Three key data cleansing strategies from our upcoming Ebook
Many enterprises have built their data house on sand and are struggling to get the results they were looking for as a result.
From our own experience with building a clean data practice at Andela and for our partners, we’ve produced a soon-to-be-released ebook offering strategies to help data leaders shore up the foundation of their data houses. Ahead of the launch, we’re sharing three data cleansing best practices from our full guide.
Plan data strategy vision
To become a data-driven organization, you first need to envision what that will look like at a high level. Defining specific use cases you actually want your data for and then reverse-engineering your data estate from there. It’s a mistake to first build your data warehouse, data lake, modern data platform, data fabric or data mesh and then think about your data governance. In a 2021 Survey by KDNuggets, the majority of companies reported less than 20% of their Machine Learning models get deployed.
Most organizations are lagging when it comes to supporting modern use cases. Modern use-case-driven platforms help deliver an end-to-end platform to support a specific use case such as IoT analytics, edge applications, fraud detection, global applications, customer 360, and microservices applications.— Forrester, The Future of Data Management
Create a single source of truth
Ultimately, what you want is for there to be a single source of data truth for everyone in your business. This requires data that is trustworthy, high-quality, and highly-discoverable.
This doesn’t necessarily mean a centralized data lake or warehouse. It means that the foundation of your data house has got to be an absolutely rock-solid process for getting data in, making it suitable for your needs, and then making it available to the business.
By making each team in your business obliged to make their data available in a standardized way, you can create highly-available streams of consistent and trustworthy data.
This means that users can rely on your data without having to check with other teams that it’s up-to-date, implement laborious transformations or create copies of the dataset for their own use.
Develop an actionable data roadmap
You need to decide what data you need to provide the right insights and domains for your organization. Then you want to look at the data you have available and decide how that needs to be modeled in order to meet that need.
Ideally, you would be able to create a global governance process that sets clear guidance for data producers across your business so that the data they produce is already in alignment with what you need and instantly usable by others from across the business.
This can be done at different levels. For example, at the highest level, you could set global data governance standards that ensure interoperability of data across your organization. At a lower level, a data modeler might tweak some aspects of the data on the ground to meet the very specific needs of a given business unit or domain.
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