One of the most interesting things about technology is its ability to keep evolving constantly. Let’s take a look at big data, for instance, and how it has exploded to become a one-stop solution for actionable insights in all types of businesses. We have seen big data technology evolve from a buzzword to a business staple in a very short time span, offering companies numerous approaches to the problems they encounter in their daily operations, from marketing analytics to HR.
Data streaming has been one of the key data capabilities brought about by the evolution of big data. Stream analytics can analyze, enrich, aggregate, and filter high volume live data insights for real-time use. Data streaming allows businesses to conduct statistical analysis of data at any given time. Streaming analytics has led to improvements in the key components of big data. Take a look.
Quality of Data
Today, businesses are using big data to harness insights in the present. In the past, big data could only be used to project future trends or applied in retrospective reports. Thanks to data streaming, companies have greatly improved the quality of big data via the use of parallel operations databases and distributed data stores. These techniques can cleanse and enrich high-velocity data effectively.
Data Preparation, Integration, and Virtualization
Messy and diverse data sets have always been hard to put into application. However, data streaming technology has helped businesses accelerate the usefulness of this data by improving the process of data preparation. This eases the burden of sourcing, cleansing, and sharing vast amounts of unstructured data sets.
Data integration hasn’t been left behind either. Tools such as Hadoop, Couchbase, MapReduce, Apache Spark, and so on have eased the process of data integration in organizations. Data virtualization is the technology used to deliver information from various data sources within an organization. This includes large sources such as frameworks that store data in real-time and near real-time including Hadoop.
Predictive analytics describe a group of applications that analyze big data sources to facilitate the discovery, evaluation, optimization, and deployment of predictive models to mitigate risk or improve business performance. The combination of predictive analytics and data streaming capabilities helps organizations to get the best out of big data technology.
Data Streaming and Innovation
Besides helping companies to make huge strides in their utilization of big data, data streaming has also led to innovation. Data streaming, alongside stream processing — a platform that allows an organization to examine data in motion or real-time data, and compare it to data at rest for the best results — have inspired massive innovation in the world of business.
Real-Life Examples of Innovation in Data Streaming
Data streaming allows the analysis of data at rest and data in motion consecutively. As a result, data streaming has contributed to innovations in the business world including e-commerce, finance, web, as well as brick and mortar stores. For instance, financial companies and Ecommerce platforms can now detect fraudulent activities as they happen by using machine-driven algorithms to watch suspicious patterns.
Financial institutions can also monitor market fluctuations in real-time and conduct up-to-the-minute risk assessments. They can now rebalance their portfolios using these accurate computations and prevent losses. In the case of brick and mortar stores, it gets a little easier. These businesses can use the little pieces of data gathered from their clients’ smartphones — such as location, for instance — to offer incentives when a customer is nearby.
Big data offers immense opportunities for the business community. Data streaming allows the analysis of large sets of data in motion alongside data at rest thus increasing functionality. Data streaming is set to continue leading to improvements as well as innovation in the way that we conduct business.
by: Vincent Stokes