How to manage complexity and realize the value of big data

What would the world look like if every business decision was well documented and driven by big data? Rapid technical advancements in advanced analytics, AI and blockchain suggest we might not have to wait too long to find out.

With more than 150 zettabytes (150 trillion gigabytes) of data that will require analysis by 2025, it is a critical time for businesses to adopt and enhance big data solutions in order to meet the challenge with a competitive edge.

Studies show that more than 95% of businesses face some kind of need to manage unstructured data. The term “big data” refers to the processing of massive amounts of data and applying analytics to deliver actionable insights.

Advancements in artificial intelligence have helped big data technology progress beyond simply performing traditional hypothesis and query analytics. Now, the technology can actually explore the data, detect trends, make predictions and turn unconscious inferences into explicit knowledge that businesses can leverage to make better decisions.

AI opportunities

Big data presents an opportunity for machine learning and other AI disciplines. A few years ago, a Forbes study found that there were 2.5 quintillion bytes of data created each day. Digital transformations such as the Internet of Things have contributed to this unprecedented surge of data in recent years.

Since AI thrives on an abundance of data, it can help organizations gain new insights and personalized recommendations derived from unbiased IT data. For example, a leading open-source framework like TensorFlow can help improve the abilities of virtual agents by analyzing interactions in real time and helping virtual agents answer queries quickly and conversationally.

Big data + open source

Open-source software, which is available for free and highly customizable, plays an important role in big data. The technologies have been connected for years, used together to build customer behavior models for retail, anti-money-laundering initiatives for financial enterprises, fraud-detection protocols for insurance companies and even predictive maintenance for utilities providers.

The framework most commonly associated with big data is Apache Hadoop. For years, Apache Hadoop has made it possible for businesses to build big data infrastructures and perform parallel processing, using commodity hardware and lowering costs. That said, big data is far more than Hadoop alone.

As the age of digital transformation continues to surge, velocity and real-time capabilities have emerged as prerequisites for business success. To meet these new requirements, Apache Spark—a highly versatile, open-source cluster-computing framework—is often implemented alongside Hadoop to increment performance and speed.

Changing consumer habits are also driving a shift in the data mix, increasing the amount of unstructured data such as text, audio, video, weather, geographic data and more. The traditional data warehouse is evolving into data lakes and integrating data from Structured Query Language (SQL) and non-SQL databases, as well as multiple data types.

Don’t face the complexity alone

Open-source technology continues to dominate the IT ecosystem, largely due to its ability to innovate and quickly solve problems. This doesn’t mean that there’s no room for proprietary software or commercial offerings derived from open-source software. IT environments are growing more complex, and building big data solutions often requires the integration of multiple pieces of software. As such, a number of companies have begun to test, certify and create distribution-like solutions in this space. Still, big data has become a mature market, sufficiently proven by recent acquisitions that have considerably reduced choices for customers.

Keeping everything up and running in a successful environment requires you to deal with multiple pieces of software while also integrating new data sources. Because of this, many companies are embracing support solutions for open-source technology to reduce the complexity of their IT ecosystems with a single point of contact and accountability across the infrastructure.

A single source of support for community and commercial open-source software, running on cloud, hybrid cloud, multicloud or locally deployed systems, can help you meet complex support challenges, predict and resolve problems even before they occur, and realize the full value of big data technology.

Source: ibm.com