Building a Data-Driven Culture

Introduction

Let’s walk through why data is important and also what it takes to build a data-driven culture. A data-driven culture is a culture that understands the significance of the data. It possesses a culture of using data to make all the business critical decisions in the organization.

So why an organization needs a data-driven culture? Because it really pays off in the long term. Most of the companies asked about their analytics and business decision processes found that the more of them identified as data-driven the better they executed with financial and operational achievements. Those in the top 5% are more productive and more profitable than their competitors.

But how do you become a data-driven culture and what does it take?

In my opinion a data-driven culture should posses a culture where everyone buys into the idea of using company data to make better business decisions, organizational structure within company that supports a data-driven culture and of course the technology/tools that supports a data-driven culture.

The most important and the most difficult way for any company is to transition to a data-driven organization that practices Data Ops to move to a data mindset. This move should involve all the people within the organization in a data imagination, from the producers of the data, people that design the data, maintain, eventually analyze it, to the employees who uses data in their day-to-day duties to collaborate on making data the central point of organization decision making. The main point here are the employees and the organization.

To be successful in becoming data-driven organization your employees should always use data from start, continue and conclude every single business decision, no matter how major or minor it is. This kind of culture should drive everyone on the data team, including data engineers, data analysts, data modelers, data scientists to continue elevate and refine the tools that business users need to make their decisions and because the data is accessed and used in every environment, the organization should encourage and position people, processes and technologies that minimize the organizational silos to access that data.

The lead factor to enable a data-driven culture is to make it easy for the data team to capture all of the data in the organization. Each organization has a more than enough of the internal and external data sources. These can range from business applications, products applications, public and private company interactions, monitoring, outside vendor data providers and others. All of the systems are collecting data for analytics being just an afterthought. As a result, it is not just to capture any of this data, but for less, consolidating it in one central place. The valuable data from all of these sources, therefore continues to remain its own silo. Therefore, loses many opportunities of extracting insights by putting data from different sources together.

First of all I would create enterprise data taxonomy that provides means of organizing company’s data assets into hierarchical structure based on organizational subject areas. This provides a unified view of data and introduces common terminology across the organization. The taxonomy will drive the way we structure enterprise data catalog, which will be rolled out in conjunction with for example analytical environment on the cloud. It will also act as a unifying principle for the organization of conceptual data models, the stewardship of data, and the delivery of physical datasets.

Next, take an inventory of all your data sources and create a common data capture infrastructure that is a standard procedures across the company and that lays out the correct way to capture the business data in one Enterprise Data Model. One example is enterprise logical data model that forces analysts to think about the current business requirements, it’s independent of technology, thereby highlighting opportunities for business process improvement rather than simply automating an existing procedure or recreating a legacy system on a new platform. Users understanding of their current systems can prevent them from identifying the true business requirements. The main reason for missed target dates is a poor understanding of the business requirements. Building a complete, essential, logical data model (and linking it to an essential process model) forces the analysts and the business users to completely describe all information requirements of the business area within the organization. Without this rigorous analysis, data elements will be missed or defined improperly.

The next step is to consolidate all the data so that everyone in the organization know where to go to find it and how to use and query it. Consolidating the data in one Enterprise Data Model across the organization helps everyone to collaborate around the data. You will have the business analysts and data analysts that feed the results back to the business asking questions like how to I use the data to improve the products, or how do I get the data back from my customers to change the features of the products to personalize them. When the data is standardized it would be easier for the data engineers to write self service applications and the business users use those to analyze to make strategic business decisions.

The executive team also will benefit from standardized and centralized data. They don’t typically have enough time or perhaps skills to analyze the data themselves, yet they need data to inform their decisions, maybe more than anyone in the company. So, it makes sense to build dashboards so that executive team can access the data in a timely fashion.

Tips

You need to have people in your organization that can see a big picture and understand all the ways that employees can use the data to improve the business.

Organize and model the data in a single centralize place accessible to everyone. This means eliminating data silos and effectively making the data accessible to everyone.

All the employees should feel comfortable taking advantage when it comes to suggesting ways data can be used. If you give employees power to voice their opinions as long as they are being backed-up with data it will keep your company competitive and put you better in a position with time to market.

Invest into self-service data tools, whether it is a tool to access the data, sharing or analyze the data, make sure you train your employees and they understand basic principals of data analysis, transformations, visualizations and querying the data..

Technology can take your employees thus far. You have to encourage employees to use technology and tools. Keep in mind that data doesn’t belong to data analysts, data scientists or DBA’s. It belongs to everyone within the organization.

Roadblocks

Yes, you may encounter some roadblocks to become data-driven organization. You’ve probably heard such phrases, “We can’t let everyone access the organization’s data. There are security policies and compliance problems that may occur” Yes, that’s true, but as long as you put identity to access the data, put access control in place, these issues shouldn’t stop you from becoming data driven organization.

Infrastructure is another potential obstacle, where it might become too expensive to process all the user queries. So you need to address these issues by using infrastructure that can auto-scale, be elastic, self-managed, multi-clustered and available globally.

Conclusion

Creating a data driven organization is not easy, it’s an evolution and journey, but the benefits it will provide are exponential. It will transform the way your organization manage the business so it should come with no surprise that it will also play a role in changing your culture within your organization as well.