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Data is everywhere and might, if analysed properly, give you some valuable insights you never knew were possible.
Everything is generating data nowadays. From the path a user takes while browsing your website, to the video conference you attended last night, and the email you received this morning. Data is everywhere and might, if analysed properly, give you some valuable insights you never knew were possible.
But how do you cope with the sheer volume of data? The collective sum of the world's data is said to grow from 40 zettabytes this year, to a mind-boggling 175 zettabytes in 2025. To make this more comprehensible; one zettabyte equals one billion terabytes.
What’s more is that more than 90% of all data was generated in the last two years. This is because of the tremendous increase in the number of active IoT devices in the world such as smart cameras, speakers and sensors.
It’s not easy to handle large volumes of data if it’s not your core business. For starters, it’s impossible to analyse and process these volumes in person. So automated analytical tools are crucial to gain insights into your data.
Knowing how to process your unstructured data digitally can deliver a serious competitive advantage. To protect and work with this type of data in the long term, businesses need to be able to assess, classify and apply policies to it on a large scale. Data analysts need to groom the unstructured data so it can work hand-in-hand with other types of data. If implemented correctly, you can save on storage space because you can delete much of the data immediately. Because the data is enriched with metadata, thanks to automatic analyses, it is also much easier to retrieve, which improves efficiency.
So, which tools do you need to analyse your data automatically? Machine Learning (ML) is the key to analysing your data, using algorithms to learn and improve the process for your company, without the need for explicit programming. Now that cloud computing is accessible for almost every company, high levels of processing power and almost unlimited scale are within everyone’s reach at reasonable prices.
Not every company has access to the skills needed in-house. One of the biggest pitfalls is proceeding with digital transformation while your workforce either lacks essential skills or resists the required change In our whitepaper, we outline why 8 of the top 10 challenges from capitalising on your data are cultural, not technical.
If your company is serious about digital transformation, it will probably appoint a Chief Data Officer (CDO). The CDO can play a crucial role in changing the culture and teaching everyone how to work with data. More knowledge will address the mentality that compliance and privacy are somehow incompatible with leveraging your data.
Digital transformation cannot be achieved with one stroke. Data is spread over multiple cloud providers, and the most challenging aspect will be to orchestrate, tag and harmonise data across all these services. Data volumes keep growing, and the process of accessing and preparing data becomes more specialised every day.
When analogue processes are digitised, they generate data that wasn't there before. In time better data quality, fresher data, better access and reduced risk exposure create a virtuous cycle of improvement, which helps your company to innovate even further. You can see that in the virtuous cycle in the infographic above.
Tapping into Machine Learning tools like Iron Mountain Insight will allow you to capture, classify, index, enrich and visualise both physical and digital data. It helps companies to uncover new revenue streams, opportunities and cost savings. It’s a cloud-native Services Platform that uses the full range of Google's AI capabilities on a subscription basis.
Want to know what this could mean for your company? Download our latest whitepaper for valuable insights.