Data Science Strategy

A Data Strategy: Making Data a first-class citizen

Data strategy talks about the importance of Data Strategy. And list a generic framework that can be used to develop and implement a good data strategy.

Why do organizations need a data strategy?

Data science relies heavily on the availability of multiple production data sets to build systems that can extract valuable insights from data. Unfortunately, many organizations still rely on outdated technologies and code-first approaches, leading to long, complex processes for data scientists.

This is why a paradigm shift is necessary for organizations to keep pace with the increasing volume, variety, veracity, and velocity of data. The diversity of analytical approaches and use cases requires a more flexible and agile approach to data management.

For example, consider the process of building a predictive maintenance system. A data scientist would need access to data such as asset condition, historical work orders, employee schedules, etc. Without the right technology and processes in place, accessing and combining these data sets can be a time-consuming and challenging task. A code-first approach could take several weeks or even months of planning and stakeholder engagement to acquire and merge the necessary data.

What do we need to deliver data science at Scale?

We know the steps data needs to move through, the next step is building a realistic picture of where we are as an organisation. We focus on 5 key aspects:

1. The organisation
2. The people
3. The process
4. The technology
5. The data asset

Key elements of a good data strategy

A good data strategy should focus on making data a first-class citizen within the organization. To achieve this, organizations need to understand the steps that data needs to go through in order to be transformed into valuable insights.

The next step is to identify the current state of the organization by evaluating five key aspects:

1. The organization
2. The people
3. The processes
4. The technology
5. The data assets

By analyzing these aspects, organizations can get a clear picture of their strengths and weaknesses and identify areas that need improvement.

Reaching the organization’s goals

To reach its goals, an organization must understand its current state, where it wants to be, and how to get there. A good data strategy should help organizations answer the following questions:

1. Where do we want to be?
2. Where are we now?
3. How do we get there? By answering these questions, organizations can develop a roadmap for achieving their goals, with a focus on making data a core asset for the organization.

Conclusion

A well-crafted data science strategy is crucial for organizations to stay competitive and extract valuable insights from their data. By embracing data as a first-class citizen, organizations can unlock its full potential and achieve their goals.

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