As we move deeper into the 21st century, companies will not only be valued for the amount and quality of data they have available, but also for the algorithms they use to turn that data into actions or valuable insights. Leading organisations around the world already realise the importance of algorithms to automate processes and make operational decisions.
Two of the key advantages of algorithms are speed and scale. Already, there are many scenarios where algorithms are more cost-effective and efficient than an employee (human employee, that is). These advantages will only increase as organisations gather more data.
The digital age has made it much easier to capture data. Along with the development of excellent algorithms in a wide number of industries, and the many benefits they bring, organisations have been keen to jump on the algorithm bandwagon. But here’s what you should know first:
Implementing the first few algorithms should pose no problem to an organisation. Imagine a retailer with two stores. The CTO decides to implement an algorithm that will make it easier to forecast demand.
But because forecasting demand is usually done to help the supply chain understand the amount of clothing they’ll need to order, and because each store has its own set of historical data, a global estimate has no use for a supply chain manager who needs to order for a specific store. This means each separate store will require its own algorithm for accurate and granular demand forecasting.
But what if you have fifty stores? Or five hundred? The time it takes to design and redesign algorithms for each specific store skyrockets. This scenario isn’t limited to retail stores, but happens wherever there’s an entity with its own datasets.
A second point to keep in mind when a company implements more algorithms is that you have to maintain the quality and performance of each individual algorithm.
Although it’s tempting to believe an algorithm is a bunch of code that doesn’t change, the world around us does and new data comes in all the time. If algorithms aren’t retrained, this can lead to algorithmic outputs that are either inaccurate or useless.
Let’s imagine one of the stores in our retailer example. New apartments are built nearby and the town our store is located in sees a sudden influx of people. Sales go through the roof for our store. Happy days.
But how will our algorithm adjust? Will it notice that its output is no longer in line with reality? If so, will it autocorrect? The ideal scenario is the algorithm noticing the discrepancy between output and reality and adjusting automatically, but hardly any algorithm is that smart yet. What happens instead is that the algorithm will produce an output that is inaccurate.
So the unfortunate reality is that someone will need to check the output of the algorithm, compare it with reality and retrain the algorithm with the new data. It’s a cumbersome and time-consuming process, to say the least. Particularly if you consider this would need to happen for every algorithm in every store.
Although you might now think introducing algorithms in your organisation hardly sounds worth it, above points should not deter you from the many benefits that algorithms bring. What you need to remove these issues or in fact never have them at all is a platform.
This is where the Algorithm Factory comes in. It’s a single platform to train, run and manage your algorithms. Retrain your algorithms with new datasets automatically and easily compare performance between versions. Integrate the Algorithm Factory with your existing IT structure without disruption.
There’s more to it, but suffice to say the Algorithm Factory allows you to take advantage of the benefits of algorithms, scale them at your pace, and keep their complexity under control. It’s the modern way to move your organisation forward.
Want to know more about the Algorithm Factory and how we can help you manage your algorithms? Go to our demo page and book a demo today.