Going to work on a big data project can leave you wondering whether your organization is handling the job as effectively as possible. It’s wise to learn from some of the most common mistakes people make on these projects. Let’s look at 5 critical big data project mistakes and how you can avoid them.
Not Knowing How to Match Tools to Tasks
It’s tempting to want to deploy the most powerful resources available. This, however, can be problematic for a host of reasons. The potential mismatch between your team members’ skills and the tools you’re asking them to use is the most critical. For example, you don’t want to have your top business analyst struggling to figure out how to modify Python code.
The goal should always be to simplify projects by providing tools that match their skills well. If a learning curve is required, you’d much prefer to have non-technical analysts trying to figure out how to use a simpler tool. For example, if the only programming language choices are between Python and R, there’s no question you want the less technically inclined folks working with R.
Failing to Emphasize Data Quality
Nothing can wreck a big data project as quickly as poor quality. The worst of possible scenarios is that low-quality and poorly structured data is fed into the system at the collection phase, ends up being used to produce analysis, and makes its way into insights and visualizations.
There’s no such thing as being too thorough in filtering quality issues at every stage. You’ll need to keep an eye out for problems like:
- Misaligned columns and rows in sources
- Characters that were either scrubbed or altered during processing
- Out-of-date data that needs to be fetched again
- Poorly sourced data from unreliable vendors
- Data used outside of acceptable licensing terms
Data Collection without Real Analysis
It’s easy to assemble a collection of data without really putting it to work. A company can accumulate a fair amount of useful data without doing analysis, after all. For example, there is usually some value in collecting customer service data even if you never run a serious analysis on it.
If you don’t emphasize doing analysis, delivering sights and driving decision-making, though, you’re failing to capitalize on every available ounce of value from your data. You should be looking for:
- Patterns within the data
- Ways to benefit the end customer
- Insights to provide to decision-makers
- Suggestions that can be passed along
Most companies have logs of the activities of all of the users who visit their websites. Generally, these are only utilized to deal with security and performance problems after the fact. You can, however, use weblogs to identify UX failures, SEO problems, and response rates for email and social media marketing efforts.
Not Understanding How or Why to Use Metrics
The analysis necessarily noteworthy if it’s not tied to a set of meaningful and valuable metrics. In fact, you may need to run an analysis on the data you have available just to establish what your KPIs are. Fortunately, some tools can provide confidence intervals regarding which relationships in datasets are most likely to be relevant.
For example, a company may be looking at the daily unique users for a mobile app. Unfortunately, that company might end up missing unprincipled or inaccurate activity that causes inflation in those figures. It’s important in such a situation to look at metrics that draw straight lines to meaningful performance. Even if the numbers are legit, having a bunch of unprofitable users burning through your bandwidth is not contributing to the bottom line.
One of the best ways to recoup some of your team’s valuable time is to automate as much of the process as possible. While the machines will always require human supervision, you don’t want to see professionals spending large amounts of time handling mundane tasks like fetching and formatting data. Fortunately, machine learning tools can be quickly trained to handle jobs like formatting collected data. If at all possible, find a way to automate the time and attention intensive phases of projects.