Click to learn more about co-author Adam Carrigan.
Click to learn more about co-author Jorge Torres.
Traditionally, machine learning tools were only available to enterprises with the necessary budget and expertise. Now, AI is empowering machine learning to be democratized to reach more users, allowing them to make the business intelligence-driven decisions that could transform how they operate in the year ahead. Jorge Torres and Adam Carrigan discuss the challenges SMB data scientists face, how AI is empowering the democratization of machine learning, and the impact this could have on any business that has structured data.
How Are Organizations Benefiting
from Data-Driven Business Intelligence?
Jorge: Most companies already have a significant amount of
data generated from business applications that isn’t being used for decision-making.
Making this data more accessible allows organizations to make business
intelligence drive decisions that could transform how SMBs operate in the year
Adam: We recently did a survey of healthcare insurance organizations and found that half of the respondents have ongoing predictive analytics and ML projects. Healthcare organizations are already using business intelligence to improve predictions of patient treatment outcomes. Another example: Cybersecurity professionals are already using machine learning to protect IT infrastructures by sorting through potential threats to provide more actionable security alerts.
Expect business intelligence to have an even greater
impact in the next year. Enterprises, healthcare, philanthropic organizations,
and any business using a database will all need to make better use of their
data to predict changes and craft more informed business strategies.
What Is Holding SMBs Back from Making
Use of Machine Learning Tools?
Adam: Data scientists at SMBs face a lot of challenges when
it comes to access to resources and expertise. It has traditionally been very
difficult to providedata scientists
with machine learning resources if you aren’t a large corporation.
Current machine learning tools are time-consuming to run
and require advanced machine learning knowledge, putting all the pressure on
the data scientist. In fact, in the healthcare study we ran found when it comes
to producing results of predictive analytics and ML projects, 50 percent said
data scientists are responsible, and 27.5 percent say data analysts hold
Jorge: Another barrier is data privacy. Many SMBs can’t move
their data in the cloud because of security issues. However, storing data
on-premises leads to a lack of cloud-based machine learning tools.
How Is AI Empowering the Democratization
of Machine Learning?
Jorge: Through the use of AI, SMBs can augment the knowledge of a domain expert and put ML in the hands of the people that touch the data. The recent development allowing this democratization of machine learning are AI-Tables.
AI-Tables differ from normal database tables in that they
can generate predictions upon being queried and returning such predictions as
if it was data that existed on the table. Simply put, an AI-Table allows you to
use machine learning models as if they were normal database tables. By using
data directly at the source, AI allows you to increase prediction accuracy.
Adam: The ability to execute ML models with a simple SQL
query lowers the ML expertise required to run these models. This allows regular
database users to make use of ML and makes data more accessible without putting
all the pressure on the data scientists. Bringing ML tools to the database also
means SMBs can maintain full control of their data while still gaining the
benefits of machine learning.
What Is the Role of Explainable AI When
Democratizing Machine Learning?
Jorge: The whole goal of democratizing machine learning is to
make data more accessible and actionable to help drive business decisions. This
means management and decision-makers need to be able to trust this data. Black-box
models are always difficult to trust and don’t provide all of the information
needed to make informed decisions.
Knowing when not to trust an outcome is equally as
important as knowing when to trust it. An ML generated prediction should also
clearly answer why decision-makers can trust the prediction and the model used.
Adam: In addition to offering more reliable ML predictions,
explainable AI also lets database users know how to make predictions more
reliable. When talking about democratizing machine learning, the real benefits
will be seen when as many users as possible are using ML predictions, improving
on those models, and using AI and predictive analytics in new and innovative
ways that will push their industry forward.
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