Working in cloud today has been extremely fascinating. We’re so far beyond traditional VMs and simple applications. In fact, new use-cases around cloud, data, and intelligent systems are absolutely inspiring to work with and design around.

Our constantly connected, digital world, is placing new demands around cloud and the enterprise is absolutely noticing. I wrote an article recently on InformationWeek that looked specifically at cloud migration. Again, more than just moving a VM or two, these migrations are on the fringe of what organizations are doing to leverage cloud for new kinds of workloads. For example, I discussed migrating more than 6 million lines of legacy RPG/Synon code into a service-oriented architecture compatible with cloud deployment. Similarly, I touched on working with vast amounts of data and how that would work in the cloud as well.

Today, we focus on the latter: dealing with data. Today, data has become the new oil of our generation. It’s extremely valuable, provides deep insights into market trends, and helps us create new initiatives in a digital economy.

A recent report from IDC indicates these key trends around data:

  • The evolution of data from business background to life-critical. Once siloed, remote, inaccessible, and mostly underutilized, data has become essential to our society and our individual lives. In fact, IDC estimates that by 2025, nearly 20% of the data in the global datasphere will be critical to our daily lives and nearly 10% of that will be hypercritical.
  • Embedded systems and the Internet of Things (IoT). As standalone analog devices give way to connected digital devices, the latter will generate vast amounts of data that will, in turn, allow us the chance to refine and improve our systems and processes in previously unimagined ways. Big Data and metadata (data about data) will eventually touch nearly every aspect of our lives—with profound consequences. By 2025, an average connected person anywhere in the world will interact with connected devices nearly 4,800 times per day—basically one interaction every 18 seconds.
  • Mobile and real-time data. Increasingly, data will need to be instantly available whenever and wherever anyone needs it. Industries around the world are undergoing “digital transformation” motivated by these requirements. By 2025, more than a quarter of data created in the global datasphere will be real time in nature, and real-time IoT data will make up more than 95% of this.

Finally, IDC pointed out that big data and business analytics (BDA) related software revenues will be $67.2 billion in 2019, with end-user query, reporting, and analysis tools ($13.6 billion) and relational data warehouse management tools ($12.1 billion) being the two largest software categories.

That being said, it’s important to understand how you can gather, quantify, and actually analyze this information. Let me give you a specific example. Coupled with solutions around data analytics and big data processing, data warehousing allows you to take valuable information to an entirely new level. Powerful data warehouse solutions help you create data visualization to make better decisions around your business and the market.

Today, a data warehouse helps with data ingestion and is a decision support system which stores historical data from across the organization, processes it, and makes it possible to use the data for critical business analysis, reports and dashboards.

That said, let’s ask the next question: where is your data warehouse located? There are more data sources, more streams of information, and more ways to process it all. This is a big reason why so many companies are now looking to the cloud to offload data requirements and even data warehousing.

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Major initiatives that I’ve seen heavily revolve around the utilization of data and the best possible ways to process it. This is why so many designs are now involving smarter and even automated data warehousing solutions. Let me give you an example. Let’s say you’re an Oracle shop that leverages an Oracle backend to do a lot of data processing. Well, now that both cloud and automation are on the table, how do you design around it?

Removing legacy data management components can be scary, costly, and time-consuming. However, new solutions are specifically designed to make all of this easier. Using the above scenario, many Oracle customers are now looking at new Oracle architectures to support evolving data requirements. Specifically, Oracle’s Autonomous Data Warehouse is designed from its core to be an easy-to-use, fully autonomous database that scales elastically, delivers fast query performance and requires no database administration. But here’s why this solution is so special. Built-in machine learning technology eliminates manual configuration errors to ensure reliability. In addition, unlimited concurrent access combined with advanced clustering technology enable businesses to grow data stores without any downtime. When it comes to cost and pricing, Oracle customers have fine-grained control of pre-configured compute and storage resources allowing for independent scaleup and down to avoid overpaying for expensive, unused, fixed blocks of cloud resources.

Pretty cool, right? Taking a step back here, you as an organization now have amazing options to work with vast sets of data. And, not just how you process and ingest all of it, but in the ways you’re managing your data sets as well. What Oracle is doing here is really special. They’ve basically designed the world’s first self-driving database cloud that’s architected to perform all routine database maintenance tasks such as patch, update, backup, without human intervention, all while the database is running. Coupled with machine learning, database migration tools, and enterprise-grade security, this type of autonomous data warehousing creates the foundation for the sustainable management of large amounts of data.

Here’s the really cool part. You can start taking all of this into the cloud. When it comes to cloud-based data loading, you can already integrate with fast, scalable data-loading from Oracle Object Store, AWS S3, or even on-premises. This means your cloud design can focus on the best possible use-cases around data management. Most of all, you can design around public, private, hybrid, and even multi-cloud architectures.

The scariest part is dealing with all of the data. But, none of this has to turn into a nightmare. The most successful data warehousing, and even cloud-driven data migration, projects that I’ve been a part of involve a holistic approach to data management. First of all, make sure there’s a good use-case. Then, it’s critical to involve all necessary stakeholders to ensure your project can scale. Finally, work with a good partner that can help navigate data design and cloud integration. Remember, leaders in the space are actively leveraging advanced data-driven solutions to impact the industry, their customers, and their competitive stance in the market. A great way to do this is to capture the value of data, and leverage the power of cloud and automation to make it all happen.

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