Every Product Will Be a Data Product

Author
Joanna He
Senior Director, Product Management
Dec. 01, 2021

Why build a data product?

As Software has eaten the world, every software company is becoming a SaaS Company, and every SaaS company has a massive volume of data. As organizations transition to data-driven decision-making, they require the adopted SaaS products to provide in-product analysis abilities or data API to support their thriving needs on data.

 
software-is-eating-the-world_kyligence
 

The benefits of such data products for the SaaS company are

  • The data economy: Maintaining data assets is a cost, but turning them into products generates business value (and revenue)
  • Agile and flexible business: Product can scale with business expansion but not the data, as data itself cannot be scalable without a companion architecture
  • Democratize your data: Everyone can interact with a product, but not necessarily a dataset!
 

What is a data product?

You might have the same question in mind as me: What exactly is a data product?

As defined by DJ Patil, the U.S. Chief Data Scientist, a data product is " a product that facilitates an end goal through the use of data."

From my perspective, a data product can sometimes be the synonym of data application; for the general consumer, as we all experience in our daily life, it can be either a digital banking statement or a Covid-19 cases tracker like this one.

 
Covid-19-cases-tracker

Screenshot is taken from Bing
 

Through a data application or a data product, the end-users can consume the data within the proper context and easily make sense of the data.

 

Different types of data product

Conceptually, depending on the user persona and the use cases, there are 4 different types of data products:

 
 four-use-cases-of-data-product
 
Different-Types-of-data-product
 

There are more examples to demonstrate different types of data products.

  1. Use case 1: data product will be the Public API that the company exposed to internal or external data engineers/ developers who can consume or develop to another layer of a data product. In this case, the data product producer will be an application developer who will build the API for the consumer.
  1. Use case 2: data product is mainly used for internal decision-making support purchases.

In this use case, the internal data team is the data product producer. Data engineers and analysts prepare the data pipeline and data model for business users to perform self-service analysis. And power analysts will design the canned dashboard for business owners and business stakeholders to view on a given frequency set.

  1. Use case 3: data products can be prepared by the citizen developers (line of business users) for automating or streamlining internal repetitive business processes. Citizen developers use low-code/no-code applications such as Microsoft Power Apps to build their internal business applications.
  1. Use case 4: data products that are part of the external applications/ SaaS services that the company provides to their end customers. In this use case, the application developer will be the data product producer who calls the data API as part of the application backend and build the Web / Mobile front-end UI for the end-users to consume.
 
data-product-producer-and-consumer.
 

Some real customer examples:

Next, I will share some of the typical customer stories of different types of data product use cases.

AppZen builds their Mastermind Analytics product in a few weeks

AppZen overhauls the way finance teams work, automating spend approvals and providing insights that help finance auditor reduce spend, comply with policy, and streamline process. What AppZen achieved:

  • AppZen built a data product with Kyligence during the Covid-19 pandemic
  • Hundreds of customers onboarded in 12 months
  • 90% Increase in auditing process efficiency
  • A scalable and cost-effective architecture
  • AWS S3 + Kyligence + Apache Superset
 
 

How Kyligence helps AppZen build its data product

Kyligence provides:

  1. A high-performant, high-concurrent data service natively on AWS.
  2. A rich set of APIs (ODBC, JDBC, Rest API, Python Client) to have a wide range of options for integrating and building the data product front-end.
  3. Flexible data service that can support queries on both the summarized high risk reports and expense line details.
 
Kyligence-helps-AppZen-to-build-its-data-product

Combining Kyligence and Apache Superset, AppZen can provide rich self-service audit analytic dashboards and reports to the expense auditor. As a result, the auditor can now have visibility of expenses at all levels with summarized and detailed data consistency.

 

Strikingly provides their 100,000 website builders built-in Analytics service with ease

 
Strikingly-website
 

Strikingly is one of the best free website builders for anyone to create a gorgeous, mobile-friendly website easily. For the website builders, Strikingly provides Built-in Analytics, a Google Analytics-like website traffic analytics.

 
Strikingly-Product_visitors

Image is taken from Strikingly Product

Strikingly Built-in analytics gives their end-user information about:

  • Unique Visitors, Top Country, Top Pages, Countries of visitors, Devices, Traffic Sources

Image is taken from Strikingly Product

How Kyligence helps Strikingly build their Built-in Analytics

 
Kyligence-helps-Strikingly-to-build-their-Built-in-Analytics
 

Kyligence helps Strikyling with:

  • Provides a data service that enables website builders to perform traffic analytics
  • Reduces TCO with better performance
  • Managed Services offered by Kyligence to take over data service operation
 

Summary

In this blog, I explained my understanding of data products, four common use case, and how at Kyligence we help empower SaaS companies to provide their in-apps data product for their end-users.

If you are interested in the data product solution Kyligence provides, here is what you try as a next step

  1. you can try out our playground here that can give you a taste of how Kyligence can inspect your SQL queries and recommend data models with a few clicks.
  2. you can also Test Drive the full version of Kyligence on our hosted cloud environment.

Reference & Credit:

[1] Simon O'Regan, Designing Data Products (2018), Medium.com

[2] Max Beauchemin, How the Modern Data Stack is Reshaping Data Engineering (2021), Preset.io

[3] Zhamak Dehghani, How to Move Beyond a Monolithic Data Lake to a Distributed Data Mesh (2019), martinfowler.com

[4] Mastermind Analytics from AppZen Provides Big and Small Picture on Spending (2020), AppZen [

5] Integrate Artifical Intelligence into Your Expense Audits & Systems, AppZen

[6] Experience AppZen: Expense Audit (2021), AppZen

[7] All You Need To Know About Your Website Stats (2021), Strikingly

[8] Using Your Strikingly Built-in Analytics (2021), Strikingly

 

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