The document outlines key technology trends for 2020 identified by SAS and Accenture, emphasizing analytics' role in enhancing customer experiences, AI integration, and the governance of smart devices. It highlights the need for real-time analytics to provide continuity in customer experiences, improve human-AI collaboration, and address governance issues related to data privacy and usage. Businesses are encouraged to adopt advanced analytics tools and governance practices to navigate the complexities of modern technology effectively.

1. THE
ANALYTICS
ANGLE
WITHSAS
ACCENTURE TECHNOLOGY VISION 2020

2. #TechVision2020
These Tech Vision trends
are coming—for many,
they’re already here.
How will you put them
to work at scale?
The I in Experience AI and Me
The Dilemma of Smart Things Innovation DNA
#TechVision2020

3. Analytics has a
major role to play
in achieving scale.
Here are insights from SAS and
Accenture on the analytics angle
for each trend.
Athina Kanioura
Chief Analytics Officer and Global Lead
Applied Intelligence, Accenture
Dave Macdonald
Executive Vice President
and Chief Sales Officer
SAS
Executive sponsors
#TechVision2020

4. #TechVision2020
The I in Experience
Customers will expect
“cooperative experiences”
where companies are partners
in experience creation, not
just the providers and owners
of experiences.
The trend:
#TechVision2020

5. #TechVision2020
The I in Experience
You’re going to need a
forensic-level understanding
of customers.
• Which devices are they using—and when
are they using them?
• How have their preferences changed
since last month?
• How has the context shaping their
preferences changed?
Analytics implications:

6. #TechVision2020
Insights needed to deliver
continuity of experience
Today there are countless ways for your customers
to get from point A to point B. You need to be able
to ensure a high level of continuity from one leg
of the customer journey to the next.
The I in Experience
Analytics implications:
Key analytics tools:
• Offer optimization/
recommendation engines.
• A/B testing.
• Multivariate testing.

7. Analytics implications:
Citation for stats: https://www.sas.com/en/whitepapers/customer-experience-2030-111008.html
The I in Experience
agree that brands can
be trusted to keep their
data private.
are concerned with
how brands use their
personal data.
54% 73%
feel they should be
able to see what data
a brand has captured
about them and be
able to change,
update, or even delete
that data whenever
they want.
believe that companies
and brands should not
be allowed to share
their data with other
companies or brands.
feel they have no
control over the
level of privacy they
need for themselves,
their family, or
their children.
78% 66% 61%
To earn trust,
deliver data
transparency
You need consumers to offer
up their data. To do it, they
need to know it’ll be used in
the right ways.
#TechVision2020

8. #TechVision2020
AI and Me
To tap into the true potential
of AI in the enterprise, leaders
will enable tighter human-AI
collaboration to transform
what businesses actually do,
not just how they do it.
The trend:
#TechVision2020

9. #TechVision2020
AI and Me
Analytics implications:
Real-time oversight of
AI systems is required
• As humans rely more heavily on AI systems
to make decisions and execute tasks, the
need for real-time oversight increases.
• Analytics is the only way to deliver faster,
more accurate oversight at scale.
#TechVision2020

10. #TechVision2020
AI supercharges
analytics insights
• When paired with AI, analytics capabilities
can present human decision makers with
more dynamic choices rooted in live data.
• AI can automatically deliver a steady
stream of insights and recommendations,
helping people take action faster, using
more insight.
Analytics implications:
AI and Me
#TechVision2020

11. #TechVision2020
Deliver user-specific context
for AI–based decisioning
• How can AI systems distinguish between users
to give them decisioning capabilities that match
their roles?
• Analytics can generate individually appropriate,
role-based decision options
• With more human input, these systems can
rapidly refine these capabilities.
Analytics implications:
AI and Me

12. #TechVision2020
The Dilemma of Smart Things
As smart things are continually
updated, they introduce the
“beta burden” —the unintended
consequences when products
and their contained experience
are constantly in flux.
The trend:
#TechVision2020

13. #TechVision2020
The Dilemma of Smart Things
New focus on
analytics governance
• In the past, governance for most
products ended at the point of purchase.
• With smart things, the sale opens the
door to a host of new governance issues.
• Data presents the most significant
governance decisions
Analytics implications:
Data governance questions
for smart things:
• What can be done legally and ethically
with “smart things” data? Who can do it?
• How is collected data being used?
• How long is the data kept?
• Which tools are used to analyze data,
and how is use governed?

14. #TechVision2020
Open development
demands more coordination,
over longer timespan
• Smart things require a wider range of partners
and providers working in alignment.
• Data connects them all.
• In this context, analytics platforms—not just
discrete tools—are crucial for managing the
complete development lifecycle.
Analytics implications:
The Dilemma of Smart Things
#TechVision2020

15. All analytics tools used
to enable smart things
must be cloud-ready
Analytics implications:
• The impact of cloud computing on
critical decisions about architecture
is often overlooked.
• Be ready to move to newer, cheaper,
faster compute environments at a
moment’s notice to remain competitive
and deliver more to customers.
The Dilemma of Smart Things
#TechVision2020

16. #TechVision2020
Building blocks of innovation:
• Maturing digital technology that is more commoditized and accessible.
• Scientific advancements that are discrete yet deeply disruptive.
• Emerging DARQ (distributed ledgers, artificial intelligence, extended
reality, and quantum computing) technologies that are poised to
rapidly scale and bring organizations into the post-digital era.
Just as human DNA determines
individual traits, the building
blocks of innovation DNA will
define enterprises in the future.
Innovation DNA
The trend:

17. #TechVision2020
Innovation DNA
To execute, combine
business rules and
analytics models
• Innovation decision making requires all
relevant business processes, data sets,
rules and analytical models working
together in a single decision engine.
• Record and track all information about
a decision—not just for compliance
reasons, but for insight into what’s
working and what isn’t.
Analytics implications:
#TechVision2020

18. www.accenture.com/techvisionsas
Thankyou
#TECHVISION2020
#TechVision2020