MIT SMR Connections
MIT SMR Connections is the custom content creation unit within MIT Sloan Management Review.
AI is making headlines—and not just in futuristic technologies like self-driving cars. It’s transforming business processes in established industries, from retail to financial services to manufacturing.
Not surprisingly, business and IT leaders ask about the best way to adopt AI for their organization. The answer is consistent: anchor your approach in the fundamentals. The best path to AI and applying the breakthroughs in machine learning is to establish a foundation in capturing, preparing, and analyzing data.
However, nobody said establishing a foundation is easy. Data scientists spend up to 80% of their time on data wrangling, data munging, and data janitor work before the predictive capabilities promised by AI can be realized. This critical step of capturing, preparing, and analyzing data creates the foundation for successful AI initiatives. To help leaders create this virtuous cycle, Google Cloud has prepared A CIO’s Guide to Data Analytics & Machine Learning that outlines key enabling technologies and how managed cloud services greatly simplify the journey—regardless of an organization’s maturity in handling Big Data.
Building on new research and Google’s own contributions to Big Data, the guide walks readers through each step in the data management cycle, illustrating what’s possible with concrete examples. It’s designed to help business and IT leaders address some of the essential questions companies face in modernizing data strategy:
- For my most important business processes, how can I capture raw data to ensure a proper foundation for future business questions? How can I do this cost-effectively?
- What about unstructured data outside of my operational/transactional databases: raw files, documents, images, system logs, chat and support transcripts, social media?
- How can I tap the same base of raw data I’ve collected to quickly get answers as new business questions arise?
- Rather than analyzing historical data in batch, what about processes where I need a real-time view of the business? How can I easily handle data streaming in real time?
- How can I unify the scattered silos of data across my organization to provide a current, end-to-end view? What about data stored off-premises in the multiple cloud and SaaS providers I work with?
- How can I disseminate this capability across my organization—especially to business users, not just developers and data scientists?
Wherever your company is on its path to data maturity, Google Cloud is here to help. Download the CIO’s Guide to Data Analytics and Machine Learning here to unlock the transformational potential of your data.