Why Treat Data as a Product? Unraveling Its Worth (1)

1

• Why Treat Data as a Product?
• Unraveling Its Worth

2
Lets have a reality check. Everyone is saying
that data is the new oil. Agreed. But unlike oil,
data is abundant and has the potential to be
infinitely reusable. However, simply having a
wealth of data isnt enough its true value
comes from how its utilized. Many businesses
collect vast amounts of data but lack a coherent
strategy for its use. This data often ends up in
silos, inaccessible, and most of the time
unused. Thats the state of data in many
organizations today. But by treating data as a
product, you can capitalize on real value for
your business.
3
Data as a Product refers to a way of thinking
about how you manage and use data within your
product, and within your organization.Just like
any product, data needs to be designed, built,
maintained, and constantly improved.DaaP
focuses on ensuring the data is high quality,
readily available, and meets the needs of its
users.The core idea is to make data usable for
specific purposes. This involves understanding
who will be using the data (both internally and
potentially externally) and ensuring its
presented in a way thats clear and actionable
for them.Imagine taking raw oil. By itself,
its not particularly useful. But by refining it
into gasoline, you create a valuable product that
powers many things.

• What is Data as a Product (DaaP)?

4
In the same way, DaaP focuses on refining data
into something insightful and beneficial.A data
product includes several parts that work together
as a whole, usually stored in one Git
repository.
5
Think about the most successful products. They
solve problems, fill needs, and offer something
unique to their users.Data can do the
same!Heres why this approach is a
game-changerUncover Right Hidden
InsightsData can reveal patterns and trends
invisible to the naked eye.For example, you can
discover why certain marketing campaigns resonate
with customers, identify areas for operational
improvement, or even predict future market
fluctuations.More Better Decisions,
EffortlesslyWith a clear understanding of your
customers, operations, and market, you can make
data-driven decisions that are more likely to
succeed.Imagine trying to navigate a dark forest
without a map data is your roadmap to success,
guiding you toward the most optimal choices.
Data as a Product Why Do Product Companies Need
It?
6
Boosts Innovation, in Every AreaData can spark
new ideas and fuel creative problem-solving.It
can help you identify opportunities you never
knew existed, perhaps revealing a hidden niche
market or suggesting a new product feature that
perfectly meets customer needs.Data can be the
spark that ignites your next big
innovation.Creates Competitive Advantage, in
RealEveryone is using data. But what is a
powerful differentiator is the ability to
extract insights and act on them.Companies that
effectively leverage their data can gain a
significant edge over their competitors, making
them more responsive to market changes, more
efficient in their operations, and more
attractive to customers.
7
The applications of data as a product span across
various industries, each with unique challenges
and opportunities. Here is how top companies are
gaining the most out of it1. Walmart Leverages
DaaP to Analyze Customer PurchasesBackgroundObj
ective To gain deeper insights into customer
purchasing behaviors and optimize inventory
management.Data Sources Point of sale (POS)
data, online purchase data, customer feedback,
and social media interactions.ImplementationDat
a Integration Combining in-store and online data
for a holistic view of customer
behavior.Predictive Analytics Forecasting
demand and trends using historical purchase
data.Personalization Tailoring marketing
campaigns and promotions based on customer
preferences.OutcomesReduced stockouts and
overstock situations.More effective promotions
lead to higher sales.Enhanced shopping
experience through personalized
DaaP at Work in the Real World
8
2. NetFlix Employs DaaP to Deliver a Personalized
Viewing ExperienceBackgroundObjective To
enhance user engagement and retention by offering
a highly personalized viewing experience.Data
Sources Viewing history, user ratings, search
queries, and demographic data.ImplementationRe
commendation Engine Utilizing AI and ML to
recommend content based on user preferences and
behavior.Content Insights Analyzing viewing
patterns to guide content creation and
acquisition.User Segmentation Categorizing
users to tailor recommendations more
effectively.OutcomesHigher viewer retention
and time spent on the platform.More informed
decisions on content investments.Enhanced user
experience through accurate and relevant
recommendations.
9
2. NetFlix Employs DaaP to Deliver a Personalized
Viewing ExperienceBackgroundObjective To
enhance user engagement and retention by offering
a highly personalized viewing experience.Data
Sources Viewing history, user ratings, search
queries, and demographic data.ImplementationRe
commendation Engine Utilizing AI and ML to
recommend content based on user preferences and
behavior.Content Insights Analyzing viewing
patterns to guide content creation and
acquisition.User Segmentation Categorizing
users to tailor recommendations more
effectively.OutcomesHigher viewer retention
and time spent on the platform.More informed
decisions on content investments.Enhanced user
experience through accurate and relevant
recommendations.
10
3. JPMorgan Chase Applies DaaP to Combat
Financial FraudBackgroundObjective To
enhance fraud detection capabilities and minimize
financial losses due to fraudulent
activities.Data Sources Transaction data,
customer behavior data, and external threat
intelligence.ImplementationReal-time Data
Processing Using DaaP to monitor transactions in
real-time for anomalies.Advanced Analytics AI
and ML algorithms to detect unusual patterns and
flag potential fraud.Collaboration Sharing
insights with other financial institutions to
stay ahead of emerging fraud techniques.Outcomes
Significant reduction in fraudulent
transactions.Automated fraud detection processes
reduce manual intervention.Increased trust due
to enhanced security measures.
11
1. Identify the StakeholdersEvery product has
users, and data products are no different. The
first step is to identify who will use the data
and how.Stakeholders could be internal teams,
like marketing and sales, or external customers
who might benefit from insights derived from the
data.2. Understand the Use CasesOnce
stakeholders are identified, its crucial to
understand their needs and use cases. What
problems are they trying to solve? What
insights do they need?This helps in designing
data products that are relevant and valuable.For
instance, a marketing team might need detailed
customer behavior analytics, while the product
team might require usage patterns to improve
features.
A Framework to Transform Data into a Valuable
Product
12
3. Ensure Data Quality A product is only as
good as its quality, and data is no
exception.Ensuring data quality involves
cleaning up inaccuracies, removing duplicates,
and filling in missing values.This step is akin
to refining crude oil into a usable product.
High-quality data leads to reliable insights and
better decision-making.4. Develop a Delivery
MechanisumHow will the data be delivered to its
users?This could be through dashboards, reports,
APIs, or even direct access to datasets.Remember
The delivery mechanism should be user-friendly
and tailored to the needs of the
stakeholders.Because this step is about making
the data accessible and actionable.5. Iterate
and ImproveLike any product, data products need
continuous improvement. Gather feedback from
users, monitor usage, and iterate.This ensures
that the data remains relevant and continues to
provide value.Think of it as an app that gets
regular updates to enhance its functionality and
user experience.
13
Once you have a solid foundation, you can explore
ways to make your data product even more
valuable.For that,Data MonetizationConsider
offering anonymized or aggregated data sets to
external businesses for market research or trend
analysis.This can be a great way to generate
additional revenue.ExampleA retail company
could sell anonymized customer purchase data to
market research firms, helping them understand
consumer behavior and product trends.Predictive
AnalyticsMove beyond basic reporting and use
your data to predict future trends and customer
behavior.This allows you to be proactive and
make strategic decisions based on whats coming,
not just whats already happened.ExampleA
logistics company could use data on weather
patterns, traffic congestion, and customer
locations to predict potential delays and
optimize delivery routes.
Taking Data as a Product to the Next Level (With
Examples)
14
Once you have a solid foundation, you can explore
ways to make your data product even more
valuable.For that,Data MonetizationConsider
offering anonymized or aggregated data sets to
external businesses for market research or trend
analysis.This can be a great way to generate
additional revenue.ExampleA retail company
could sell anonymized customer purchase data to
market research firms, helping them understand
consumer behavior and product trends.Predictive
AnalyticsMove beyond basic reporting and use
your data to predict future trends and customer
behavior.This allows you to be proactive and
make strategic decisions based on whats coming,
not just whats already happened.ExampleA
logistics company could use data on weather
patterns, traffic congestion, and customer
locations to predict potential delays and
optimize delivery routes.
Taking Data as a Product to the Next Level (With
Examples)
15
Data Sharing and CollaborationPartner with other
organizations to combine datasets and dig even
richer insights. After all, two minds (and two
datasets) are often better than
one!ExampleImagine a healthcare provider
collaborating with a pharmaceutical company to
analyze patient data and identify new drug
targets or treatment options.Data sharing can
lead to groundbreaking discoveries and
innovations that benefit the entire industry.
16
Data debt refers to shortcomings in data quality,
architecture, or processes that hinder the
effective use and future development of your
product.This occurs due to many reasons,
including - Data quality issues- Poor data
architecture- Inefficient data processes-
Inconsistent data models- Manual processes
How to Deal with Tech Debt for Data as a Product?
17
1. Implement policies, standards, and processes
to manage data quality and compliance.2. Use
iterative development cycles to address technical
debt gradually.3. Develop a plan to refactor
legacy code, improve data models, and enhance
automation without disrupting operations.4.
Monitor and report on data accuracy,
completeness, consistency, and timeliness.5.
Regularly clean and validate data to ensure it
meets quality standards.6. Automate ETL/ELT
processes to reduce manual intervention.7.
Adopt scalable and flexible architectures (e.g.,
microservices, data lakes) to handle growing data
needs.8. Anticipate potential technical debt
and address it early during the development
cycle.
Strategies to Avoid Technical Debt
18
Bringing the most (and the best) out of your data
isnt about magic algorithms or overnight
success.Its about a commitment to understanding
your data, treating it strategically, and using
it to make smarter decisions.This requires
expertise in data management, design, and
engineering. Thats where we came to help.Were
a software product development company.Our data
engineering team brings together a blend of
technical proficiency and user-centric design
thinking.We partner with you to? Develop
data as a product strategy? Design a
user-friendly data experience? Engineer robust
data solutionsSo, dont let your data remain an
untapped resource.Lets connect and turn your
data into a game-changing asset, together!
Turn Your Data into Gold Mine with Azilen
19
Original Source Data as a Product What is the
Right Roadmap? (azilen.com)Original Published
at www.azilen.com
20
Thank You