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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 dont work well together. Without more disciplined, centralized management, executives might not see a full return on their investments.

This chaotic environment presents many challenges.

Data scientists cant 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 differentand possibly incompatibletools. 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 weeksor even monthsto deploy the models into useful applications.

Application developers cant 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 cant 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 productionand they are less likely to back the investment in projects they perceive as too slow.

Learn about the data science lifecycle (PDF)

Original post:

What is Data Science? | Oracle