How to Nurture a Data-driven Culture in Your Organization

In today's data-driven world, the most successful companies are the ones who are able to adopt a data-driven culture across all levels of the organization. This data-driven mindset enables everyone to make use of data and analytics to make decisions, drive innovation, and improve business performance.

Creating a data-driven culture can be challenging, but it is essential for organizations that want to stay competitive. In the Gokongwei Group, the JG Summit Digital Transformation Office (DTO) is using training programs and learning communities to establish foundational data skills for a data-driven culture throughout the conglomerate.

Empowering Employees with Data Democratization

Data democratization is a crucial first step in developing a data-driven culture. Data democratization refers to the process of making data accessible, understandable, and usable by a wide range of people within an organization, regardless of their technical skills or expertise. Through this process, employees at all levels are given access to data relevant to their needs and trained to develop the skills to use this data. This promotes inclusivity and transforms the way companies utilize their data, often increasing the number of people who actively access and use data in their day-to-day as well as strategic tasks.??

Training Everyone to Know How to Use Their Data?

To ensure that everyone knows how to use the data available to them, it is important to establish foundational data skills through a series of training programs and workshops. These training sessions are further reinforced by continuous review and application, so that learners can see how data analytics skills are applied in actual scenarios and projects.

It is also important to embed employees at various role levels and degrees of expertise in various communities to encourage knowledge sharing and to foster ongoing collaboration and conversations about data and analytics. To do this in your organization, consider these steps:?

1) Identify Knowledge Gaps?

Start by identifying knowledge gaps in the organization. This involves assessing the current level of data literacy within the company and identifying areas where employees need training. It also includes prioritizing which groups or business units to start with, usually those that can most benefit from training and with potential pilot projects to implement.??

For example, during the pandemic, many business units discovered the necessity of activating online channels such as websites, apps, and social media platforms, but needed help monitoring the resulting interactions and analyzing the profusion of available metrics. In these cases, training on analyzing and narrating the insights was useful in helping the teams make sense of the data.?

2) Design and Conduct Training Programs

Once knowledge gaps have been identified, organizations can focus on building data analytics skills by designing training programs that are tailored to the needs of the various teams. JG Summit DTO’s training programs provide the necessary data skills and knowledge, using a variety of formats such as in-person workshops, online courses, and mentoring and coaching sessions.

The training should be interactive, engaging, and relevant to the employees' work. Ideally, even when designed for specific roles and users, the sessions should also be applicable to other groups and can be repurposed to optimize the resources used and ensure that as many business units as possible can benefit.

One such training exercise was the Data for Everyone session, a half-day virtual session meant to establish a common understanding of the basics of data, which was open to all employees regardless of function. This capability-building program catered to all competency levels across the conglomerate and covered topics such as data science, data visualization and storytelling, and machine learning at an introductory level.?

3) Make Resources Accessible?

To get around the limitations of individual schedules and ensure that training sessions have the widest possible reach, all learning resources should be made accessible through searchable shared portals. These learning platforms can be leveraged for onboarding and general training purposes, and can be monitored to ensure that employees have the required data skills and training for their roles.??

JG Summit DTO’s Sharepoint houses workshop recordings and slide decks which can be accessed by all users who can then review the topics at their own pace. The Data Community of Practice, meanwhile, has its members’ resource center for checklists and data tools.

4) Build Learning Communities?

In addition to training programs and workshops, organizations can also build various learning communities that foster a culture of continuous learning and collaboration. These provide opportunities for employees to interact with others with the same interests, consult with more experienced colleagues, and engage in a culture of experimentation and knowledge sharing. Typical examples of learning communities include communities of interest and communities of practice, which differ in the degree of skill, involvement, and experience of the members.

The Data Analytics Community of Interest is a community of over 400 Gokongwei Group colleagues who share a common interest and passion for data. Members don’t have to be formal data practitioners; an employee with an interest in data analytics can join regardless of his or her function. The group was formed to leverage each other's shared passion and interest to strengthen the teams’ knowledge and skills around the rapidly growing field of data analytics, with the goal of better applying data analytics to their respective organizations. The group meets monthly and hosts internal and external speakers who discuss data analytics examples, case studies, and related topics.??

There is also a smaller and more focused Data Analytics Community of Practice composed of data practitioners in the conglomerate. This group of data professionals holds case clinics for research and development and shares innovations implemented in their work, which eventually become documented case studies that drive best practices throughout the group.?

5) Host Knowledge Sharing Activities?

Relevant communities can then host knowledge sharing activities to disseminate their best and most relevant practices and findings. Other organizations conduct lunch-and-learn sessions, guest talks to share actual data analytics examples, and more involved workshops such as hackathons or data challenges, where employees can work together to solve real-world business problems using data.

As mentioned, the Data Analytics Community of Interest hosts monthly talks, which throughout 2022 covered some of the following topics: understanding business needs as first step in analytics, using customer analytics to maximize profitability, determining if data contributes to business impact, defining the scope of a data project, and others.

These talks are slanted towards practical business users and bridge the gap between theoretical data concepts and business applications. The community also maintains a Teams channel which serves as a venue for sharing articles and links.

6) Encourage Collaboration?

Finally, beyond talks and workshops, communities can also facilitate projects carried out by cross-functional teams of members from different departments or even business units. By working together, employees can learn from each other and apply their knowledge and domain expertise to solve complex business problems. Data tools developed by in-house groups of various business units or by these cross-functional teams can be shared with others, possibly in the form of reused code or shared platforms that ensure the rapid spread of innovation and development throughout the conglomerate.?

The Data Analytics Community of Practice has a number of examples where custom-made tools were developed jointly in collaboration with the DTO and different Gokongwei Group business units. These reusable data tools are available to members and simplify and automate repetitive tasks used across all businesses.

Examples include a customer lifestage segmentation algorithm, a tool that uses pre-trained open source GloVe vectors to represent text, a tool that uses OpenAI's Whisper and GPT-3 APIs to automatically transcribe and summarize texts, and a tool used to summarize tens of thousands of customer comments and find major themes among these.?

In conclusion, establishing a data-driven culture in your organization is essential for staying competitive in today's market. By providing employees with the relevant training needed to develop the skills and knowledge to work with data, and facilitating a culture of continuous learning and collaboration through related communities, organizations can create a data culture that uses data to drive innovation and improve business outcomes.?

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