Are You Ready To Lead An AI-Driven Economy?

Data availability is the lifeblood of organizations in every industry. Over the past decade, the amount of data available has grown exponentially. This data—structured and unstructured—can be used to better understand customer behavior, identify market trends, and improve operational efficiency. As a result, leaders are starting to rethink how they store, manage, and connect every data set for analysis and automation to increase productivity, make decisions faster, and gain insights. New details.

Accepted as standard practice in previous years, tiering data across multiple storage layers is no longer a realistic option, Amazon Web Services reported last year. Instead, leaders increasingly need to be able to move data seamlessly between applications and workloads, and do it all at scale. Therefore, they must understand the organization’s data infrastructure to ensure it can meet current and future operational needs.

Data has always been important to companies, but the age of Artificial Intelligence (AI) has taken it to the next level, he concludes. Atlantic. In the past, data was used primarily for operational purposes such as tracking inventory or customer orders. Today, data is used to understand customers, personalize their experiences, and drive business decisions. For example, consider that this week, ChatGPT gained 1 million users in less than a week. Asset reported that “AI chatbots are designed to disrupt search”. AI is not only the ability to process numbers but also to leverage data to learn from the past and predict the future. That is why, according to a recent report by Grand View Research, the global AI market is valued at $93.5 billion by 2021 and is expected to expand at a compound annual growth rate. is 38.1% from 2022 to 2030.

To succeed in this new economy, companies must invest in solutions that allow them to more efficiently store and manage data so they can take full advantage of AI. Doing so will help them prepare for the future of work and stay ahead of the competition.

VAST Data is an explosive growth company driven by business demand for AI-powered infrastructure solutions and data analytics. The company recently posted annual recurring revenue that is four times higher than last year, with a gross margin of 90%. Driven by acquiring new data-centric customers and expanding petabyte-scale deployments globally, VAST expects its business momentum to continue in the coming months as the company doubles in the flash cloud, revolutionize data warehousing, and build data platforms for the next wave of machine and artificial intelligence. Renen Hallak, founder and CEO of VAST, said: “As data becomes the most important asset in an enterprise, organizations realize they need more utility than legacy data platforms. can offer to drive new AI solutions. .

He believes that leaders will demand fast and seamless access to their data to drive AI innovation. He argues that “the whole concept of data stratification is outdated.” Instead, as data ages, it often becomes more valuable. VAST provides a system that collapses the pyramid of tiers—hot, warm, and cold data—that allows organizations to access all of that data, anywhere, in real time. Hallak added: “We believe the market is ready for an all-flash data platform that can power any workload of any size.

Jack Tillotson, a lecturer at Finland’s Vassa University, agrees. “A new platform is needed to unify data across all types of workloads and break down the silos that legacy systems have created,” he said in an interview. He believes that data infrastructure is becoming more complex as organizations strive to keep up with the increasing volume, speed, and variety of data—especially true for companies that deal with large amounts of data. Large data sets are growing exponentially.

To deal with this complexity, Tillotson suggests that leaders think of data infrastructure in terms of the “three Cs”: capacity, cost, and complexity. “The first two are relatively easy to understand,” he said. “Capacity is the amount of data that can be stored and processed, and cost is the price of that. The complexity is a bit more nuanced, but it’s basically the amount of effort required to manage the data infrastructure.”

While it’s impossible to completely eliminate the complexity of data infrastructure, Tillotson believes it can be managed in a way that doesn’t stand in the way of progress. “The key is to simplify and automate as much as possible,” he advises. “That way, you can free up time and resources that can be better spent on more strategic initiatives.” Here are four tips to get you started:

1. Importance of data quality

Data quality is essential for making informed decisions and ensuring your system runs smoothly. Conversely, poor data quality can lead to errors, inefficiencies, and lost opportunities. To deliver high-quality data, leaders should put in place robust processes and controls to manage data collection, storage, and analysis. For example, they should establish clear guidelines on how to collect data and ensure that it is stored in a secure and accessible manner. Furthermore, they should identify “high-quality” data and implement mechanisms to monitor and improve data quality over time.

2. The need for scalability

As data volumes continue to grow, it is essential to have a data infrastructure that can scale to meet your organization’s needs. This means having the ability to add more storage and processing power as needed. Leaders should work with their IT teams to ensure that their data infrastructure is scalable and can meet future needs. For example, they might consider combining on-premises and cloud-based solutions to get the most out of both.

3. The value of data security

With the increasing importance of data, the need for security is increasing. Leaders should ensure that their data infrastructure includes robust security measures to protect against unauthorized access and data breaches. For example, they can encrypt data at rest and in transit, implement access controls, and use firewalls to protect their systems. Furthermore, they should plan for data recovery in the event of a disaster.

4. The Necessity of Team Collaboration

Data infrastructure is not a one person job. A team of skilled professionals must design, build, and maintain a robust data infrastructure. Leaders should create an environment that fosters collaboration and ensures that everyone on the team has the tools and resources they need to succeed. For example, they can provide training on new technologies and processes, allow team members to work on challenging projects, and encourage knowledge sharing.

The applications and algorithms that enable these new forms of business intelligence—unlike before possible—require workloads to run on data infrastructure that allows random access and sharing , Hallak identified in a video interview. Legacy vendors, he said, tend to be very good at iterating what they have but not very good at adopting a new model. “We’ve seen them try to compete by improving on legacy infrastructure architectures, but artificial intelligence and machine learning are innovations that require clear thinking.” Hallak predicts that next-generation infrastructure delivers the high-speed, low-latency performance required by these demanding applications and algorithms, allowing them to perform at their best. Therefore, all data needs to be connected to become more creative and valuable. Without it, the benefits of AI and machine learning would be thwarted, limiting their usefulness and impact.

In short, leaders need to think carefully about their data infrastructure if they want to leverage AI and machine learning technologies. They need to ensure that their infrastructure is secure, scalable, and collaborative, and they must put in place robust processes to manage data collection and analysis. That way, they will be able to harness the potential of these powerful technologies and reap the rewards of a modernized, data-driven organization.