By using data to track your strategy, you ensure that the way you work contributes to the achievement of your goals and discover how to refine customer-employee interactions. This goal is achieved by methodically capturing signals that arise in your organization.
These signals arise as you go through processes. Processes to deliver products or services, support customers or handle administration, for example. These signals are captured and displayed in user-friendly dashboards.
You start with targeted capture of signals using software applications. When storing these signals in the databases of the software applications, you transform the signals into data. An intermediate step is usually required to visualize this data. This is done in a data warehouse.
The visual insights then lead to informed decisions aimed at improving your organization. This creates a continuous cycle of improvement - the essence of the data-driven organization.
The customer is central. The essence of your organization is rooted in addressing their problems, fulfilling their needs and providing solutions that are worth their investment. This is the company's value proposition: generating revenue by delivering solutions that meet customer requirements.
A well-defined data strategy reinforces this value to your customers.
Customer demand serves as the input, while the resulting solution is the output. A process is a carefully constructed series of actions to move from input to output to achieve the desired result. At various points in this process, specific control points can be set. These include measurements such as, for example, the response time from request to solution for a customer request or how many different departments are involved in working out this solution.
Understanding these intricacies of these processes is paramount when embarking on a data project. A comprehensive understanding of how your business works paves the way for effective data-driven initiatives.
Going through the various steps in the process produces signals. In some cases, these signals are kept on paper, or in the minds of employees. Today, signals are usually captured in a software application that supports the process. In the context of using data to track strategy, this is the moment when data is created: the moment when a signal is captured in a software system and stored in an underlying database. Technically, this means recording a particular 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 potential customer that is recorded in a CRM system. Or the invoice for a purchase by a customer that is recorded in accounting software.
To use the data in these systems to follow up on your strategy, a number of conditions must be met.
First, the data must be of sufficient quality. You can achieve this by having the data entered or checked by the people who know the customers and the process best. Make sure it is clear what important concepts such as “Customer,” “Product,” “Order,” “Delivery” mean. At first glance this seems obvious, but when you get into it you will soon discover that many different interpretations are possible. It is important to arrive at common definitions to make it clear how input into the systems should be done. To some extent, certain rules can be programmed into the systems to prevent erroneous entries.
Second, data must be captured. Often it is not technically necessary to store the data after a process is successfully completed. Sometimes too few details are kept to distill valuable insights. Also, by overwriting data, rather than recording the changes, information is lost.
Third, data must be accessible. It must be possible to extract the data from the software systems to use for analysis. With the rise of SaaS solutions in the cloud, because of the limited API capabilities, this is not always as easy as you might expect. When you had to install the software on your own infrastructure, it was often easier to access your data than it is now. Now you were dependent on the maturity of the APIs offered by the SaaS vendors. Make sure you research this in advance when choosing a software application!
Often the software applications we mentioned above have their own dashboards to track what is going on. The problem with these dashboards is that they only use the data from their own software system. There is tremendous value in combining the data from different systems to arrive at insights, but this requires an integrated data model. This integrated data model must reflect what is really going on in the organization, breaking down the silos that may exist. A good data model should allow you to track how the organization is doing as a whole, not just in the individual parts.
You can track the metrics you have defined in the processes in a visual way by presenting the data in intuitive graphs and tables. Such a collection of visual elements is called a dashboard. The most important measurement points that give an indication of how the organization is doing are called Key Performance Indicators (KPIs).
The purpose of the KPI dashboard is to define actions to improve your organization. An example of an action might be to better align your product gama with the needs of your customers. But by doing this, the processes and systems change and so does the data, so you have to evolve the data pipelines, data integration and data visualization along with them. A business intelligence project is not a linear, one-time thing; business intelligence is a cycle of continuous improvement.
So why am I telling you all this? I help people understand their data. The story above is the framework I use to discover where the risks and opportunities lie in your organization. In the first phase of a project, I 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 what concrete steps are best for you right now.
