Putting big data systems to work across varying companies and industries all have one thing in common, almost all forms of big data work end up being time-demanding. This cuts into productivity in many ways, with the most obvious being that less time can be allocated towards analysis.
To address the problem, the first step is to identify the varieties of time killers that often occur during these projects. Let’s take a look at four of the most significant as well as solutions to avoid them.
Data Acquisition and Preparation
One of the most easily recognized time killers is the effort that goes into simply collecting data and preparing it for use. This occurs for a host of reasons, including:
- Difficulty finding reliable sources
- Inability to license data
- Poorly formatted information
- The need for redundancies in checking the data
- The processing time required to go through massive datasets
Solutions run the gamut from paying third parties for data to creating machine learning systems that can handle prep work. Every solution has an upfront cost in terms of either money or time, but the investment can pay off generously if you’re going to reuse the same systems well into the future.
Lack of Coordination
Another problem is that lack of coordination can lead to various parties within a company repeating the same efforts without knowing it. If an organization lacks a well-curated data lake, someone in another division might not realize they could have easily acquired the necessary information from an existing source. Not only does this cost time, but it can become expensive as storage requirements are needlessly doubled.
Similarly, people often forget to contribute to archives and data lakes when they wrap projects up. You can have the most advanced system in the world, but it means nothing if the culture in your company doesn’t emphasize the importance of cataloging datasets and making them available for future use.
Not Knowing How to Use the Analytics Tools
Even the best of data scientists will find themselves picking and sticking to get a system to work. Some of this issue is inherent to the job, as data science tends to reward curious people who are self-taught and forward-thinking. Unfortunately, this is time spent on work that a company shouldn’t be paying for.
Likewise, a lack of training can lead to inefficient practices. If you’ve ever used a computer program for years only to learn that there was a shortcut for doing something you had handled repeatedly over that time, you know the feeling. This wasted time adds up and can become considerable in the long run.
Here, the solution is simple. The upfront cost of training is necessary to shorten the learning curve. A company should establish standards and practices for using analytics tools, and there should be at least one person dedicated to passing on this knowledge through classes, seminars, and other training sessions.
Poorly Written Requirements for Projects
When someone sits down with the project requirements, they tend to try to gloss over the broad strokes, identify problem areas, and then get to work. A poorly written document can leave people wondering for weeks before they even figure out what’s wrong. In the best-case scenario, they come back to you and address the issue. In the worst-case scenario, they never catch the issue and it eventually ends up skewing the final work product.
Requirements should include specifics like:
- Which tools should be used
- Preferred data sources
- Limits on the scope of analysis
- Details regarding must-have features
It’s always better to go overboard with instructions and requirements than to not provide enough specifics.
It’s easy during a big data project to get focused on collecting sources, processing data, and producing analysis. How you and your team members go about doing these things is, though, just as important as handling them. Every business should have processes in place for weeding out the time killers in projects and ultimately making them more streamlined. This may include project reviews such as when team members are prompted to state what issues they encountered. By taking this approach, you can reduce the amount of time spent on mundane tasks and increase the amount of work that goes into analysis and reporting.