn more, check out Alteryx Machine Learning. 


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Rishi Kapoor
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