3 Ways to Break Down SaaS Data Silos
Access to data is critical for SaaS companies to understand the state of their applications, and how that state affects customer experience. However, most companies use multiple applications, all of which generate their own independent data. This leads to data silos, or a group of raw data that is accessible to one stakeholder or department and not another. Data silos also prevent information from different sources from being blended together to gain a more accurate picture of what’s happening in your application.
Other companies have cultural issues, including a lack of cross-departmental collaboration, which contributes to these silos. Data silos create all sorts of problems without the right data management strategy, including:
- Inaccurate or incomplete information
- Duplication of effort across teams
- Inconsistent data across resources
- And more.
This blog will focus on three ways that companies can enable their data analysts to gather a 360-degree view of their applications via customer data on usage. This data can be used to help drive product-led growth (PLG) and improve user experience.
How to break down data silos
While there are many ways to improve access to data for analytics, we’ll focus on three ways to make an impact on data silos immediately. As a result, you’ll be able to generate a more holistic view of your application data and analyze its impact on customers.
1. Leverage a cloud data lake
If we’re going to talk about data lakes, we’d be remiss not to talk about the differences between a data lake vs. data warehouse. Some features and use cases are the same, but for a cloud-native world, a data lake is preferable.
Data warehouses extract data from transactional/operational systems, transform it into a usable format, and load it into business intelligence (BI) systems to support analytical decision-making. A big pain point for data warehouses is the need to move data from its source in order to analyze it, since data must fit a predefined schema. To contrast, in a data lake, unstructured, semistructured, and structured data can all live in the same place. Instead of transforming data before it enters a data lake, data is transformed at query time.
Many SaaS companies already use a cloud data lake for storage (e.g. Amazon S3 or Google Cloud Platform) but may not be leveraging it effectively for data management and data analytics at scale. Data can be sent to a cloud data lake from a variety of sources and stored for longer periods of time at lower costs. Data lakes tend to be more flexible for SaaS companies because they can store all kinds of data, only transform data at query, and have loosely coupled storage and compute requirements.
As a result, they often work well for managing data with undefined use cases. But they typically require the expertise of data engineers or data scientists to figure out how to sift through all of the multi-structured data sets. Data lakes also necessitate integrations with other systems or analytic APIs to support the analytics use cases for PLG or product usage data analysis.
2. Integrate tools into the data lake for effective log and event analytics
You may not realize it, but DevOps analytics and application telemetry data are important parts of PLG. Operational IT data can be critical for monitoring and analyzing key PLG metrics. That’s because operational data can provide deeper application insights to help teams understand certain user behaviors, including:
- Active customers
- Time in app and time to value
- Customer activities for discovery, activation, monetization, retention and referral
- Types of users
- And more.
Often this data is collected across a variety of disparate and siloed systems, making it difficult to generate meaningful analysis or results. A cloud data lake approach can help teams gain a single source of truth for live log analytics and SQL-based event analytics, so everyone at the company can trust data across domains and be truly data-driven.
Using tools like ChaosSearch, teams can blend data across IT and SaaS applications to discover unique insights. With live ingestion and fast time to value to explore log and event data in real-time, DataOps and ProductOps teams can quickly discover product and customer data insights. Some examples might include using production log data to speed up software delivery and performance, or correlating user data with system events to understand user actions. For ease of use, and to keep costs low, choose a solution that allows you to catalog, index, and search data without having to move data out of the data lake.
3. Encourage a culture of data literacy
The right tools do little good however if employees don’t understand how data access benefits their line of business. That’s where data literacy comes in.
Building a company culture of data literacy starts with your people. Here are a few tips to get them mobilized.
- Get executive buy-in. C-level executives must understand the impact data democratization can have on an organization. To do this, give specific examples of how line-of-business leaders can be empowered by data, and use others’ stories to demonstrate the success of data literacy programs.
- Assess employees’ baseline skills. Have business users take an assessment or self-identify areas of strength and weakness with data. This information will give you a good starting point.
- Work with HR and data teams to set objectives. You may decide to pilot a data science education program with a small group of employees first, or roll it out to the entire organization. It’s important to have a cross-functional team set a plan for employee development, and determine how to measure success.
- Provide access to data literacy training. Fortunately, there’s no need to start from scratch to get a good curriculum for data literacy. There are tons of free or low-cost online data literacy courses. Pick one based on your employees’ skill levels.
Ready to unlock data silos at your organization?
With the right strategy in place, organizations can build a culture of data-driven product management and improve customer experience. An important step along the path is achieving digital business observability, with the right tools and systems in place to unlock data access for key business stakeholders. From there, it’s easier to gain a more complete and accurate picture of your data to inform decision-making that improves customer experience, success and retention.