Building a data foundation for AI and Machine Learning

82% of enterprises are at
least considering AI adoption 1

Yet, there’s a roadblock looming many haven’t considered.

 

Their data
foundation may
be crumbling or
incomplete because
the data required
for AI and ML has
grown drastically
in terms of:

Volume of data

Variety of data
types

icon-03-03

Speed of creation

3 ways to make sure your AI and ML
implementation stays on solid ground

01

Make data from all
sources available

Despite data growth, many feel they lack usable data due to poor data management integration.

0%
say data availability
is a barrier to
implementing AI.1

0%
of AI developers
surveyed listed
data ingestion as
a problem.2

Read white paper  

Learn how a common SQL engine with virtualization helps you write queries once and run them anywhere.

Make data from all sources available
02

Act on streaming data for
quick insights

Streaming data is great for driving real-time insights,
but not all solutions can keep up.

0%

are currently using or
planning to use ML for
streamed data.3

0%

said that using intelligent
machines increased the
decision speeds.4

Read report  

Read the latest Forrester report about the ingestion,
analysis, and storage of streaming and fast data.

03

Offer the right tools to leverage data and offset the skills gap

Both tools and skills remain notable barriers
to successful AI implementation.

0%

of developers named quality of existing tools a barrier to adopting AI & ML.2

0%

of respondents see skills as the top barrier to AI success in 2018.1

Read report  

Read IDC’s analysis of an integrated analytics system that uses built-in data science tools to help businesses bridge the current skills gap.

Whether you’re looking to build or reinforce your AI and ML foundation, IBM Hybrid Data Management can help. Seamless integration across the Db2 Family is provided by the Common SQL Engine, whether you choose a cloud or on-premises deployment. The breadth of IBM solutions means you can leverage all data types, sources and structures. Learn more about how you can do more with your data.