There is already an incredible amount of data in the world, and it only continues to grow. IDC predicts that the total amount of digital data created worldwide will rise by 163 zettabytes in the next four years alone. To mine this vast array of information for critical insights, companies in the public and private sectors are bulking up their data science investments and looking for ways to strengthen their existing teams.
With that in mind, it should come as no surprise that Forrester recently published a report, “The Tech Executive’s Primer on Data Science, Machine Learning, And AI,” to help IT leaders get up to speed. Below are five of the most critical things the firm thinks you should know about data science:
1. If it wows like in the movies, it’s probably not AI. As the adage goes, “If it looks too good to be true, it probably is.” When it comes to artificial intelligence (AI), products that can actually deliver on their promise have limited intelligence and autonomy. That’s not to say that we won’t see a highly sophisticated, Hollywood-like bot in the future, but it’s certainly not where the technology is today.
2. Worry first about humans. An algorithm is only as good as the data it’s supplied. Because humans are the ones supplying this information, it’s important to focus on the people component when developing models. We’ve written about ethical AI concerns in previous APEX of Innovation posts, but social issues such as race and gender aren’t the only biases that can negatively impact data sets. Tech leaders must be aware of these concerns, ensure that data is screened appropriately, and see that models are validated and tested.
3. Improve data over time. Don’t delay starting a project until every data element is perfect. Working with the dataset enables you to understand what information you need and in what form. This same advice can also be applied to your algorithms.
4. Choose projects you can actually implement and measure. Don’t start anything unless you can state what you want to achieve, what system and processes you will use to achieve it, and how you intend to measure your progress.
5. Always remember the users. You could have the best data, best algorithm, and best system, but if the users don’t know or don’t care about using it, it’s ultimately not going to do much for your business. Involve users from the start and make sure that tools designed for knowledge workers are intuitive and accessible in order to accelerate data-driven insights.
For additional data science tips to be mindful of, take a look at this TechReport article.