The primary aim of constructing a data pipeline is to enhance alignment with the business strategy. This involves harmonizing customer value delivery with strategic goals and refining customer and employee interaction processes. This objective is achieved by methodically capturing process-generated signals and presenting the trajectory of strategically selected metrics through user-friendly dashboards.
This process starts with the intentional capture of signals using accessible software applications. Subsequently, the data undergoes processing within a data warehouse to facilitate meaningful visualizations. These visual insights then drive informed decisions aimed at process enhancement, initiating a perpetual cycle of improvement - the essence of the data-driven loop.
At the core of it all lies the customer. Your organization's essence is rooted in addressing their problems, fulfilling their needs, and providing solutions worth their investment. This embodies the business's value proposition: cultivating revenue streams through the delivery of solutions that meet customer demands.
A well-defined data strategy can amplify this value.
The customer's inquiry serves as the input, while the resulting solution forms the output of the process. A process is a meticulously crafted sequence of actions to achieve the desired outcome. Throughout various junctures within this process, specific checkpoints can be established to monitor key factors. These could encompass metrics like response time from inquiry to resolution or the involvement of different departments in formulating the response.
Understanding these intricacies of your processes becomes paramount when embarking on a data project. A comprehensive understanding of how your business operates paves the way for effective data-driven initiatives.
By going through the different steps in the process, signals are produced. In some cases these signals are kept on paper, or in the minds of the employees. These days the signals are usually captured in a software application that supports the process. In the context of the use of data to follow up on strategy this is the moment data is created: the moment a signal is captured in a software systems and is saved to an underlying database. Technically this means you register a certain transaction with certain properties in a relational data model.
An example of a step in the process where data is created in a software system, is a request for a quote from a prospective customer that is registered in a CRM system. Or the invoice of a purchase of a gift for a customer that is booked into the accounting software.
In order to use the data in these systems to follow up your strategy a number of conditions have to be met.
First the data has to be of sufficient quality. A good understanding of the people who know the customers and the process best. Make sure you understand what the mean when talking about important concepts like 'Customer', 'Product', 'Order', 'Delivery', 'Success'. On first sight this seems crystal clear, but when you go into it you will soon discover a lot of different interpretations are possible. It is important to come to a common understanding to make clear how the input in the systems should be done. To a certain extend certain rules can be programmed into the systems to avoid faulty input.
Second the data has to be captured. For a lot of processes it's technically not necessary to store the data after the process has been successfully completed. Sometimes too little detail is kept to distill valuable insights. Also by overwriting data, instead of register the changes, information is lost.
Third the data has to be accessible. It has to be possible to get the data out of the software systems to use it in analysis. With the rise of SaaS solutions in the cloud, this is not always as easy as you would expect in the current day and age due to limited API capabilities. When you had to install the software on your own infrastructure it was often easier to access your data than it is now, where you have to depend on the maturity of the API's the SaaS vendors are offering. Make sure you do your research in advance when choosing a software application!
Often the software applications we mentioned here above have dashboards of their own to follow up what's going on. The problem with these dashboards, is that they only use their own data. There is huge value in combining the data from different systems in an integrated data model. This integrated data model should reflect what's actually going on in the organization, breaking down the silos that can exist. A good data model should allow you to follow up how the organization is doing as a whole, not just in it's separate parts.
To follow up on the measuring points you defined in the processes, so how the customer can be provided with a better solution for his problem, is done in a visual way by presenting the data in intuitive charts and tables. This is called a dashboard. The most important measuring points are an indication on the performance of the organization we call Key Performance Indicators (KPI's).
So why am I telling you all this? I help people make sense of their data. This is the framework I use to discover where the risks and opportunities are in your organization. During a leadership alignment session I will go through these different levels where data is relevant in different ways. Depending on the maturity of the organization I meet you where you are and together we figure out which one of the tracks of the data strategy roadmap suits you best at this time.
