Getting a handle on huge amounts of data is a challenge for IT departments. Without secure and efficient data management, forget artificial intelligence and machine learning.
Jan 01, 2019
Today, artificial intelligence (AI) and machine learning-powered systems have dramatically changed the way enterprises run their business. Gartner analysts predict that by 2020, AI technologies will be virtually pervasive in almost every new software product and service, and AI will be a top five investment priority for more than 30 percent of CIOs.
Companies such as Amazon, Uber, and Netflix have found ways to leverage AI and ML to create more profitable business models as well as improve customer experience. From self-driving cars to virtual assistants, like Alexa, enterprise companies are looking for innovative ways to leverage data and adopt advanced analytics to gain a competitive edge within their respective industries.
While AI and ML initiatives offer exciting possibilities, the unprecedented volume and complexity of managing data across sources create operational challenges for IT teams, preventing them from realizing the powerful insights to benefit the business.
One of the most valuable sources of data for AI and ML modeling is internal data on the organization’s products and customers. It’s the secret sauce to gaining a competitive edge for enterprises. This information about customer behavior, pricing, demographic trends, product demand, and technology adoption can easily be categorized and organized to create a foundation for data modeling and interpretation.
However, providing accurate machine learning models require that the data be accurate and meaningful. Organizations need an effective way to gather the right data in the right formats and systems in the right quantity.
In order to achieve that, enterprise IT teams need to overcome this challenge: the method in which data is accessed, aggregated, secured and delivered to a machine learning algorithm, data science initiative or business analysis framework.
Data Pipeline Framework
Without an automated and seamless data management platform that integrates with data masking technology, data science models underpinning AI and ML initiatives can be inaccurate at best, catastrophic at worst. That’s why a comprehensive approach to managing data is critical in providing both business and technical direction.
Here are the 5 attributes of an optimal data management strategy for AI and ML initiatives:
Automate data ingestion so various teams can spin-up, teardown and create unlimited data source copies with a single API call
Seamlessly input new data sources to test new sources and pipeline changes before data heads into AI/ML production environments
Anticipate and test changes before a broader rollout as AI and ML structures evolve
Virtualize data to deliver any amount of or slice of data on demand without incurring significant infrastructure overhead costs
Govern your data to ensure data controls can be embedded into key workflows and integrated with data security measures through masking technology
How good your data is will make the difference between success and failure for your AI and machine learning projects. The more accurate and plentiful your data is, the better your results. If your data is out of control, implementing AI and ML won’t magically fix your problems or help.