The document discusses the challenges of scalability, efficiency, consistency, and accuracy in data processing for SMEs, emphasizing the need for automation to address issues of human error and bias. It highlights the complexities of dataset management and the difficulties in finding qualified talent. The future vision presented includes actionable, real-time data driven by machine learning and AI advancements.

1. Act Fast

2. #1
Scalability
Data processing bandwidth needs to
grow without human effort
New data appear during weekends
and during holidays

3. Many SMEs do not have access to
necessary resources
Common secret that manual analysis
cannot go in depth
Human creativity and curiosity are
unparalleled if put in good use
#2
Efficiency

4. Human judgment can be inconsistent
or biased
Knowledge is often tied to individuals
within organizations
Manual data analysis is prone to
human error
#3
Consistency

5. #4
Accuracy
Dataset complexity leads to inaccurate
reports or slow responses
Aggregated reports are not enough for
informed decisions

6. Talent with domain knowledge and
experience with data is hard to find
Maintaining a healthy & high quality
automation is complicated
Technical teams and data quality
usually don’t work well together
#5
Tech
barrier

7. Baresquare
Overview
YOUR EXISTING
ANALYTICS DATA PROACTIVELY DELIVERED HOW YOU WORK BEST
AUTOMATED ANALYSIS
WITH ANOMALIES
Time Series
datasets

9. What does
the future
look like?
“90% of data will be
actionable in real-time
using ML”
“AI is going to be the
primary driver of moving to
a 4-day work week”
Source: Google Cloud Next ‘22