The United States has the world’s best economy, but it’s also the most competitive market. In fact, only half of all startups in the U.S. make it past the five-year mark. Only the strongest businesses thrive in our free-market economy, but how do you know if your company has what it takes?
These days, every industry in the American economy uses big data to get insights to improve their products, customer service, and business efficiency. However, extracting data from various, disparate data sources makes it hard to maximize business intelligence, which is why companies use data virtualization for their data services. Continue reading to learn what data virtualization is and why your startup needs it.
What is data virtualization?
As mentioned in the introduction, your company will collect massive amounts of important information from multiple data sources in its efforts to optimize its operations. The challenge that disparate data sources create is they make it difficult for end-users to maximize data as there’s little to no continuity or correlation between different data sources.
Data virtualization takes data from traditional databases, data lakes, web services, and other sources. It also allows data architects to create a default setting for all your data. Virtualization creates a virtual layer that functions as a data warehouse where data is secure, centralized, and uses the same syntax.
As you can see, data virtualization is central to making the most of your data services. In the following sections, we’ll cover why it’s a good idea to get started with data virtualization for your startup.
Data virtualization software is used for big data analytics.
Data analytics is one of the most key functions of virtualization platforms. In fact, most of the time when people refer to actionable insights or business intelligence, they’re speaking about one or more of the many forms of analytics. Analytics turns data into metrics or stats that highlight key indicators of performance, efficiency, future events, and customer solutions.
Predictive analytics is one of the most promising areas of big data. In fact, police forces across the nation use it to forecast crime sprees and patterns. As you can imagine, the bookmakers in Vegas use it to determine the odds and spreads for sporting events. However, there are many practical use cases for organizations of all sizes.
One of the best uses of predictive analytics is predictive maintenance in manufacturing settings. Equipment failures cost manufacturers millions of dollars in man-hours and lost production, but data virtualization uses available data to create predictive models for future events. That means you can catch an equipment malfunction before it occurs and schedule maintenance accordingly, mitigating equipment failure and unplanned downtime.
Data virtualization is a prime data integration solution.
Could you imagine if you had to have a single app for every data source your company uses? Furthermore, could you imagine having to manually move that data from disparate sources to one centralized data warehouse so you can structure it? If it sounds like it’s labor-intensive and something you’d only want to do if you’d already watched paint dry on every continent including Antarctica, that’s because it is.
Traditional ETL data integration methods were time-consuming, budget-breaking, and still left much to be desired in terms of results. Data virtualization allows data to remain in their data silos while making it available to end-users on a virtual layer that enables fast access from all apps that need the data.
The reality is that it is tough sledding for startup companies, and yours can use all the help it can get to gain a competitive advantage. You need all the available data to create in-demand products and stay ahead of possible pitfalls. Indeed, you’re going to need big data to build a successful company and a virtualization tool to help you make sense of it all.