Data is king. Everyone knows that. Big Data has been on the buzzword most wanted list for a while now, along with machine learning, Hadoop and Spark.
Companies eagerly collect data and build massive pipelines to process that data in order to gain insight into what it all means. Guess what? Some companies are able to do that successfully for particular aspects of their operation. This typically happens when the company has:
- the ability to collect data on a massive scale and over a long timeframe
- data that is relatively stable
- a talented team of data scientists combined with domain expertise that can sift through the enormous piles of data and make informed decisions about what's relevant and what's not
- the know-how to configure and tune the huge big data/machine learning apparatus
- the knowledge to interpret the results and know how to tie the numbers to concrete steps or actions in the field.
This is extremely difficult.
A few companies transcend the hype and can actually demonstrate measurable improvements. Many others live in a world of illusion that their fate and strategic direction are optimized by hard data, when in practice they follow a false path. The problem is that there are no controls. No company can afford to split into two and compare its performance over time with two different approaches.
That said, big data, analysis and machine learning are amazing technologies that can really improve and scale a company. The trick is to do it right and that takes a lot of hard work. It gets easier and easier to hook up various data sources into big data pipelines, but the important parts are difficult to automate.
Data Analytics, machine learning, big data analysis