Why We Need Data Scientists AND Data Engineers in AI
Most of us recognize that data scientists and data engineers are not the same, but we're not 100% sure what they really do when it comes to artificial intelligence (AI). Part of the confusion comes from job titles. There are data analysts, data scientists, data engineers, data translators, data visualization specialists, and any other position that can have data in the title. Despite the numerous titles, individuals working with data are, at their core, mathematicians, scientists or engineers and they tend to be focused on data or infrastructure. Terms such as translator or visualization define a subset of the skills that scientists and engineers need.
Data engineers build the foundation for artificial intelligence. They create pipelines that bring information from different systems together and prepare the data for use by data scientists. Their goal is to construct a system that makes it easy for others to access the data. The process may sound straightforward, but it's not.
Most organizations' digital infrastructure evolved. Maybe it started with an accounting or inventory tracking system. Later, a customer relationship management (CRM) system was added along with an electronic scheduler for the factory floor. None of the systems interfaced with each other, and data was maintained in separate databases or file systems. For AI to work, the data from these disparate systems must be centralized in a consistent format.
Data engineers develop programs that normalize data into a single source for AI applications to access. For example, the date and time field in an accounting package may appear as MM/DD/YYYY and HH:MM:SS, but a scheduling package uses a DD/MM/YY and a 24-hour time clock. Data engineers are responsible for converting these values into a standardized format. They are also responsible for deciding what to do if the field is empty or the values are incorrect. Now, multiply that process by the number of data points in every digital system in an enterprise. Suddenly, the straightforward process is a lot more complex.
AI needs current information. Data engineers are tasked with developing a pipeline for new data to be fed into the data stream used by AI. The process may involve:
Finding ways to improve the quality of data at the point of origin
Modifying the format of the incoming data to be consistent
Deciding how and where data will be stored
Ensuring scalability throughout the pipeline
Without a reliable data pipeline, AI can not learn.
Data engineers are responsible for constructing the infrastructure that will house data. They will make decisions on how to process both structured and unstructured data. Data engineers evaluate databases, warehouses, and lakes to determine the best architecture for handling data. Their goal is to design and develop a self-sustaining architecture that meets the needs of an artificial intelligence implementation.
Data scientists have become technology unicorns -- a label for someone who doesn't exist. Today, companies expect data scientists to collect, analyze and interpret data to deliver valuable business insights or outcomes. They expect a data scientist to be part mathematician, scientist, statistician, computer professional, and effective communicator.
While data scientists may understand the concepts used by others in the field of data science, their focus is on creating machine learning models that use sound mathematical and statistical practices to deliver sound business outcomes. Data scientists represent a marriage of disciplines involving analytics, machine learning, mathematics, statistics, and programming. Their goal is to bring structure to data so patterns become visible and effective decisions can be data-driven.
Whether it's called problem-solving or critical-thinking skills, data scientists need a thorough understanding of the scientific method of discovery as well as traditional and evolving data analysis methods. Their skills go far beyond analyzing data. They may develop new algorithms, find innovative ways to explore data, validate business use cases, or design processes to provide insights into business problems. Data scientists must employ a range of tools to create algorithms that enable machines to replicate human intelligence.
Statistical analysis focuses on data collected over time. It represents what has happened. Machine learning (ML) techniques, however, do not report on the past; they are designed to predict outcomes based on the insights gained from data analysis in real-time. ML provides ways to process volumes of data using algorithms rather than traditional statistical methods.
For data scientists to be effective, they need to understand how to apply machine learning algorithms to data analysis. Knowing which ML tools to use for different applications can produce fast and accurate results. Without ML knowledge, data scientists can spend months working on solutions that could be solved quickly by modifying a generic ML algorithm.
Data scientists are frequently asked to develop computer models for business analysis. These models require an understanding of mathematical principles to ensure a valid model. If the model's design is faulty, the resulting AI will be flawed. Data scientists must understand their underlying assumptions to minimize bias. They must be able to explain their assumptions to non-technical executives, so they are confident that the AI implementation will meet their business objectives.No matter how technically sound an AI project is -- if it doesn't meet the business objectives -- it has failed.
Developing AI solutions is not a linear process. Data engineers and data scientists work together to create an environment where the infrastructure supports and scales to deliver the data necessary for successful AI implementations. Without data scientists, organizations would not realize the full potential of their digital assets. Without engineers, they would lack the infrastructure to support the growing demands for data.
F33.ai helps organizations leverage data science and machine learning to build more efficient and more effective AI implementations. Our innovative team drives the development of customer solutions that continuously expand the limits of what AI can do, making it possible to deliver more impactful business results. To learn more about how to make AI and ML work in your organization, contact us to set up a discussion.