Elements of data-driven culture
Data collection
Data is undoubtedly a key ingredient in a data-driven culture. But we're not talking about just any data, but the right information. The data set you work with must be relevant to the questions your company wants to ask. It also has to be accurate, clean and objective . But most importantly, it must be data you can trust .
Data must be in a format that can be merged with other chinese overseas british database in the company when needed. And the company must operate through a culture of information sharing.
Imagine an emergency patient who receives treatment and is then discharged with the requirement to go to an outpatient clinic for follow-up. The patient will receive worse care if the hospital and clinic do not share data: when, where and why he or she was admitted, what problems he or she had, what treatment he or she received, etc.
At the business level, something similar happens: the whole is greater than its parts; that is, the more data the different departments of the same company share, the better decisions can be made .
Query tools
A data-driven company must have the right tools to query and slice data . All reporting and analysis requires filtering, grouping, and aggregating data to reduce large amounts of raw information into smaller groups that make it easier to understand what's going on . You need to be able to see trends, or understand differences between customer segments. Analysts need to have tools that allow them to calculate those metrics with relative ease.
Human resources are the key to data-driven culture
Reliable and accessible data is essential, but not sufficient. The key to a data-driven culture is having people with the skills to interpret that data . The skills in question include, but are not limited to:
Ask the right questions.
Extract the right data and metrics.
Analyze them in a way that can be used for decision-making.
Data-driven elements or phases of culture
Information
Let’s say you’ve created an analytics group with access to accurate data. The group pulls in sales data and generates a report proudly stating that the company’s operations grew by 5.2% between April and May.
That report sounds like it could be from a data-driven company. However, that information is insufficient. Of course, having sales metrics is a good thing. The CFO and CEO will be interested in those numbers, and even happy about the increase. But what does that 5.2% number say? The answer is very little. There are many possible reasons why the company's sales may have grown. The actual number is just that: a numerical value with little to no context .
Alerts
Alerts are reports about what is happening right now . They typically provide very specific data with well-designed metrics. But, like reports, they don't explain why those events are happening or offer solutions to address problems. Similarly, alerts lack context and offer no causal explanations .
Analysis
Reporting and alerting are necessary features of a data-driven culture, but not sufficient. However, we should not underestimate the importance of both. Reporting is a very valuable component of a data-driven organization. The problem is that there are many organizations that focus on generating reports of which they do little or no real objective analysis.
Reports tell them what happened in the past , and provide a baseline from which to observe changes and trends. This information may be interesting and keep some investors and shareholders happy, but it is a fundamentally backward-looking view of the world. For a company to be considered data-driven, it must go further: look into the future and analyze. What defines these types of companies is their ability to dig deeper and find out why numbers are changing and, where appropriate, make testable predictions or conduct experiments to gather more data that sheds light on why.
Reports explain what happened. Analysis explains why it happened, and recommends what the organization can or should do to replicate the outcome.
Reporting is, in fact, descriptive. Analysis, on the other hand, is prescriptive. Only by understanding why something happened, can you formulate a plan or a set of recommendations. Do you know who is qualified to analyse and offer solutions? A person trained in Business Intelligence and Data management. Take a look at our dedicated master's degree and specialise.