The document discusses the strategic importance of data science in enterprises, emphasizing long-term customer and brand care over short-term profit. It outlines various use cases such as predictive maintenance, churn prediction, and improving customer experience, while also addressing challenges like data limitations and model deployment. Key insights include the need for understanding market dynamics, organizational economics, and the complexity of building effective data science solutions.

1. Data Science Use Cases in
the Enterprise
Srinath Perera
Chief Architect, WSO2, Apache Member

2. Context: Understanding
Enterprise (ROI)
● It is about Money: long-term Money.
○ If you are looking to make a million once, sometimes,
you can get away with exploitation.
○ If you are looking to make a billion every year, you
have to care about customers, brand, employees as
well as the environment you are operating in
○ E.g., Indra Nooyi and her effort to move Pepsi to
healthy food.
● It is a Strategic environment where enterprises
compete.
○ “If you know the enemy and know yourself, you need not fear
the result of a hundred battles. ”
― Sun Tzu, The Art of War.

3. Context: Highly valued
Outcomes
● Efficiency, Savings
● Improving Customer Experience
● Finding new markets,
understanding markets
● Forecasts, Prediction
● Automation and Decision Support
I skate to where the puck is
going to be, not where it has
been. ---Wayne Gretzky

4. ● Examples
○ The effort by the US to use sensor and data analysis to stop
infiltration through Ho Chi Minh Trail in 70s
○ Even Nate Silver got Trump's victory wrong
● Reasons
○ History is not always representative of the future (e.g., Trump
Elections)
○ Complex systems ( highly interconnected systems where one
or few players can significantly change the outcomes)
○ Highly competitive situations such as stock Markets
■ Predictable at stable times, but not with shock
○ Average is affected dramatically by rare events (e,g, Covid)
■ Data can determine "average" outcomes with great
accuracy
○ Not enough data or data do not capture critical aspects
Nevermind the Press, Data Science does not always work

5. Use Cases @ Enterprise
● Efficiency, Self Awareness, and Forecasts
● Optimizing the sales funnel
● Predictive Maintenance
● Improving Customer Experience
● Product Use cases from a real-world iPaaS
● Finding new markets, understanding markets, Competitor
Analysis
● https://sparktoro.com/ - Instantly discover what your
audience reads, watches, listens to, and follows.
● Automate mundane tasks and let people focus on what
they are good at
● Automation and Task Assistant Systems
● Decision support systems
Often needs Explainability too

6. Efficiency: Optimize the Sales Funnel
● Each enterprise has a funnel
like this ( names may be
different)
● KPIs support decisions
● Examples:
○ conversion rates, dropoff - to find
bottlenecks
○ cost per conversion - find
activities that work well
○ Time spend on each stage
○ Forecasts
○ A/B testing optimizes

7. Efficiency: Predictive
Maintenance
● Often breakdowns have high costs
● We do preventive maintenance to
avoid that, but it leaves significant
money on the table
● Use telemetry data to predict
breakdowns
● We need to manage risk against
false negatives (e.g., cost to give
customer 100$)

8. Efficiency: Churn Prediction
● Even small churn compounds
significantly to reduce topline, and create
negative word of mouth.
● How is the user using the product?
● Has he given up?
● Are there complaints?
● Is there anything we can do if we know
before?
Need to think through the full story -
Ask “so what” until you see $$

9. User Experience: Understanding Choreo

10. Choreo Use Cases and Challenges
● Can collect data about everything, clicks,
messages, logs etc
● The focus is using AI to improve user
experience
● The system will have 10s of thousands of users
○ We can’t run a model per user
● Some use cases have limited data
● The specific user would not have enough data
initially, so we have a cold start problem
● Some use cases require personalization

11. User Experience: Forecasting Performance
● Performance feedback while
you write code
● API, service, database calls
dominate performance
● Use historical data about each
API, service, database call and
fit Machine Learning models
● Use queuing theory to model
the throughput and latency

12. Getting a Model to Production is Complicated
● Data Collection
● Model training
● Model deployment and
integrating the model into the
user experience
○ Acting on results
● Getting user feedback
● Evaluating and improving
models

13. User Experience: Automatic Data Mapping
● Programming with APIs
need us to map data
between two API calls (
and two systems)
● Automatic data
mapping suggest
mapping between two
data types
● It can maps data types
it has never seen

14. User Experience: Anomaly and Root Cause Prediction
● Detecting Performance anomalies in
the system
● The goal is to detect and performance
problems and notify the users and
supporting them in troubleshooting
● We started with several states of the art
papers and eventually beat them
○ 90% precision and 50% recall vs. 98% vs.
81% recall
● Working on attributing anomalies to
parts of the system and providing root
cause predictions
42

15. Understanding Markets: Sparktorro

16. Automation: Extracting information from Images/ Video
● Vidado.ai Using OCR to digitize Data RPA
does not work well with paper
● Icetana.com - decision support for video
surveillance
● www.dataminr.com detects high impact
events from public data
○ E.g., Brand risk, disease outbreaks, potential
new stories

17. Automation: Competitive Adjustments
● Common use cases
are adjusting the price
● This leads to curious
cases when bots are
on both sides
A good rule of thumb is to remember AI vs. AI does not work well.

18. Automation: Automate Mundane Tasks
● Works on top
salesforce
● Suggest next Action
● Provides templates
for actions
● Full context, connect
all information
● Benchmark
performance

19. Parting Thoughts
● If you plan to solve organizational problems
with data science, you need to understand
how it works and speak their language.
● Make sure there is enough data
● Think through the full lifecycle, including
economics (e.g., Choreo) and explain
● Model deployment, evaluation, integration to
customer, and evolution is complex
● Harder to build per user custom models,
better if you can create value against existing
data models and integrate as SaaS
Learn to see where
Data Science works,
but learn to see where
it does not also!!

20. Questions?