Human Augmented Analytics vs. Automated Analytics

Data analytics is essential in every industry as it helps businesses to make data-driven decisions. With the advancements in technology, there are two main types of data analytics available, human augmented analytics and automated analytics.

What is Human Augmented Analytics?

Human augmented analytics is when data analysts use technology to speed up their work and enhance the insights that they can offer. Simply put, human augmented analytics is technology that helps humans to analyze data. It's like having a side-kick, but not in the movies, in real life.

What is Automated Analytics?

Automated analytics use machine learning algorithms to analyze data. Automated analytics are great because they can analyze large volumes of data quicker than humans, and they can also identify patterns that humans may not be able to see. It’s like having a robot on your team, but less dangerous than in the movies.

Effectiveness

When it comes to effectiveness, both human augmented analytics and automated analytics have their strengths and weaknesses. Human augmented analytics excel in understanding the business context and setting the analysis objectives. Automated analytics, on the other hand, excel in processing large volumes of data.

For example, a human analyst can determine which data fields to analyze to better understand a customer's lifetime value. Once the customer analysis objectives are clear, automated analytics algorithms can be used to identify patterns among the fields.

Efficiency

Automated analytics algorithms can process large volumes of data quickly and efficiently. However, the algorithm's output is only as good as the data inputs, making human input necessary for proper analysis. Human analysts need to ensure that data sources are complete and accurate, whereas automated analytics can't verify or fill in data gaps.

Accuracy

While automated analytics can analyze data at a pace that humans can't keep up with, they are also prone to errors. Automated analytics operate on the principle of "garbage in, garbage out", making the input of accurate and complete data critical for precision output. Quality assurance and data cleaning are necessary pre-analytical steps for automated analytics.

In contrast, human analysts use their expertise and experience to make reliable analysis judgements that automated analytics might not see. There is no algorithm that could replace a human’s instinct or intuition, but the best results arise when algorithms and human judgment work together synergistically.

Conclusion

In conclusion, both human augmented analytics and automated analytics have their strengths and weaknesses. The best approach is to use both techniques to overcome any obstacles encountered in the analysis process. In other words, to fight against weird anomalies, punctuation typos, and data biases, it's best to rock 'n' roll with a human and a robot on your team.

References:

1. Polyzotis, N., Roy, S., Whang, S. E., Yakout, M., & Chatziantoniou, E. (2017). Data management challenges in Human Augmented Analytics. Proceedings of the 2017 ACM International Conference on Management of Data, 5-5.
2. Kohavi, R., & Provost, F. (1998). Glossary of terms. Machine Learning, 30(2-3), 271-274.
3. Polese, G., Di Donato, F., & Piantadosi, S. (2019). Automated analytics as a service. Expert Systems with Applications, 138, 112829.