Michael Alp, Vice President, Pure Storage Asia Pacific and Japan
Every CEO is aware of the value of data, but the truth is, not many have been making full use of what they have for actionable customer insights that could help them identify new opportunities. The root of the problem is usually that businesses have simply been complacent, not moving – or moving fast enough – to catch up with the pace with which data has been evolving. Data, both in terms of volume and the rate it expands, is growing – and it’s not showing any signs of slowing down. The good news is there’s always time to catch up.
In the very near future, we can expect to see a massive data boom, thanks to Internet of Things (IoT). Domestic application of IoT devices will become ubiquitous, and the datasets required to sustain them will only get bigger. However, at present, the challenge of collecting the data from thousands or millions of data sources and moving that data to the compute elements is proving to be tremendously difficult given the existing network capabilities.
And this is only the beginning – over the next decade, the business environment will almost be unrecognizable. Everything that can be connected will bring greater efficiency to business processes, but the data involved in sustaining this will be enormous and it will feed a whole host of new artificial intelligence (AI) based applications and services.
For a long time, AI felt a nebulous concept. Now we’re seeing examples of machine learning in many aspects of our daily lives, and we will only become more accustomed to these technologies as chatbots and AI assistants become mainstream consumer technologies.
In the very near future, we can expect to see a massive data boom, thanks to Internet of Things (IoT)
All the top tech players have already been exploring AI for long. Google, for instance, has invested considerable amounts of money and hours into developing its AI assistant, which is now powering the devices on its Android platform. In time, all the pools of data that are stored in these companies’ storage will eventually be turned into the fuel for machine intelligence.
But AI brings with it a dichotomy for those looking to invest. On one hand, the potential of AI to address macro challenges in critical areas such as healthcare and genomics is immense. Sooner or later AI will take over mundane tasks and provide us with creative insights, benefiting every sector from banking to manufacturing.
On the other hand, it requires seriously strong infrastructures to underpin the innovation. Take the automotive industry
– making autonomous cars a mainstream part of our lives will be a data-heavy task. Cars will need to be fed vast amounts of data to operate efficiently and safely. This industry will become a pool of data that needs to be managed.
Regardless of industry, the useful processing of data, either contemporary or historical, can add value to the bottom line as useful information is extracted and used to compete more effectively, innovate more rapidly, improve customer engagement and so on.
When it comes to processing data, a simple, scalable, high-performance data center environment is already one of the most powerful strategic assets any firm can possess, and that’s not going to change in the future. But as we’re moving to the cloud-era it brings with it a new set of demands for storage, with a requirement for a high degree of performance at scale.
Businesses need to act fast, so cloud-era flash storage can handle data at speed, as high latency and data bottlenecks are going to have a negative impact across the business. A cloud-era solution therefore focuses on exponentially expanding the value of data through faster and more reliable access. This enables businesses to build a new class of applications to extract new insights from data and leverage technology developments such as AI.
As we move into our AI defined data rich world, it’s critical that organizations start considering how they will be able to support their AI and ML systems to draw out those insights. After all it’s only when you can extract the information hidden in the data that the data itself becomes valuable.