How to collect useful data for supporting your decision-making process? If you’re not collecting data yet, this is the obvious starting point, But before you jump into data collection, try to understand what types of data you’re going to need and whether that data is static or dynamic.

In a previous article, we talked about how to know if your data is useful by understanding your data collection strategy and identifying the types of data you can leverage and how coherent it is. As a brief recap, static data implies that the state of an entity such as a user or a machine doesn’t change or rarely changes, whereas dynamic data represents information that is in flux, describing interactions between an entity and a system.

Download our free guide and start collecting useful data and generating powerful insights


How can you collect static data?

Static data is usually captured through forms. For example, a landing page could propose a signup form for an online application; a spec sheet could represent the characteristics of a machine; a blueprint of a house might describe the form and materials used in construction; or a CV that is filled out online by candidates could reflect a set of skills and experience that describe a person.

How can you collect dynamic data?

Dynamic data is usually collected via an analytics tool that logs events, or data points that represent interactions between an entity, such as a person or a machine, and a system. Events refer to interactions that have occurred and allow us to track behavior over time. Event properties represent precise descriptions of these events. If an analytic event were used to track a pass on a football field, the event could be called “passed_ball” and its properties could be “time”, “passer_id,” “receiver_id,” “ball_height,” etc.

Some examples of analytics tools include Google Analytics, Mixpanel and Segment, which collect information on user behavior across applications, but also from hardware. Bloomberg terminals that register real-time financial data and supply chain management systems that register the flows of goods and services between various entities are also examples of analytics tools.

Thinking about infrastructure

It’s also important to understand that if you need to collect data from the physical or digital worlds, your collection infrastructure will likely have to be adapted to your specific situation and may have implications on your budget.

If you run a SaaS application, you will likely be collecting data from digital sources, using forms or analytics events. If you’re a manufacturer selling home appliances for connected homes, you will likely be collecting more dynamic data from the environment such as temperature fluctuation or the configuration process of a heating system. For such capture you will need physical devices to capture and send information to analytics tools, but also the spec sheets for each appliance and perhaps blueprints of the space.


Once you have defined your data collection strategy and infrastructure, it’s time to make sure that the data you are collecting is coherent.
1. The first step is to guarantee that the data is collected across homogeneous entities (users and users, customers and customers, machines and machines, etc.).
2. Secondly, you must make sure that the tracking of fields, events and properties are the same for comparable entities across your entire system.

  • The coherence of static data is fundamentally based on forms that have the same fields. As a result, it’s important to know the percentage of common fields across all static information collection services.
  • The coherence of dynamic data, means that you will want to track the same events across all similar interactions


Our experience shows that the best way to collect useful data without disrupting your business is to link your data collection strategy to your business goals. For this reason, at metriq we have developed an a methodology we teach internally and apply to all of our projects..

  1. It all starts with an educational course to understand what you want to do with your data. This program emphasizes an approach to reducing risk, while clarifying, quantifying and tracking objectives.
  2. Once your objectives have been defined, we identify how much data you have and how useful it is.
  3. This data audit enables us to define and implement a strategy adapted to your organization’s needs:- If you have no data, we work with you to define what data you need and how to collect it.
  • If you have data, but it’s not useful, we identify ways of collecting new data or enriching your existing data to make it useful.
  • If you have data and it’s useful, we structure it for some application using a model or algorithm.

If you are interested in learning how metriq can support your company in making data more useful, don’t hesitate to schedule a call with one of our experts.

Let’s chat!