The massive volumes of information generated and used for decision making in business today fuel the booming business of Machine Learning (ML) and Artificial Intelligence (AI)-enabled applications.
Software companies exploiting Artificial Intelligence help businesses achieve valuable insights in many areas. This includes in-depth customer research, product development, cost management and competitive analysis. Applied AI also provide operational intelligence, enabling companies to investigate, monitor, analyze situations and then react and respond.
Software applications taking advantage of AI and ML affect organizations across practically every industry and play an active part in developing strategies for their success.
For retailers, AI-enabled solutions help them to get a better understanding of customers and the most effective ways to keep them coming back. Manufacturers can solve problems faster and make more agile business decisions. For bankers it can help minimize risk and fraud.
Artificial Intelligence, Machine Learning, and Big Data are complementary terms, hence the also-used phrase Applied AI. Data is the fuel that feeds AI. Several very successful publicly traded companies are helping enterprises harness their data and digital assets, setting the foundation for powerful AI applications.
The list of companies applying AI to large end markets and use cases continues to grow. Publicly traded Big Data players that are in the business of helping enterprises translate their data and digital assets into actionable insights include Splunk (SPLK), Tableau Software (DATA), New Relic (NEWR), and Alteryx (AYX). These companies can specialize in various areas, including data mining and cleaning, data analysis, machine data, visualization and storage.
The Big Data trend has also fueled an expansion of initial public offerings. Recent Big Data IPOs include Elastic (ESTC) and Talend (TLND). Splunk and Tableau Software were among the first to come public. Splunk came public in 2012, followed by Tableau in 2013.
What Is Big Data?
Big Data describes huge volumes of data that flood a business on a daily basis, coming from an ever-growing variety of sources. It typically describes data sets with sizes beyond the ability of traditional data processing software tools to capture, process and curate in a timely fashion.
The data creation and gathering process come from sources that include wholesale or retail transactions, e-commerce data, shipping, audio and video logs, text messaging, internet search queries, satellite imagery and GPS data, as well as stock-market activity and financial transactions. Data is also flooding in from the Internet of Things.
The vast amounts and various types of information collected enable Big Data companies to approach the market in different ways and specialize in specific areas.
What Is Data Analytics?
Data analytics is the method of extracting and analyzing all manner of data in order to draw conclusions about the information. It uncovers hidden patterns or unknown correlations within the data, or discovers emerging market trends and customer preferences.
But data analytics is not necessarily applicable just to Big Data. Data analytics can apply to most types of processing that analyze data, but as the size of organizational data grows, the term data analytics is evolving to favor Big Data-capable systems.
The field of data analytics is growing fast, driven by market demand for systems that tolerate the intense requirements of Big Data.
Turning Data Into Insights
Splunk won accolades as a pioneer in its field. The company provides Intelligent Software designed to help organizations search, correlate, analyze, monitor and report on data in real time. They focus on machine-data applications.
“Machine data is produced by nearly every software application and electronic device across an organization and contains a real-time record of various activities, such as transactions, customer and user behavior, and security threats,” Splunk said in its annual report.
Splunk revenue in 2018 jumped 38% from the year-ago period to $1.8 billion. Adjusted earnings rose 39% to $1.33 per share.
Tableau Software helps companies perform analysis on their information without the need for technical specialists.
The flexible design of its products can handle a broad range of use cases. It ranges from answering questions with small spreadsheets to complex enterprise business intelligence projects.
“Our software products put the power of data into the hands of everyday people, allowing a broad population of business users to engage with their data, ask questions, solve problems and create values.” the company said in its annual report.
Tableau revenue in 2018 leapt 32% to $1.15 billion. Adjusted earnings soared 478% to 1.56 per share.
Alteryx provides self-service data analytics software that organizations use to improve productivity and business outcomes. Organizations can easily profile, prepare, blend, and analyze data from a multitude of sources and then benefit from data-driven decisions.
“Our platform democratizes access to data-driven insights by expanding the capabilities and analytical sophistication available to all data workers,” the company said in its annual report.
“We bring the fragmented analytic process into one simple and cohesive self-service experience, combining tasks that were previously distributed among multiple tools and parties.”
Alteryx revenue in 2018 climbed 55% to $204 million. It has yet to show a profit.
New Relic’s products enable organizations to collect, store, and analyze massive amounts of software data in real time. Users can quickly find and fix performance problems as well as prevent future issues.
“Our mission is to empower organizations to build the best modern software possible and to improve their business intelligence using the data flowing through and about that software,” the company said. “With our products, technology users can quickly find and fix performance problems as well as predict and prevent future issues.”
New Relic revenue in 2018 rose 35% to $355 million. It showed an adjusted profit of 1 cent per share, vs. a loss of 49 cents.