The data landscape continues to evolve and with that the need to stay up to date has never been more crucial. A lot of companies risk falling behind competitors if they don’t properly invest in harnessing the power of their data. In this article, we’ll take a look at some common problems that can prevent you from being in the position to get more from your data.
Data quality
The age-old expression ‘you get out what you put in’ has never been truer in the context of data. Obviously, the best approach is to ensure your data is of the highest quality possible when it is created or initially sourced, and there are many simple steps to take to achieve this. If this isn’t possible, you will need processes in place to clean, transform and model your data. Neglect this prerequisite and the value of your data is greatly diminished. When companies try to adopt Machine Learning models without paying close attention to data quality, it often yields disappointing or confusing results. Ensuring that you have taken steps to ensure you have high quality data, will set you up to yield outstanding results.
Data Infrastructure
When becoming ready to harness the potential of Machine Learning, a lot of companies overlook the need to have a stable data infrastructure set up before investing in ML. Ensuring that your data architecture is appropriately thought out and implemented is a crucial prerequisite to deploying ML resources. As algorithms are often computationally heavy, your business needs to have the correct technology in place to ensure it can handle the data processing required. It’s also important to make sure that your data infrastructure is scalable to allow more data sources to be added easily, allowing you to continually increase the power of your ML capability.
Skills Gap
As with any new technology, there is lag between the demand of people who want to use and the supply of those able to provide it. Machine Learning is especially unique due to the nuanced nature of data, meaning ‘one size fits all’ solutions are often either not cost-effective or simply just not fit for purpose. That’s why it’s important to employ highly skilled technicians who can correctly implement ML models which provide real competitive advantage. As the heading suggests, the number of professionals who have the skills to be able to do that is severely limited and the number of companies who require the skills is growing rapidly. Therefore, there is a growing need to source these valuable skills, either in house or externally.
Badly Defined Measurables
At Keri Analytics, a phrase we often refer to customers is ‘you get what you measure’. If you haven’t accurately defined your measurables you’re not going to get a true representation of what you want to see. This is amplified when you begin to engage in Machine Learning, without clearly defined metrics, you aren’t going to get any valuable insights. It’s extremely important that before transitioning to predictive analytics, a stable foundation of understanding your landscape is developed.
Unknown return on Investment
Investing in Machine Learning and data products in general is often a costly and time consuming process. It’s difficult to justify the costs to senior executives when you aren’t able to quantify the benefits. That’s why it’s important to clearly define your strategy and take time to understand how you’re able to benefit from adopting ML. It’s also beneficial, to identify any quick wins that you can gain through improving your capability, this can be the foundation to longer term investment and greater benefits to your organisation.
If any of these issues sound like something you can relate to or something you would like to understand more about, please contact us at enquiries@kerianalyics.com for a free consultation on your data landscape.