ETL (extract, transform and load) is one of the most important processes in big data analytics — and simultaneously, it can be one of its biggest bottlenecks. (For more on big data, check out 5 Helpful Big Data Courses You Can Take Online.)
The reason ETL is so important is that most data a business collects is not ready, in its raw form, for an analytics solution to digest. In order for an analytics solution to create insights, the raw data needs to be extracted from the application where it currently resides, transformed into a format that an analytics program can read, and then loaded into the analytics program itself.
This process is analogous to cooking. Your raw ingredients are your raw data. They need to be extracted (purchased from a store), transformed (cooked), and then loaded (plated), before they can be analyzed (tasted). The difficulty and expense can scale unpredictably — it’s easy to make mac n’ cheese for yourself, but much more difficult to create a gourmet menu for 40 people at a dinner party. Needless to say, a mistake at any point can make your meal indigestible.
ETL Creates Bottlenecks for Analytics
ETL is in some ways the bedrock of the analytics process, but it also has some drawbacks. First of all, it’s slow and computationally expensive. This means that businesses often prioritize only their most important data for analytics, and simply store the rest. This contributes to the fact that up to 99% of all business data goes unused for analytics purposes.
In addition, the ETL process is never certain. Errors within the ETL process can corrupt your data. For example, a brief network error may prevent data from being extracted. If your source data contains multiple file types, then they might get transformed incorrectly. Garbage in, garbage out, as they say — errors during the ETL process will almost certainly express themselves in terms of inaccurate analytics.
A corrupted ETL process can have bad consequences. Even in the best-case scenario, you’ll probably have to re-run the ETL, which means a delay of hours — and in the meantime, your decision-makers are impatient. In the worst-case scenario, you don’t notice the inaccurate analytics until you’ve begun to lose money and customers.
Streamlining ETL with Machine Learning and AI
You can — and probably do — assign someone to monitor ETL, but it’s honestly not that simple. Bad data can result from process errors that happen so quickly that they can’t be noticed in real time. The results of a corrupted ETL process often don’t look different from correctly loaded data. Even when errors are obvious, the problem that created the error may not be so easy to trace. (To learn more about analyzing data, see Job Role: Data Analyst.)
1. Detect and Alert Across ETL Metrics
Even though your data is a constantly moving picture, the ETL process should still produce consistent values at a consistent speed. When these things change, it’s cause for alarm. Humans can see big swings in the data and recognize errors, but machine learning can recognize subtler faults, faster. It’s possible for a machine learning system to offer real-time anomaly detection and alert the IT department directly, allowing them to pause the process and remedy the issue without having to discard hours of computational effort.
2. Pinpoint Specific Bottlenecks
Even if your results are accurate, they might still come out too slowly to be of use. Gartner says that 80% of insights derived from analytics will never be harnessed to create monetary value, and that may be because a business leader can’t see an insight in time to take advantage of it. Machine learning can tell you where your system is slowing down and provide you with answers — getting you better data, faster.
3. Quantify the Impact of Change Management
The systems that produce your data and analytics are not static — they constantly receive patches and upgrades. Sometimes, these affect the way that they produce or interpret data — leading to inaccurate results. Machine learning can flag results that have changed and trace them to the specific patched machine or application.
4. Reduce the Cost of Operations
Stalled analytics operations equal lost money. The time you spend figuring out not just how to solve the problem but also who is responsible for solving the problem is time you could be spending building value. Machine learning helps get to the heart of the matter by alerting only the teams that may be responsible for responding to specific kinds of incident, letting the rest of the IT department free to continue performing core job functions. In addition, machine learning will help eliminate false positives, reducing the overall number of alerts while increasing the granularity of information they can provide. Alert fatigue is very real, so this change will have a measurable impact on quality of life.
When it comes to winning in business, analytics is crucial. A landmark study from Bain Capital shows that companies employing analytics are more than twice as likely to overperform financially. ETL provides the foundation for success in this arena, but delays and errors can also prevent the success of an analytics program. Machine learning, therefore, becomes an invaluable tool for the success of any analytics program, helping to guarantee clean data and accurate results.