The Database of Tomorrow: The Self-Driving, Autonomous Database

This article is sponsored by Oracle – redefining data management with the world’s first autonomous database.

In the coming years, the amount of data we create worldwide will grow to 175 zettabytes of data per year by 2025, up from 33 zettabytes in 2018. Over half of this data will be created by the Internet of Things devices and over 60% of it will be enterprise data. By 2025, 30% of all the data created will be in real-time, offering organisations great opportunities to constantly optimise their business.

Clearly, the organisation of tomorrow is a data organisation. However, simply collecting vast amounts of data is not enough. You would also need to analyse the data for insights and change your organisational culture to benefit from it. According to McKinsey, data-driven organisations are 23x more likely to acquire customers, 6x more likely to retain customers and 19x more likely to be profitable. Being data-driven is good for business.

The Importance of Data Governance

When collecting petabytes of data, it becomes vital that this data is of high-quality. Organisations that focus on high-quality data are better able to deal with changing business environments and achieve strategic objectives. As such, in today’s data-driven world, data governance has become a necessity for organisations, and they cannot get away anymore with minimal effort.

Instead, organisations need to rethink how information is created, audited and managed, how ownership is defined and how data is kept private and secure. Organisations require a data governance framework that combines business and technical perspectives to respond to strategic and operational challenges involved with data. Such a framework consists of five domains, including data principles, data quality, metadata, data access and data lifecycle.

Data principles define how business users can manage and deal with the data available. Data quality refers to the accuracy, timeliness, credibility and completeness of the data. Metadata is defined as “data about data”, provides a description of data and facilitates the understanding of data. Data access determines who has access to what data within the organisation and data life cycle is about how data is used, stored and organised over time.

Managing your Data Life Cycle

The data life cycle is the sequence of steps a piece of data moves through from collecting to deletion. It consists of data capture (for example using IoT devices), maintenance (data processing before it can be used), active use (the data is used to support an organisation’s activity such as personalised marketing), publication (sending data outside the organisation, for example sharing it with industry partners using a blockchain solution), archiving (when data is no longer needed, it is archived instead of deleted, for future use) and finally purging (GDPR-compliant deletion of data that is no longer required).

Optimizing your data throughout its life cycle is vital for the organisation of tomorrow. Only high-quality data, that complies with a data governance framework, and is monitored throughout its life cycle can be useful to an organisation. Early database systems only took care of some steps of the life cycle (such as data capture and maintenance), but modern systems take care of the full data life cycle.

With the volume of data growing, database systems become more important. Whether you store data in the cloud, on-premises or using a hybrid cloud is only part of the story. How you manage your data and your databases becomes increasingly important, and difficult, with the growth in data.

The Autonomous Database

Fortunately, modern database systems do a lot more than allowing you to store and maintain your data. We have now reached the era of the autonomous database, where artificial intelligence takes over the role of database engineer. Autonomous databases use machine learning and automation to take care of the hard work, while the engineer can focus on more important work. As a result, your data will be better managed and becomes more useful.

The autonomous database uses machine learning to automatically upgrade, patch and tune itself while running. Security updates are automated with no downtime required. Machine learning is used to make sure the database consumes less compute and storage thanks to automatic compression of data. The result is a faster, more reliable and cheaper database than the traditional database.

One of the organisations at the forefront of this development is Oracle. The Oracle Autonomous Database, the world’s first autonomous data management in the cloud, offers total automation based on machine learning and eliminates human labour, human error, and manual tuning —including performing all routine database maintenance tasks while the system is running—without human intervention. This new autonomous database is self-driving

Using automation and machine learning, the autonomous database is self-driving, freeing IT staff to support the business. It is self-securing as it autonomously protects the database from attacks and self-repairing, keeping your business continuously up and running.

As such, it maximises the value you can retrieve from your data. It allows you to create new data models, drive new insights and do all this faster than ever before. Consequently, intelligent automation allows organisations to better monetise the data at hand; a 10% increase in data accessibility can result in $65 million extra income for a typical Fortune 1000 company.

The Database of Tomorrow

The organisation of today can already benefit from the database of tomorrow. An autonomous database is a giant leap forward from the traditional, hard-work databases where a batch of engineers is required to keep it up and running and secure. Especially with data becoming the key ingredient for remaining competitive, it is vital for organisations to ensure the maximum output of their data. That is only possible when data can be put to work, hackers don’t stand a chance and your database is up and running all the time.

With automation and machine learning making its way into the organisation, it only makes sense to also use AI in your database. After all, also in data management, AI is much better at eliminating complexity, human error and ensuring security than we humans are.

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