Businesses should watch for several data integration trends in 2021 that can help them gain a competitive advantage. These include embracing automation to eliminate manual tasks, leveraging more data types like spatial and real-time data, evolving infrastructure to the cloud, improving customer experience with AI, planning for effective metadata management, and being prepared for changes in processor technology. To get the most value from data, organizations need data integration solutions that can adapt to these evolving trends.

1. Data Integration Trends
Businesses Should Watch for in 2021

2. 2020 is behind us.
Looking forward to 2021 & beyond!
The importance of data continues to grow.
Up-to-date and available data is critical for making the best decisions.

3. .
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4. We Know and Love Data. All Data.
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5. Today’s Webinar:
We plan to share trends we have observed with our
data integration customers, the broader data market
& industry leaders.

6. Businesses that get the best value from data
have a competitive advantage.
To get a competitive edge, organizations need to:
1. Embrace Automation
2. Leverage More Types of Data
3. Understand the Evolution to the Cloud
4. Improve Customer Experience with AI
5. Plan for Metadata Management
6. Bonus: Cutting across them all

7. Data Trends to
Watch for in 2021

8. 1. Embrace Automation

9. Automation
Individual Level
Eliminate lightweight manual tasks such as data validation, data entry, report building,
data sharing, simple email communication, notifications
Department Level
The shift from monolithic applications to razor focused applications continues. This
results in the need to synchronize information across applications and departments
Organizational Level
Ensure heterogeneous IT environments are connected with Enterprise Integration.

10. Poll Question
Where do you need automation the most?

11. Automation
Getting Started:
1. Identify manual processes in your organization
which require multiple handoffs or repetitive data
entry tasks.
2. Rank each process based on Positive Impact,
Learning Curve and Effort to automate.
3. Start with small high impact projects, and iterate,
building upon each success
Goal: Eliminate repetitive manual tasks along with
front-ending the process with a self-service portal
Resource: Spatial Data Automation Webinar

12. 2. Leverage More Data Types

13. Power of Multiple Data Types
However more value can be achieved from
data assets when they are combined in
different ways.
It is by combining and mixing data types that
organizations gain new insights.
Each type of data has value and is collected to answer specific questions or measure
a certain conditions.

14. Spatial Data
The importance of spatial data for decision making continues to grow.
In 2020, cloud vendors Google, Snowflake and Amazon added and expanded support.

15. Spatial Data
Just recently, new entrants Unfolded and Cockroach Labs have unveiled cloud based
spatial offerings
The demand for spatial data is increasing. A plan to leverage spatial data is key.

16. Real Time Data
Due to the proliferation of sensors and 5G, organizations need to analyze and manage
more real-time data than ever before
Examples:
● Web Data (ex. website visits, social media, applications)
● IoT Data (ex. sensors, meters, devices)
● Real-Time Events (ex. Fire alarm, heat sensor)
● Continuous streams (ex. moving assets, app log streams)
Organizations must work with all real-time data types to make fast, accurate decisions

17. Augmented Reality (AR)
AR will change the world for business and beyond:
● Traditional Use Cases: Underground utility
assets, street-level asset identification
● Leading-Edge Use Cases: Customer
satisfaction, real estate, community
engagement, social media, etc.
Blog: 5 Ways to View Your Data in AR
Article: Apple Unveils LiDAR Scanner

18. 3. Evolution to the Cloud

19. Evolution to the Cloud
Factors driving organizations to the cloud:
● Remote workers - COVID19 has accelerated the move to remote work
● Scalability & elasticity - Trend towards pay for what you use rather than capacity.
● Serverless services - From DB’s to other services pay for use and level of service.
● Serverless compute - leverage open source container technology (i.e. Kubernetes)
● Enhanced security - Build on the secure platform of leading vendors
Resource: Understanding
Cloud Agnostic Strategies
Cloud technology is evolving fast and you must be agile.
Select data management tools that are cloud agnostic.

20. 4. Improving Customer
Experience with AI

21. Improving the
Customer Experience
Customers want better control over their
data, and better control over their
processes.
With tools like machine learning, customer
experiences can be effortlessly
personalized. Web apps and browser
options are also becoming more popular as
they allow easy, on-the-go access.

22. Importance of Data Engineering Article: We Don’t Need Data
Scientists, We Need Data
Engineers by: Mihail Eric
Today, the bottleneck in helping companies get machine learning
and modelling insights to production centers on data problems.
How do you annotate data? How do you process and clean data?
How do you move it from A to B? How do you do this every day as
quickly as possible?

23. 5. Planning for Metadata
Management

24. Metadata Management
As organizations collect more data, data governance and management becomes an
increasing concern for CIOs.
Metadata Answers:
● What is this data?
● Where did this data come from?
● Where does this data reside?
● How has it been modified?
● How will my changes impact other systems/processes
Article: Gartner’s 10 Changes Coming to Data Analytics by: CIO Dive

25. 6. Bonus Trend

26. ARM Processor:
Changes the Game
Game changing with unmatched performance
per watt becoming important!
Desktop: Cooler, faster, smaller, longer battery,
no fan
Data Center: Cooler, faster, smaller, lower cost
Processor technology also changes over time.
Look to agile data management tools from vendors that embrace change.

27. Businesses that get maximum
data value through
data integration make
better decisions and
win the marketplace.

28. Thank you!
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