From Silos to Collaboration: How to Democratize Data in Product Analytics
Companies who develop software products generate massive quantities of product performance and user engagement data that can be analyzed to support decision-making about everything from feature planning and UX design to sales, marketing, and customer support.
Leveraging product data throughout the enterprise represents a significant opportunity to achieve a competitive advantage, but challenges like siloed data systems, poor data literacy, and the complexity of data analytics in the cloud can prevent organizations from making full use of their raw data.
The answer is data democratization: the ongoing process of centralizing data from multiple sources, breaking down silos, providing tools, and establishing data governance procedures to make product analytics accessible to everyone in the organization.
In this week’s blog, we’re taking a closer look at the benefits and impact of data democratization in product analytics. We’ll share exactly how democratized data access can help organizations build more successful products, along with five of the biggest challenges faced by organizations democratizing data access.
What is Product Analytics?
For companies that build and distribute software products, product analytics is the practice of collecting, processing, and analyzing data to understand how users are interacting with the product and develop insights into product performance and user engagement.
Product analytics can leverage several types of data from a variety of sources, including user behavior data from real product interactions and engagement, telemetry data (logs, metrics, and traces) from cloud apps and services, website or app usage data, customer feedback, and sales and marketing data.
By analyzing this data, companies can gain valuable insight that helps to answer important questions like:
- Which product features are driving revenue growth and customer engagement?
- Which product features are driving customer churn?
- How are users navigating through the product?
- How often are users encountering bugs or errors within the product?
- What UX changes could enhance customer experiences?
- What new features are most likely to drive additional product usage?
- How effective are our marketing campaigns at driving product engagement?
- What is the average load time/network latency of the product?
- What is the typical CPU/memory usage of the product?
- What is the average response time of the product or its components?
Product analytics empowers companies to make data-driven decisions about product development, feature prioritization, user experience design, sales and marketing strategy, customer retention, and more. Product analytics can also help companies discover new opportunities for growth and optimization, identify potential areas of improvement and diagnose performance issues to ultimately create better products.
What is Data Democratization in Product Analytics?
Data democratization is the ongoing process of making product data available and accessible for analytics applications to all departments (e.g. sales, marketing, customer success, product team, etc.) within an organization - not just the data experts that work in IT.
That’s because it isn’t just specialized data teams and product analysts who can benefit from product analytics - it’s everyone:
- Software developers can use product analytics to prioritize which features to work on, identify and resolve bugs, optimize application performance, or assess the health of cloud services and infrastructure.
- UX designers can analyze product data to understand how users are navigating the product, how they interact with product features, which parts of the product are driving churn or drop-off, and how to optimize the user experience.
- Marketers can use product analytics to understand the customer journey, measure the impact of marketing campaigns or promotions on user behavior, and optimize their marketing strategies and campaigns.
- Sales teams can use product analytics to identify the most popular and high-performing features, determine which customer segments are the most profitable, and adjust their sales strategies to maximize revenue generation.
- Customer success teams can use product analytics to identify user pain points and other issues impacting customer satisfaction and retention.
The goal of data democratization is to foster a culture of data-driven decision-making throughout the enterprise by enabling and ensuring data access for all departments, and providing both training and tools to help users generate valuable insights from product data.
Most importantly, data democratization in product analytics removes the IT department and data teams as a bottleneck between users and insights. When users across the enterprise are working with data and running their own product analytics, IT departments save time and resources that can be allocated to more productive activities.
Why is Data Democratization Important in Product Analytics?
In many companies without a democratized approach to data access, product data is provided exclusively to specialized BI or data analysis teams who analyze the data and share insights with other stakeholders in the organization. A common approach is to Extract, Transform, and Load (ETL) product data into a data warehouse where it may be accessed for analytics by BI, analysts, and reporting teams.
However, these approaches have proved less than ideal when it comes to maximizing the value and impact of product data.
Restricting data access to just a few teams means that only those with specialized skills get access to product data, and there’s significant risk that the insights generated by these teams won’t always align with the needs of other departments. Plus, relying on these teams to run product analytics for the whole organization can create bottlenecks and delays in the analysis process, resulting in delayed decision-making and missed opportunities to leverage data into value.
Data democratization gives every department access to product data and allows users throughout the enterprise to run their own product analytics and extract relevant insights without involving specialized teams of BI or data analysts. In doing so, data democratization helps enterprises eliminate bottlenecks in the analytics process, break down data silos, and accelerate insight generation to support data-driven decision-making.
5 Data Democratization Challenges in Product Analytics
Data-driven organizations in 2023 are embracing the concept of data democratization and recognizing its potential to deliver a competitive advantage. But despite its clear benefits, organizations seeking to democratize access to data will have to overcome key challenges to be successful:
1. Complexity of Data Analytics Infrastructure
The complexity of existing data analytics infrastructure is one of the primary reasons why data democratization can be a challenge. In most organizations, data engineering teams build pipelines that extract product data from applications and services, normalize it, and load it into a data warehouse to support downstream analytics applications. Building and maintaining these pipelines is complex and time-consuming, requiring specialized skills that end users don’t have.
To democratize data access, organizations need to modernize and simplify their data analytics infrastructure while offering tools and technologies that make it easier for non-technical users to engage with product data.
2. Siloed Data / Lack of Centralization
Another challenge for data democratization is data silos within the organization and the lack of centralized storage for enterprise and product data. Too often, marketing data stays within the marketing department, sales teams generate and manage their own data, and product data stays in IT until someone requests it.
To get the full benefits of data democratization, organizations need to break down data silos and aggregate data from multiple areas of the business, creating a single source of truth where data is openly shared and readily accessible for new kinds of analysis.
Read: 3 Use Cases for Relational Access to Log Data
3. Data Security and Privacy
Data security and privacy are critical concerns when it comes to democratizing data access, especially for businesses operating in industries with strong regulatory oversight and compliance requirements. Making data more accessible to a wider range of stakeholders can increase the risk of unauthorized access, data breaches, and other security threats.
Effective data governance and security protocols must be established to address these concerns and ensure that data (especially sensitive customer data) is protected from unauthorized access and misuse.
4. Data Literacy
Data literacy is critical for a successful data democratization initiative. Without a basic level of data literacy, individuals may struggle to understand the meaning and context of data, or may misinterpret it, leading to incorrect or suboptimal decisions.
To address this challenge, organizations must invest in data literacy training and support for all employees, including those who do not have a technical background. This can help to ensure that every department has the skills and knowledge to enhance its decision-making using product analytics.
5. Data Quality and Consistency
Data quality refers to the accuracy, completeness, consistency, and relevance of the data. If an organization’s product data is poor in quality, it can lead to suboptimal decision-making that negatively impacts business outcomes. If users lack confidence in the quality and reliability of the organization’s data, they will make decisions without consulting it.
Organizations need the right tools and technologies in conjunction with robust data governance processes to ensure their product data remains accurate, complete, and relevant.
Implement Data Democratization for Product Analytics with ChaosSearch
ChaosSearch® is a cloud data platform that transforms your AWS or GCP cloud object storage into a hot data lake for direct and accelerated analytics. Simply land your product data in Amazon S3 or GCS and ChaosSearch automatically indexes the data for full-text search and SQL analysis with up to 20x file compression.
Once your data is indexed, our Chaos Refinery® tool lets you apply virtual transformations to analyze the data with no data duplication, no changes to the underlying representation, and no deterioration of data quality.
ChaosSearch helps organizations overcome data democratization challenges by:
- Enabling a modern, simplified analytics architecture that minimizes cost and complexity,
- Providing a simple interface for querying and visualizing data that’s accessible to users with a wide range of technical expertise,
- Ensuring data quality and consistency by applying virtual transformations while preserving the original data, and
- Delivering security features like encryption, Single Sign-On (SSO), Roles-Based Access Controls (RBAC), and audit logging that protect data privacy and security.
Ready to learn more?
Read our white paper Digital Business Observability: Analyzing IT and Business Data Together to learn more about how organizations benefit from democratizing access to business and IT data.