Analytics has laid the groundwork for change in technology. It is on the verge of a revolution, but it will only reach that potential when it becomes transparent enough to integrate into users’ daily decision-making processes without substantial overhead in personnel or training.
In this report, we take a look at the ingredients that make a good analytics solution: visualization, self-service tools, and guided machine learning. We identify the strengths and weaknesses that each of these ingredients brings when used individually, and we show why combining these strengths into a new approach is essential for an efficacious, comprehensive solution.
Key findings of this report include:
- Self-service tools using visualization may have shortened the delivery cycle for data analysis and in certain cases allowed business users to take matters into their own hands. However, without proper backing, self-service analytics is a double-edged sword, as it provides a false sense of confidence that can lead to statistically unsupported conclusions.
- Some may see data scientists as the new rock stars with organizations putting a burden on their shoulders to deliver everything they hope their data analysis can be. But the all-encompassing notion of a data scientist is inaccurate. Real data scientists are in short supply, and placing such strong dependency on one person or a small team is a risky proposition for any business.
- Machine learning is a process that has been hard and expensive to configure and execute. Even experts have a hard time interpreting generated results. Business users and experts need more-comprehensible self-service machine learning.
Thumbnail image courtesy of: iStock/Thinkstock.
- Seeing is believing, but visualization is not analytics
- Why data science rock stars won’t scale
- Machine learning matters
- Approachable analytics: guidance and statistical integrity
- Key takeaways
- About George Anadiotis
- About BeyondCore
- About Gigaom Research