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Challenges of implementing data science projects

IDSA Professional Certification in Data Sciences prepares students to expand their career prospects or change career paths by developing foundational data science skills. Candidates for the Professional Certification, a non-degree, part-time program, are required to complete a minimum of four required courses: Algorithms for Data Science, Probability and Statistics for Data Science, Machine Learning for Data Science, and Exploratory Data Analysis and Visualization.

Challenges of implementing data science projects

Despite the promise of data science and huge investments in data science teams, many companies are not realizing the full value of their data. In their race to hire talent and create data science programs, some companies have experienced inefficient team workflows, with different people using different tools and processes that don’t work well together. Without more disciplined, centralized management, executives might not see a full return on their investments.

This chaotic environment presents many challenges.

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Data scientists can’t work efficiently. Because access to data must be granted by an IT administrator, data scientists often have long waits for data and the resources they need to analyze it. Once they have access, the data science team might analyze the data using different—and possibly incompatible—tools. For example, a scientist might develop a model using the R language, but the application it will be used in is written in a different language. Which is why it can take weeks—or even months—to deploy the models into useful applications.

Application developers can’t access usable machine learning. Sometimes the machine learning models that developers receive are not ready to be deployed in applications. And because access points can be inflexible, models can’t be deployed in all scenarios and scalability is left to the application developer.

IT administrators spend too much time on support. Because of the proliferation of open source tools, IT can have an ever-growing list of tools to support. A data scientist in marketing, for example, might be using different tools than a data scientist in finance. Teams might also have different workflows, which means that IT must continually rebuild and update environments.

Business managers are too removed from data science. Data science workflows are not always integrated into business decision-making processes and systems, making it difficult for business managers to collaborate knowledgeably with data scientists. Without better integration, business managers find it difficult to understand why it takes so long to go from prototype to production—and they are less likely to back the investment in projects they perceive as too slow.

The data science platform delivers new capabilities

Many companies realized that without an integrated platform, data science work was inefficient, unsecure, and difficult to scale. This realization led to the development of data science platforms. These platforms are software hubs around which all data science work takes place. A good platform alleviates many of the challenges of implementing data science, and helps businesses turn their data into insights faster and more efficiently.

With a centralized, machine learning platform, data scientists can work in a collaborative environment using their favorite open source tools, with all their work synced by a version control system.

The benefits of a data science platform

A data science platform reduces redundancy and drives innovation by enabling teams to share code, results, and reports. It removes bottlenecks in the flow of work by simplifying management and incorporating best practices.

In general, the best data science platforms aim to:

Make data scientists more productive by helping them accelerate and deliver models faster, and with less error

Make it easier for data scientists to work with large volumes and varieties of data

Deliver trusted, enterprise-grade artificial intelligence that’s bias-free, auditable, and reproducible

Data science platforms are built for collaboration by a range of users including expert data scientists, citizen data scientists, data engineers, and machine learning engineers or specialists. For example, a data science platform might allow data scientists to deploy models as APIs, making it easy to integrate them into different applications. Data scientists can access tools, data, and infrastructure without having to wait for IT.

The demand for data science platforms has exploded in the market. In fact, the platform market is expected to grow at a compounded annual rate of more than 39 percent over the next few years and is projected to reach US$385 billion by 2025.

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