AI and machine learning will dominate the CIO agenda this year
We are experiencing a data revolution. Data has become the engine of the global economy and it’s growing like never before. Our relationships, our jobs, our health, and our security all depend on it.
Some industry experts say over 90 percent of the world’s data was generated in the last two years alone. By 2023, enterprises, machines, industries, consumers, science and more will be generating 103 zettabytes per year, according to IDC.
Now more than ever, organizations realize that to manage this unprecedented data growth, a solid data strategy must be front and center for them to survive and thrive. This includes thinking about where your company’s data should reside and how to incorporate the latest innovations to harness that data and turn it into valuable insights.
As the industry moves toward new innovations, considers new architectures, and strives to work together on open standards, here are a few of the top trends to keep in mind for 2020:
In 2020, we’ll see more standardization in the AI/ML ecosystem that makes it easier to integrate and deploy solutions at the edge.
Analytics are the competitive advantage – companies that aren’t investing in analytics by 2020 probably won’t be in business in 2021. There is just too much company data that can be collected, processed, and then turned into insights. Those that do not take full advantage of modern analytic tools in this space will suffer.
With this, the new connected world has more and more of the workloads moving to the edge, increasing the need to ensure these very small edge devices have the capability to run and analyze increasing large amounts of data. Due to the small footprint and the rapid deployment needs, we’ll need to have more standardization and interoperability. This will take the form of more open architecture, open standards, open messaging and more.
In 2020 and beyond, companies will begin automating data scientist roles for ML.
There are simply not enough data scientists in the world to support the growth of ML workloads. Companies are now developing ways to put the power of ML into the hands of software engineers and/or business subject matter experts.
New off-the-shelf tools will be able to fulfill the baseline role of the data scientist, and true data scientist roles will shift to higher-level value-add such as fine-tuning ML for specialty use-case work. In 3-5 years, ML automation will become the norm, and companies will have more tools at their disposal to empower data scientist personnel to be more efficient and agile.
In 2020, AI/ML will help drive the adoption of “auto” everything.
In IT, the quest for greater efficiency is never-ending, and automation is essential in succeeding. From database tasks to manufacturing to cars to customer experiences and self-service business functions, virtually anything and everything that can be automated, will be.
New AI/ML models and insights, leveraging the convergence of multiple data types, will be the key enablers of automation. In 2020, organizations will continue to implement AI/ML in order to unlock the power of automation to drive efficiency and increase productivity. The result? Reduced costs across the value-chain.
In 2020, the number of local and regional data centers, globally, will grow.
Cloud adoption is not slowing; however, two factors are driving the growth of localized data centers. The first is data compliance. As multiple countries seek to have or have already passed data localization laws, organizations will need to keep their data closer to understand and mitigate potential security and privacy risks associated with data compliance.
The second is cloud repatriation. In essence, larger enterprises are looking to own their data and rent the cloud for better costs and controls, including security, latency, and data access. These larger enterprises will use the cloud more selectively for specific burst applications, use cases and projects.
Organizations who plan ahead with a comprehensive data strategy and future looking infrastructure will be well positioned to stand the test of time. Those who treat data as a strategic asset will have an economic and competitive advantage in the marketplace of today, and tomorrow.