Updated: Jan 13
It is estimated that an incredible 2.5 quintillion bytes of data are being created every day. In fact, 90% of the world's data has been created in the last two years alone. This means that this figure will only continue to rise, with the expectation that the volume of data is to double every two years. With this in mind, it’s impossible to underestimate the importance of a strong data infrastructure. Data is king, and vitally important to any company, as without easy access to it, it becomes much more difficult to make insightful and effective decisions. The kind of decisions that companies need to take to increase revenue, reduce risk or raise productivity.
To give a quick crash course, data infrastructure is the entire backend computing support system required to process, store, transfer, safeguard and consume data. In essence, it is the digital structure which supports business data driven operations. Once you have a strong infrastructure in place, you will be able to more readily understand your data and deploy AI to hit those key KPI’s or drive positive change.
While a good data infrastructure can support your business, the opposite is also true – a bad or underdeveloped data infrastructure can have a negative impact. An anecdote I like to use comes from a discussion I had with a company in the banking sector. For years, this company had different departments creating, analysing and building datasets on the same accounts independently. This meant that there were hundreds of various versions of the same data, the duplicate accounts and the same customers all scattered across different departments. This duplicated data caused more problems than just lost server space, it also led to confusion and made it impossible to understand what was really going on in the business. It became a twelve-month project just to right the ship and get a good view of what normal was, let alone what was required to deploy AI effectively on top of it. There are other ways to cause problems for yourself, for example if data managers do not provide clean and adequately structured data. The noise found on this data can throw off predictive models and bias the new data used by that model to make future decisions. This comes firmly under the old age adage “junk in junk out”, for which there’s a whole market devoted to tools that can help with this.
Now that you understand its importance, what are the key components to building a strong data infrastructure?
1. Set out a clear list of defined goals you need the data to achieve, so that your strategy and infrastructure can match. You also need to educate your employees so they can support that strategy.
2. Use the best bedrock technologies, technologies like Spark, Kubernetes, Kafka etc… Make sure the tools you use are flexible and guarantee performance as your business scales.
3. Whatever tool or product you’re using, it is essential that it be readily deployable with any common IT infrastructure. Any platform needs to comply with modern DevSecOps practices.
4. Your data needs to be clean, which means you either need to be collecting clean data or have the ability to clean it before you use it. The ability to merge data together is also important when it comes to creating machine learning models. These two functions can be done manually but are time consuming and are not the best use of a data scientists time!
5. Invest in open information sharing to centralise your data and focus on permissions rather than silos. Avoid the common pitfalls of duplicating data sets and storing data in repositories across the business.
6. This next one maybe goes without saying but invest in security!
7. Finally, constantly assess and check performance against those goals you’ve set.
I hope that this blog has been helpful and given some food for thought. A strong data infrastructure is a key to making successful decisions and finding useful insights from your data. By making sure to have these levels of safeguarding, you are able to have the most efficient access to your data, as well as laying the groundwork to use AI to drive business performance. Our AI platforms have been designed with these seven tips in mind.
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