Assessing capabilities

1. Assessing capabilities

Welcome back! In this video, we'll explore how organizations can assess and improve their data culture program using a maturity model.

2. Maturity models

Maturity models are valuable for assessing and planning organization improvement efforts. They typically have multiple levels or stages, each representing a degree of competency in specific areas. The typical structure for a maturity model is arranged in a hierarchy. Achieving higher levels on a maturity model is a great way to progress towards more effective and efficient processes, better alignment with business goals, and improved performance.

3. Customizing maturity models

Organizations often customize maturity models to suit their specific needs and goals. For example, you're seeing two different models for data maturity, the Gartner and IBM model. Although they have different names for each step, they ultimately achieve the same end goal. While these models provide a helpful approach to handling data, they do not address all aspects of creating a data culture. Let's create a simple five-level maturity model and apply it to see how valuable having a maturity model is for measuring the success of data culture.

4. Awareness

The initial level is named Awareness, where the organization knows that data is important but lacks a plan for how to understand and analyze its data.

5. Adoption

The Adoption level comes after the Awareness stage. At this point, some stakeholders within the organization have attempted to make data-informed decisions but the approach is often limited or siloed.

6. Standardization

The next level is Standardization, which is achieved by breaking through the siloes between different pockets of the organization and acting as one unified vision.

7. Optimization

Next comes Optimization. This is when the organizations try to improve efficiency by coordinating between different groups and data streams. As the organization improves how they handle data and communication, employees work more smoothly together. This ultimately leads to the final level, Innovation.

8. Innovation

At the final stage, a data culture is born and data's value, importance, and utility is realized at every level. Organizations can now use data to drive innovation.

9. Case study: Shine & Glow

With our maturity model in mind, let's take a look at a fictional company, Shine & Glow, which specializes in organic skincare and makeup products. Shine & Glow have a massive amount of data about their customers, products, and sales, but they lack a clear idea of how to use it effectively. They decide that leveraging a data culture will reap significant rewards. On the whole, Shine & Glow are at the awareness level, but looking deeper, we can see pockets of maturity that we can cultivate, augment and enhance. The marketing team has a robust approach to data, while the product development team makes all their decisions based on gut feeling. Shine & Glow, knows that in order to succeed, they need to work to get everyone on the same level to achieve business success through their data investments. They're thus interested in ways to improve.

10. Bridging the gaps

After assessing their current data culture maturity and identifying areas for improvement, Shine & Glow began to bridge the gaps between different maturity levels. They developed a roadmap outlining specific actions for achieving the next level of data culture maturity, including introducing new processes, tools, or technologies. They also found that more advanced teams can be models for others to follow. For example, instead of creating a new data platform, the marketing team's data platform could easily accommodate new data streams from research, breaking siloes in data infrastructure across the company. They also monitored progress and made necessary adjustments along the way. Shine & Glow can continue progressing up the maturity model towards innovation by fostering collaboration and standardization. It's important to note that there's no one-size-fits-all solution to build a data culture. However, starting with an honest assessment can make the process much easier and help highlight where to focus efforts.

11. Let's practice!

Time to put your knowledge into practice!

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