Artificial Intelligence requires significant data, building and deploying AI and machine learning systems requires significant data sets. Creating key machine learning algorithms is dependent on large volumes of data. To expand and deepen the results and findings made by the algorithm, machine learning requires data from a range of sources, in various formats and from a variety of business processes.
At the same time, AI itself can be vital in determining and preparing the data required to drive the further value of AI and analytical systems. Businesses require more data scientists and specialised analysts to integrate the necessary AI and machine learning algorithms.
A new era of enterprise analytics is developing and it involves a combination of automation and contextual information. AI-focused analytical systems can develop vital insights and information that can be passed onto decision-makers without requiring specialist analysts to prepare the data. Business intelligence analysts and other data professionals will still play an important role, but many will not be needed to provide added support to other team members and data users.
Smaller businesses that don’t necessarily have the budget for data scientists will be able to measure their data with better accuracy and clearer insights.
The potential to efficiently automate data tasks is dependant on the industry and overall circumstances. Often, there is a need for adequately trained human support for AI and machine learning plans, especially if the output is critical to the business.
While automated AI data science tools can be simple and effective, they may leave businesses with unanswered questions. If you don’t have a background in data science or machine learning, you may not be capable of determining the results or implementing the suggested changes, which can be challenging and time-consuming.
There is the potential to automate certain parts of a data scientist role, but the skills of a data engineer will continue to be a vital asset to an organisation. Data engineering is required to produce smart and intelligent information that can enhance predictive accuracy and support detailed business analysis.
There may be ways to automate various pieces of data science roles, but the skills category that will still be essential is that of a data engineer. There are many tasks required to source, manage and store data in which data scientists don’t necessarily want to get involved. “To succeed with AI, companies should have an automation environment with reliable historian data,” a McKinsey report explains.
Then, companies “will need to adapt their big data into a form that is amenable to AI, often with far fewer variables and with intelligent, first principles-based feature engineering,” the study’s authors, led by Jay Agarwal, state. Data engineering is needed to produce “smart data” to improve predictive accuracy and aid in root-cause analysis. This, along with equipping staff with the right skills, can provide services that can help increase revenues up to 15 per cent, they relate.
Data engineering is vital. A data scientist can’t discover or utilise information until there is a good set of data to work with. Data scientists and specialised data analysts will continue to be in demand and will remain important in supporting businesses to design and test algorithms and data that can determine trends, automate processes and engage with customers. The challenge, however, is the volume of data flowing into businesses and the rising demands for new algorithms and capabilities with data. AI is unravelling a new path to a more effective and accessible AI.
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