Having big data capacities at a business or an organization isn’t an end in its own right. The goal is to produce projects that generate value for the organization, stakeholders, and ultimately the end customer.
At some point, however, everyone will struggle to come up with what their next move is as well as new projects to execute those ideas. Let’s explore 5 ways you can find inspiration for your next data project.
Look at What Competitors Are Doing
While there may be some things you’ll want to run past legal if you go into production with the results of your efforts here, looking at the ideas coming out of your competitors’ Big Data divisions is worthwhile. In addition to thinking about their work product, you should especially consider how they’ve accomplished certain goals. Sorting through the various possibilities of how they got a particular result may inspire you. Likewise, you might spot somewhere they went wrong or an opportunity to improve upon their analysis.
Cast a wide net when you’re looking for projects that competitors have done. Look for their:
- Blog articles
- Print publications
- Research papers
- Social media feeds
- GitHub repositories
- LinkedIn profiles
- White papers
- Industry reports
Revisit Existing Projects
There are many reasons to consider revisiting an existing project. NASA, for example, has been sorting through data that was gathered by the Voyager space probes in the 1980s to take advantage of technologies that didn’t exist at the time. You might find that advances in multicore processing power now make it possible to throw vastly more CPU and GPU cycles at a problem than you could have ever imagined five years ago.
Additionally, you may have access to updated data. Someone working in the financial sector, for example, would probably like to return to some of their projects since the 2020 stock market crash. There are often interesting opportunities to compare and contrast projections that you made in the past versus real-world outcomes. Focus on what you can learn as opposed to getting upset about what you might have missed.
Progress in Equipment
New equipment can be a game-changer as well. For example, single-board computers are more readily available, powerful, and cost-effective than they were a few years ago. If a project could benefit from the deployment of IoT sensors, for example, this might be the time to explore it.
Big Data work in agriculture is rapidly becoming dependent on IoT devices. There’s a lot to be said for dropping a few hundred sensors across several square miles to monitor soil chemistry, moisture, temperatures, and weather. What once would have been an unthinkably expensive operation that would require massive technical expertise can now be managed by a farmer with a laptop.
Sift Through Data Sources
You might not really see an idea that deserves to be studied until you swim by it. Looking around at the sites that cater to data enthusiasts, such as Kaggle and Data.gov. You might end up finding a dataset that sends your mind racing, and pretty soon you’ll be able to draw a line between the questions the data raises and how you know you can go about answering them.
Talk with Folks Who Know Nothing About Big Data
Living inside a data-centric bubble has its perks, but it can lead to tunnel vision. When you converse with people who aren’t immersed in the data world, listen to the problems they express interest in or frustration with. There aren’t many human endeavors where some benefit wouldn’t come from having better quality data. Doctors, artists, athletes, engineers, and many more all have puzzles they wish to be solved.
Keep a notepad and pen on you at all times so you can scribble down ideas when you encounter them in the wild. If you don’t have the person’s contact information, ask for it so you can do a follow-up, if necessary.
People often assume that inspiration just falls from the sky. It doesn’t, it demands ample amounts of thought and focus. Creators and innovators have processes such as these, putting these strategies to work is when they can help you find that next big idea for your big data project.