Your Data Strategies Need Data Science Platforms

What’s the first thing that comes to your mind when you think of Data Science?

A moving hologram of Excel sheets, algorithms and Machine Learning programs, right? Well, in reality, these imaginations are only partially true. Data science is a vast discipline that connects with nearly every business habitat and demand. From developing personalized healthcare to accurate movie recommendations, the field continues to change our lives in unimaginable ways.

In the modern era, data science aids businesses to enhance their operational efficiency, predictive abilities and financial forecasting. It plays a critical role in various factors of a business. For example, it can help deliver information about customers’ preferences and market trends that helps businesses build more personalized marketing campaigns. With precise insights, marketers can develop targeted advertisements to boost their sales and conversions. Data science can give an insight into the customers’ average spending, preferences, sentiment on a specific campaign, sales conversions, etc.

Data scientists are like magicians who can pull out insights and useful information from a bulk of data – like a rabbit from a hat! To extract data that corporate executives, managers, and sales agents can use, they need a tool to perform statistical analysis, data cleansing, predictive modeling, etc.

Hence, to make their work easier, data scientists make use of dedicated platforms that can help them perform all these complex tasks with a few clicks. Enter Data Science Platforms (DSPs)!

In this blog, we will uncover all about Data Science Platforms; what they are, how they evolved, why they are crucial, their future and more!
 

Let’s Try To Understand Data Science Platforms

A Data Science Platform, or DSP, helps analysts or data science teams in an organization centralize the entire data modeling, analysis and reporting process. Not only does this benefit in making a well-structured business data pipeline but it also facilitates better and faster decision-making. A Data Science Platform has all the resource data scientists need in one centralized place leading to better collaboration between individuals and teams.

The main objective of this tool is to help collect data from various sources, centralize and clean it so that data scientists, analysts and operators can apply them to pre-built processing models. This tool helps them boost productivity and efficiency when it comes to data operations. Any organization can deploy Data Science Platforms to build more maturity and discipline around data as an everyday function, instead of it being limited to technical users.
 

A Brief History Of Data Science Platforms

You may think that the recent surge in data-driven approaches led to the development of Data Science Platforms. Yet this tool has been around for five decades!

In the early 1960s, the term “Data Science” was first coined to describe a profession that involved the storage and interpretation of large amounts of data compiled by businesses. Back then, there was no way of making predictions from massive amounts of data (not for the next few decades at least!).

In 1974, a book named Concise Survey of Computer Methods by Peter Naur was written and the term “Data Science” was brought to the mainstream. From that point on, many other authors wrote about it, mentioning different terms such as data analysis, database marketing, data statistics, etc. Data and data science were finally #trending!


By the year 2009, the word “Data Scientist” had emerged as a new profession. In 2011, the demand for data scientists increased, with data science has established itself as a source of profit for businesses. Further down the line, around the year 2017, due to an increase in data collection and analysis techniques, the term Data Science Platform (DSP) became a buzzword. At this point, the need for better DSPs was being discussed at data conferences and in top science publications.

The rest, as they say, is history. We mean, Data Science Platforms are the norm now. From startups to massive corporations, everyone wants one to leverage the data they have collected. Yet, what makes it so critical is how they work – let's take a look!
 

How Do Data Science Platforms Work?

Do you remember Steven Spielberg’s Minority Report, a movie where data played a major role in predicting future crimes? PreCogs, who could predict the future using real-time data streams, provided the Washington Police Force with information about crimes before they were committed. Using this insight, PreCrime Police could stop crimes.
 
On similar lines, data scientists (PreCrime Police) are provided with relevant, instantaneous insights and information related to different aspects of the business through DSPs (PreCogs). Moreover, DSPs can also look at historical data and pick out trends and patterns that can help define and explain certain changes or events, such as a sudden spike or drop in sales. How cool is that!

In addition to doing all the above, DSPs contain multiple features and resources required for executing the different phases of a data science project, including:
 

• Data Ideation, Integration And Exploration

• ML Model Development

• ML Model Deployment

• Report Generation

Whether you’re developing a supply chain for your business, managing financial risks, conducting market analysis or building a fraud detection system, Data Science Platforms will play a crucial part. These platforms allow you to store or upload business data and analyze it in various ways without the need to process it beforehand. (Talk about instant insights!). These platforms also offer a wide range of predictive tools to forecast using the collected data.

In short, DSPs are vital to find new opportunities for innovation and making predictions to future-proof your business. Moreover, with its automated features, even non-technical users can leverage the tool and don’t have to spend time learning how to use the tool!

What Are The Benefits Of Data Science Platforms?

Different departments of an organization use different kinds of data to sustain their workflows. Hence, businesses need tools that can scale across use cases and applications to provide overall utility. Data Science Platforms can help various teams perform their work at scale, providing benefits such as:
 

• Better Teamwork And Collaboration

  The productivity of your organization can decrease if your data teams are working in silos. Viewing the same issue with different viewpoints or using diverse statistical methods can provide breakthroughs much quicker. Hence, Data Science Platforms come equipped with collaboration features that allow individuals in different roles to work together on larger problems. 

• Optimized Efforts

  By using DSPs, data scientists can drive their analytical models into production without extra effort. For example, consider a scenario where a business wants to deploy a product recommendation engine. Several software engineers will be required for coding, testing, refining, integrating and deploying the model before it can show recommendations according to customer behavior. Yet, using a DSP, data scientists can do all this within the tool using synthetic data, making the feature accessible through an API quickly.

• Quicker Time To Insights

  DSPs provide simple and secure access to multiple large pools of business data. This lowers the time needed to prepare and clean the data before analysis. Hence, data-driven insights can be gleaned much faster, while also diminishing inconsistencies and errors.

We’re pretty sure that at this point you understand the importance of Data Science Platforms in the current era. Yet, if it functions this well today, what is the scope for this tool in the future?
 

Future Of Data Science Platforms

We asked the Precogs to predict the future of Data Science Platforms (just kidding!)

It’s no secret that the scope of data science is only increasing with the advancement of computer technologies and the rising number of consumers in the world. To stay ahead of the competition, every business needs to invest in data science technologies, which leads to the hiring of the best data scientists and investing in the right tools.

Several reliable sources indicate that the upward climb of DSPs’ popularity will continue. With better automation features, some predict the need for data scientists might reduce but it might be too early to say for sure. Evolution in the field of data science is certain to provide automated support through Artificial Intelligence, Machine Learning, Big Data Analytics and more innovations.

A study conducted by Mordor Intelligence expects the market for Data Science Platforms to reach USD 230.80 billion by the year 2026 from USD 95.8 billion in 2021. This shows how the need for DSPs will increase to create a faster and more accurate data environment. Moreover, Data Science Platforms will have to align with newer types of data architectures and data, such as IoT networks. The combination of new features will allow businesses to centralize data science pipelines and compete in this data-driven economy. As such, the future of DSPs is full of growth and innovation!
 

Conclusion

 

Source

The importance of Data Science Platforms continues to rise as every organization and institution realizes the benefits accurate, data-driven insights can bring to their business. We will see more data science applications being driven by automation and AI soon. Hence, the question is not “why do we need DSPs?” but rather “which DSP will suit my business needs?”