Value Engineering from Data
“The world’s most valuable resource is no longer oil, but data.” The Economist for May 06, 2017.
Indeed it is; however, data is only as valuable as the insights it can provide. Analytics help us make better decisions by uncovering hidden patterns and trends in our data. Doing so, on one hand, can impact business efficiency, and on the other hand, improve the quality of human experience.
When it comes to business efficiency, data can help create better products and services, as well as generate new revenue streams. This well-being is then passed on to the end consumer who now has high-value products to improve the quality of their life with.
We need collaboration between data engineers, data scientists, and business subject matter experts to generate value from data.
Now the big question is: How can we create value from data continuously?
Data is not all about technology
Data is often seen as a purely technological topic. Technology is a critical enabler in collecting, storing, processing, and publishing data but analytics require a fair share of domain expertise. We need collaboration between data engineers, data scientists, and business subject matter experts to generate value from data.
Organizations invest in setting up data teams, platforms, and agile development practices. However, the journey of continuous value generation from data neither starts nor ends at such technical capabilities.
Business teams must own data initiatives
The data analytics story begins with business problems, such as a mobile app company being unable to retain users. Such problems are often part of strategic initiatives, i.e. the company’s goal is to reach X number of active users, but they are having trouble doing so. To understand user behavior and correlate it with user retention, we must first define the critical business concepts such as what is an active user, what is a churned-out user, how much time of inactivity is acceptable, etc.
An organization may run multiple initiatives simultaneously, and triaging to prioritize analytical use cases helps efficiently use data teams and technical capabilities. All such activities need ownership from relevant business stakeholders in the organization.
As we embark on developing data, it is critical that we invest time in designing and planning to ensure we identify key user personas and map out their journeys. This will allow us to gain a complete understanding of the various use cases.
Additionally, we need to lay out the data architecture to accommodate the ingress, storage, and processing of data. Finally, we must take the business use cases and formulate them into smaller data science tasks that can be executed by data developers.
The data implementation projects vary in scope and complexity, therefore it is essential to estimate the resources and time required for successful completion. At this stage, data platforms and agile development methodologies can be very helpful in managing simultaneous implementations and reducing time to value.
Data is more valuable than oil because we can continuously extract value from it if we apply the proper mechanisms.
As data analytics becomes commonplace in a company, it is vital to treat the outcomes of projects – such as reports, dashboards, and models – as digital products and services. This way, we can ensure that the value generated by these projects is continuous and high-quality. To do this, we must maintain the data infrastructure, the quality of the data itself, and the quality of generated insights. We must ensure we have people, processes, tools, and governance in place to maintain data products and services.
Data is a renewable resource, unlike oil. It is more valuable than oil because we can continuously extract value from it if we apply the proper mechanisms.