How to Democratize Data Science

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democratize data science

Efforts to democratize Data Science can be described as creating an environment that allows people with little expertise to perform Data Science research. This approach can be especially useful for businesses desperate to access the skills of a data scientist, but unable to hire one. A variety of user-friendly analytics tools have become available to support staff members in initiating and completing Data Science projects. 

There are two basic reasons to democratize Data Science: the shortage of data scientists, and the concern that the data scientists who are available do not understand the organization’s specific business needs. The argument can be made that businesses often have numerous data-driven business problems, which are resolved by managers and small teams of workers. Because these people have a good understanding of the business and its needs, they should have a greater influence on the Data Science decisions being made.

Data scientists may not have a good understanding of the business’s more subtle needs. 

The democratization of Data Science is made possible by a growing number of new user-friendly tools that can automate several of the processes used in Data Science. Examples include creating algorithms, massaging data, and creating the code used to deploy models for production.

Useful Software to Democratize Data Science

Businesses that want to begin performing Data Science projects are facing difficulties in finding talent. To avoid having their goals blocked by the shortage of data scientists, organizations should consider using a multipronged approach in the process of democratizing Data Science. This would include the use of automated tools, no-code tools, pre-trained machine learning models, self-service analytics, and training staff. 

Best Approaches and Tools to Use

Automated machine learning: Various tools designed to automate Data Science tasks have become available in the last few years. Organizations can use these new Data Science automation tools aggressively, empowering staff to perform tasks normally assigned to a data scientist. These tools make it possible to democratize Data Science. 

Listed below is limited selection of the tools available: 

  • Run:AI: A proprietary platform used for automating machine learning. This platform provides the controls needed for automating resource management. It works well with graphics processing units (GPUs), and helps to optimize computing resources and supports developing deep learning models.
  • AutoKeras: An open-source autoML system that is based on Keras. Their stated goal is to help make machine learning accessible for everyone. This system supports using pre-built ML blocks (pieces of pre-built code that can be used to construct an ML model).
  • Google’s AutoML Vision: This service allows machine learning models to be trained to identify and classify images according to your defined parameters. Customized training of the AutoML Vision model requires a supply of labeled examples of the type of images (inputs) you will want classified, and the categories (responses/outputs) the ML system needs for making predictions.
  • DataRobot: A proprietary platform used for automating and optimizing ML model creation. This platformis designed to support model development from beginning to end with training and deployment. It offers a range of features, such as data formatting, model selection, feature engineering, hyperparameter tuning, and monitoring. It can also provide pretrained models, a user-friendly graphical user interface (GUI), and a data catalog.

Apps development without coding: No-code software development offers drag-and-drop tools, graphical user interfaces, and other user-friendly tools to help accelerate the development of ML and AI apps. Many of the no-code development platforms are designed for enterprise-sized businesses needing to develop business processes and workflow apps on a large scale. These tools provide templates for element libraries, workflows, and support interface customization without any coding. 

A few are listed below:

  • Quixy: This is a cloud-based user-friendly business application platform that allows staff members with no coding expertise to automate processes and workflows. It uses a simple drag-and-drop design.
  • Landbot: This software allows you to create a chatbot, providing a conversational experience for customers using a drag-and-drop tool without code. It also supports advanced data workflows, Dialogflow, and natural language processing.
  • Caspio: A no-code platform designed for developing online database applications. It is described as an all-in-one platform offering all tools needed to create apps for business operations and workflows. It comes with a visual application builder, integrated cloud database, scalable global infrastructure, and regulatory compliance. 

Pre-trained ML models: Developing and training ML algorithms is typically the data scientist’s responsibility. A number of ML software developers and startups have developed and launched pre-trained ML models. By purchasing pre-trained ML and AI models capable of data preparation, feature engineering, algorithm selection, and evaluation, the development and training of ML models is no longer required – with the exception of unique situations. (Pre-trained models are generally available for video, audio, image, or text analysis, opportunity workflow automation, sales, customer service, interactive advertising, and automated equipment inspections.) 

A few pre-trained ML model sources are listed below:

  • Model Zoo: This is probably the most popular repository of pre-trained ML models nowadays. Model Zoo has a nice, easy-to-use interface in which you can search the available models, filtering them by keywords, tasks, and frameworks. You can find several models for Tensorflow, PyTorch, Caffe, and others. Most of the models are published on Github, so you can also see their license and requirements there.
  • PyTorch Hub: PyTorch offers a selection of pre-trained models in their PyTorch Hub. Models can be searched by categories and keywords. A short description (as well as instructions) is presented with each model.

Self-service data analytics: Recently, tools have been developed that can be used to provide data-based insights to non-data scientists. These self-service analytics tools are offered by several business intelligence and analytics software providers. They often include features that augment data discovery and analytics. Features such as natural language query and search, and visual data discovery can help users easily find clusters, correlations, exceptions, links, and predictions without relying on analytics teams or data scientists.

  • Sisense: This is a user-friendly tool that allows its users to integrate data and discover insights with no coding or scripting, and comes with a front-end for visualization and dashboarding. 
  • Sigma: A no-code BI and analytics tool designed to be used with cloud data warehouses. It provides a user-friendly, spreadsheet-like interface (similar to Excel), and automatically translates data into a SQL format.
  • Qlik: This platform offers a broad range of analytics and business intelligence tools. The Qlik platform allows an organization to merge all their data sources, providing a single view. 

Educating the staff: Data skills are considered quite important, and it never hurts to provide staff with additional training, particularly regarding Data Science. Generally, however, the additional training doesn’t happen. This is primarily because everyone is too busy. Additional training will only take place if management supports it and includes it in the scheduling (or the business pays the employee to study during their off-duty time). 

The Challenges of Data Science Democratization

Occasionally some members of staff and/or management are resistant to change. It takes energy to learn new processes and develop new habits, and some people prefer to coast (or not stress) through their work life. (You can coast, and still do a good job, if you know what you’re doing. Having to learn new processes strips away that comfort level … for a while.) Any number of rationalizations can be used to argue against the changes. Ultimately, however, management and staff must change as the business changes. (Replacing people can be difficult right now, and there are no easy answers for that problem.)

Efforts to democratize Data Science does come with its own challenges.

Another potential problem is confusion during the implementation process. Without the proper onboarding and training, staff who have been given access to self-service and Data Science automation tools may misinterpret the data. In the early stages of moving to the democratization of the Data Science program, a go-to person (or two) should be available. This might be the data steward or chief data officer.

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