Making an Impact

Data Science is not at all what I imagined it to be; it is so much bigger than that. Most people are just thinking of data analytics when they hear the word data science, but normally these data analytics are working with data that have already been cleaned. In real life, data is messy. It can have different formats. It can be stored in different data silos, and some data may be missing. A good data scientist knows how to cope with messy data and can still bring some results out of it. It is going to be tough, but the potential benefit from it all will be worth all of the effort.

When a company or an institution wants to be data-driven, data strategy is very important. It has to be aligned with the mandate or the objectives of the company or institution. Otherwise, you will just be wasting resources on something that will not be utilized in the end. Introducing something new can be very daunting, and people will only embrace it once they see how it could be helpful in their business practices.

Communication is the key in the entire process. The objectives of the project should be very clear from the start, so that expectations can be managed. You should be able to work with a team, and it will difficult to complete the project if there are arguments within the team. At the end of the day, the results of all the data analytics have to be communicated back to the client. If there is communication failure at any stage, the project will fail.

One must not also forget that all of these data-driven initiatives have potential impact on the client, and to society in general. Most data scientists are too excited about the potential benefits of the project, that they fail to think about the risks on data privacy and other ethical issues. If these issues are not addressed, the project will also fail, no matter how revolutionary it is.

Now, when I look back at the projects I’ve been involved with before, I now know why it didn’t have the impact that I imagined. I now have an idea what to do from now on.