In the rapidly evolving business landscape, agility and responsiveness are key. This article delves into the importance of harnessing and acting on the vast data available to businesses. It highlights the need for companies to become proactive 'thermostats' rather than reactive 'thermometers' in their environment, emphasizing the role of strategic automation in achieving this goal. The presentation explores five critical barriers to data-driven maturity: scalability, efficiency, consistency, accuracy, and the tech stack. Each section provides insights into overcoming these challenges through smart automation and data handling strategies. For example, it discusses how scalability can be achieved by automating data processing, and how efficiency in data analysis leads to more strategic and creative business practices. It also touches upon the future of business and technology, predicting that 90% of data will become actionable in real-time through machine learning, and AI will play a significant role in transitioning to a 4-day work week. This shift promises greater efficiency and a better balance between professional and personal lives. Finally, it introduces Baresquare as a comprehensive solution to these challenges. As an enterprise-level SaaS product, Baresquare streamlines data handling and decision-making processes, offering real-time actionable insights through its AI-powered analytics platform. It positions itself as a tool that not only meets current business needs but also paves the way to a smarter, AI-driven future in business operations. More details available in this blog post: https://tzamtzis.gr/2023/business-life/act-fast-revolutionizing-data-driven-decisions/

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