Business intelligence initiatives have significantly evolved—changing data analytics practices across teams and departments. And it is much needed as the percentage of data and analytics projects that fail is still over 70%, primarily due to poor adoption, classifying most businesses as lacking BI maturity.
One of the latest trends in efficient modern data analytics practices and furthering BI solutions, is headless business intelligence. Headless BI aims to consolidate the semantic layer of data modeling or create a shared data model that anyone can use to build a dashboard based on single-source metrics. This approach is all about avoiding rebuilding metrics, models, visualizations, security, or deployment and to have a seamless way of incorporating data with third-data BI tools.
Traditional BI tools are not sufficient for joining the semantic model with visualization tools and components (dashboards, reports), it presents the need for headless BI solutions. By building from a shared model and preparing the data, the goal is to create consistent and available metrics across every use case.
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How headless BI work and why choose it
A typical data analytics process requires three key components: data, metrics, and outputs. The data component is the raw information from products or third-party applications. Metrics are defined and calculated values from raw data (DAU, WAU, and MAU ratios). And outputs are the real analytics in the form of a dashboard, reports, API, and such.
Headless BI centralizes the process and definition of metrics and makes the transformed/calculated data available to any output. This makes all data available and consistent. The alternative would make for calculating and defining metrics in multiple places, bound for inconsistencies. For example, a data scientist might use Python notebooks and the data analyst: Tableau, Qlik, Power BI. Therefore, remember that it’s more complex than it seems to centralize data, and it might not always be the best option for a SaaS business.
The product management side of things
Assessing how data is prepared for product use is generally not a product manager’s responsibility, yet it can affect product performance and that is clearly important to their role. Understanding how the product’s data is structured will allow a product manager to identify when new data or metrics are required and how new data may affect their product performance and security of that data. With this level of insight, product managers can identify cost effective and user-friendly solutions that can be maintained over time.
What to consider regarding headless BI
Sacrificing results for efficiency
Each output, such as dashboards, ad hocs analysis, and APIs have particular infrastructure and architecture requirements. Centralizing metrics is the preferred way of making all data and consistent metrics available to anyone or thing that needs them. A good Headless BI solution will allow teams create any output they need without limitations on the performance or infrastructure they choose. However, many teams approach Headless BI with a more monolithic approach because they assume a single solution will be easier to manage. Unfortunately this means that teams have less flexibility to choose their infrastructure specific to their needs. For example, if the product team needs lightning fast performance but the data science team is willing to sacrifice speed for easier discovery. By prioritizing simplistic management of data, this limits what teams can do with it.
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Short-term convenience over long-term maintenance
Many teams begin the journey towards Headless BI or centralizing metrics by attempting to re-use the same metrics for different purposes. For example, metrics used to manage internal performance may look similar to metrics that are used for customer-facing dashboards but they often diverge or use different inputs over time. For example, the way a SaaS company calculates revenue on their platform will evolve over time but the way revenue is calculated for the merchants that use the platform will probably stay constant. It’s not easy to know what the future holds, even if your product team has a perfectly groomed roadmap. Avoid oversimplifying your solution to ensure long-term flexibility.
Multi-tenancy throws a wrench in everything
Gathering data from a centralized source, such as a database or warehouse, for example, is highly complex and challenging to maintain as the system evolves. By pushing all of your business analytics data, like advertising data, into a centralized source, you will combine information that will not be used as part of the customer-facing analytics. With all of this data combined, data segmentation becomes more complex and the number of issues that can occur grows – exposing product features to performance and security risks.
A holistic approach to implementation
Product managers need to ensure their team prioritizes the product performance not engineering efficiency when implementing a Headless BI solution. To do this, take a holistic approach to understand everyone’s data needs so the solution accounts for every workflow in every department. Just like every other feature you build, take an iterative approach and think both short and long-term to help avoid making big mistakes in the future. If done correctly, a Headless BI strategy will transform the way businesses manage their data and increase the speed at which they can move.
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