A decision-making strategy is designed to set long-term direction for a company. The use of Big Data and data science to aid in making these decisions has become more important day by day. However, thorough research is required to understand how to use data in different contexts to avoid making bad decisions by trusting only raw information.
Decision-making has long been an esteemed subject among businessmen and entrepreneurs. Because of the growth of Big Data over the last years, there’s no wonder that data-driven decision-making is a promising application of data science.
Data-driven decision-making can be defined as the practice of basing decisions on the analysis of data instead of using mere intuition or gut-feelings. As the old addage goes, humans are stupid decision makers. The discussion surrounding this concept has to do to mainly with the issue of understanding the limits of technology. When can decisions be made entirely by an automated process and when they require human intervention?
Outside automatic operating decisions, there are many circumstances where human involvement is necessary for a variety of reasons, among them, to avoid making a bad business choice (and end up in bankruptcy!).
Forbes recently published an article by Daniel Graham that explains how to make bad data-driven decisions in three steps. The goal is, of course, avoid the following scenarios:
- Use bad data. If you get the right data, it will be more likely to get the right answers. So, think about this as a first step: you need to be sure that the data your business uses is clean, fresh, up-to-date, and enough. Data analysts should look for missing values, typos, and duplicates in order to avoid bad results.
- Don’t question the methods. Data scientists need insights from the business they are working for in order to design great analytics. If they are left to work on their own without any guidance, a data scientist will let the data take him in a direction that might end in a bad business decision.
- Use spreadsheets all the time, whenever you can. If you fashion yourself a data-driven organization, then one of the first steps you need to take is to have a strong data plan with proper data storage. Relying on spreadsheets for data collection will ensure chaos. Even if it seems like everyone uses spreadsheets, they are jam-packed with errors. Always question and triple check data that was entered manually.
Trading bots exchange a billion shares a day on Nasdaq. Online advertisers bid on hundreds of thousands of keywords a minute, in deals based almost entirely on optimization models. Nowadays, the amount of data and the speed and volume required for specific transactions are too much to handle by humans alone. But, yes, there’s risk in letting mere data make most of the decisions. The sweet spot is avoiding the dangers of gut instinct as well as the downsides of blindly obeying what our algorithms dictate.
