Published On: August 18th, 2020Categories: Data GuideBy 4.9 min read

Data collection and storage in the age of AI.

If a company wants to continually evolve and stay ahead of the competition, it must have a data and technology-centric approach. Today, data collection is increasing at an exponential pace and data is becoming a key strategic asset for businesses. But as we accelerate into a data-driven future, it’s becoming more and more challenging for businesses to get a grip over their data. Understanding how to utilize and unlock its true value is becoming increasingly complex. Data is not only growing in size but also in complexity. The amount of data sources and the mix of data types add to this growing complexity, leading to confusion for many companies.

But just how much data is being generated and gathered in 2020?

The numbers are truly astounding. At the end of 2017, an estimated 3.8 billion people around the world were using the internet. By the end of 2019, this figure stood at a whopping 4.6 billion. That’s an additional 800 million people online generating data every day. Every minute, 200 million emails are sent, 4.2 million Google searches are conducted, and 480 thousand Tweets are posted. In terms of the ‘Global Datasphere’ (the total amount of data generated), it’s estimated that by the end of 2019 it stood at 4.4 ZB, up from 2.7 ZB in 2017. To put this into perspective, one Zettabyte is equivalent to one billion Terabytes or one trillion Gigabytes[1].

Data collection & storage

Our methods of data collection and storage have also advanced in recent years. Huge amounts of business data are generated every day and most of it is now being stored. In the past, storage capacity was low and storing vast amounts of data was expensive. There was less desire to store data unless you had a direct use for it sometime in the immediate future. Today things are very different. With data regulations like GDPR, companies are much more conscious of how and where they store their data. Additionally, companies feel pressured to keep up with new technologies. In an effort to stay competitive, many companies feel compelled to try new technologies as soon as they become available. In the fast-paced digital world that we live in, a lack of innovation can see you left behind as your competition steamrolls ahead.

AI and ML are examples of this type of technological pressure companies are experiencing. The goal of these technologies is to use large data sets to uncover patterns and action new (and more efficient) ways of working. With good use of AI and ML, companies can become more cost-efficient, gain a better understanding of their data and make more informed and accurate decisions.

AI is only as powerful as your data

As for all systems that work with data, the result will never be better than the quality of the data you start with. And the quality of the data is not only about the actual quality of the input but also about using the correct data from the right data sources. To ensure that we are in control of our AI and Machine Learning output, we need to combine correct algorithms with correct data of the expected quality. This may seem like an obvious statement, but for the vast majority of businesses, ensuring data quality is a huge challenge.

Most often the output is based on a complex set of data sources. For example, data warehouses, data sets, transformations, multiple BI systems and applications, expressions and graphical views, etc. As we continue to use more systems and applications, this complexity continues to grow.

Outsourcing & data quality

Additionally, many businesses today are relying on outsourcing. These businesses are outsourcing their maintenance, development, and even control to external companies. This means that the company is not in full control of their data. Instead, they are trusting their external partners to control their data for them and often contracts are signed before trust is earned. You might be thinking “well this is how business works”. This might be true, but for data management, it’s worth thinking carefully about who you trust with your data as well as how much data they have access to. Data has the potential to transform the future of a company.

Data intelligence solutions

One effective way of regaining control of your data is to employ data intelligence software, such as NodeGraph. NodeGraph’s intuitive platform makes data sharing across an organization much simpler, helping to ensure that the right people understand the right data to deliver accurate results. Our platform offers:

  • Fully-automated metadata management 
  • End-to-end data lineage
  • Data cataloging
  • Unit and regression testing
  • Impact analysis
  • Data migration
  • & much more

Read the full guide

Access the full guide and learn about the importance of high-quality data and the harm that can come from poor quality data, as they are used in AI and ML.

The guide covers

An overview of the current data landscape
The relationship between AI and data quality
How bad data harms AI and ML
How to achieve high data quality
Data quality and ROI

Fill in the form to access the full guide.

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