MIT SMR Connections
MIT SMR Connections is the custom content creation unit within MIT Sloan Management Review.
Machine learning is taking predictive analytics to the next level to drive tangible business value for a wide array of industries. But machine learning models are only as good as the data they ingest, and bad data can lead to difficulties such as inaccurate insights or inherent bias — factors that can hamper intelligent business decision-making.
This guide suggests four steps business leaders can take to establish the policies and frameworks that will enable their organizations to leverage their data to derive value from machine learning.
1. Align machine learning initiatives with business priorities. Establishing which of a business’s strategic priorities have the best potential to be advanced through machine learning provides clarity around which data sets are most important to collect, store, and prepare for analysis.
2. Create and maintain a comprehensive view of all data assets. Challenges like legacy systems, mergers and acquisitions, and poor data onboarding practices can create silos of unidentified and untagged information. But for data to be useful, a business must maintain a comprehensive view of what data it has and how it is stored.
3. Lay the groundwork for data governance. At the core of every data management strategy is a set of rules and systems that ensures that data is secure, handled in compliance with applicable regulations, accessible, and useable.
4. Identify the specific roles required to build a strong data foundation for machine learning. A well-thought-out organizational structure can clarify roles and responsibilities within a machine learning practice.
Download the full guide, which includes a data management strategy checklist, to begin developing an action plan for unlocking the power of data through machine learning.