Every day, 2.5 quintillion new bytes of data are created. Every minute, 156 million emails are sent. 90% of the data available on the internet was produced in the last two years alone. We are entering the age of data chaos, and, while that means there is more useful material out there for big data analytics, it also spells difficulties for those trying to navigate the unfathomably high volume of unwanted data.
Obviously, we must adapt. But how?
Use semantic layers
Semantic layers, as the name suggests, are layers between a user and their data which translate the information into common, business-friendly terms.
“As the use of embedded analytics becomes more widespread, they make it so non-technical users don’t have to understand the complexity of the schemas, tables, and columns in data sources,” explains Good Data. “As a result, users can conduct ad-hoc data explorations in a way that makes sense to them, without having to dive into the underlying data structure or programming or data language.”
Basically, semantic layers make complex data more digestible for a layperson.
Utilise embedded analytics
Rather than using independent systems that collect various types of data from multiple sources, embedded analytics are integrated into the platform it needs to analyse. The resulting data is therefore more specialised and – though you may still need to use other sources to generate a broader understanding of it – less chaotic.
“While traditional BI has its place, the fact that BI applications and business process applications have entirely separate interfaces forces users to switch between multiple applications to derive insights and take action,” says Logi Analytics. “Instead, an embedded analytics platform puts intelligence inside the applications people use every day to improve the analytics experience and make users more productive by combining insight and action in the same application.”
Embrace big data technology
While it may be true that the total volume of data is increasing at an alarming rate, it is also fair to say that we have adapted technology that can process it. Artificial intelligence systems can utilise machine learning techniques to aggregate and analyse huge swathes of data, making them invaluable in a chaotic landscape.
“With Big Data becoming bigger with each passing minute, it is pertinent that companies learn how to sort and analyze big data,” says Eduonix. “Once, they learn how they can extract the useful information from the data, the rest not only becomes easy but also rather useful when making big decisions about your company or your products.”
This kind of tech doesn’t have to cost the earth, either. It doesn’t matter whether you’re a huge corporation or a tiny start-up, there will be BI and data management tools out there that suit your needs and budget.
Be prepared to evolve
It’s all very well and good to build a data model and rely on analytics programs – but to think that’s the end of the process would be foolish. We’ve already established that data is evolving, both in its volume and its applications, and so businesses must do the same.
The technology of today will be obsolete tomorrow, or next month, or next year. It is not enough to stick with one method of data analysis and hope for the best. Instead, it is necessary to keep up, to adapt, and to face data chaos with the attitude that it can be overcome.
Still not sure how to survive this brave new technological landscape? Here’s our advice in a nutshell: roll with the punches, utilise the latest technology, and prepare yourself for further changes. It might not be the easiest path to navigate – but taking those first steps is a much better option than getting lost in data chaos!