Data Analytics
Abstract:
We now live in a data-driven society; thanks in large part to the recent widespread proliferation of connected devices, almost everything has the potential to be tracked or is being tracked already and converted into data. According to IBM, 2.5 quintillion bytes of data are generated every day, which presents some challenges but also countless opportunities: data becomes extremely helpful if it can be analyzed quickly and put to work. Once properly interpreted, data can pro- vide us with valuable insight into macroeconomic trends, customer preferences and employee tendencies so that enter- prises can make informed decisions to optimize resources and act intelligently in real-time. In order to efficiently and effectively tap into the massive amounts of data that companies have at their disposal, there has been a growing demand for various analytics platforms and applications. Recent developments in machine learning and artificial intelligence have aided Data Analytics providers in removing the need for manual intervention, and compa- nies are able to sift through seemingly endless amounts of data rapidly and without the risk of human error. Real-Time Analytics continue to expand with the growing need for bringing analytics closer to the user, with cognitive, predictive and prescriptive analytics increasingly being embedded into line-of-business applications. Big Data Analytics solutions complement Real-Time Data Analytics as the longer-term data store where deeper, longer-term analytics can be per- formed on an ongoing basis. As is the case with most technological revolutions, there is no shortage of emerging companies that have developed so- lutions to help organizations capitalize on the data they have collected, many of which have chosen to focus on specific verticals while others have focused on broader horizontal platforms. IDC believes the worldwide Big Data and Business Analytics market will increase from $130 billion at the end of 2016 to $203 billion by 2020, representing a CAGR of 11.7%. As funding continues to be poured into this sector, many investors continue to show confidence that there still is room for Data Analytics providers to innovate and differentiate in this increasingly competitive landscape.
Discussion Topics:
Please introduce yourself, your firm, and take a minute to express your views on today’s discussion topic.
What are some of the most common use cases you have witnessed for Data Analytics?
How has the rise of Big Data platforms (e.g., Hadoop, Spark, etc.) changed how Data Analytics providers are devel- oping their solutions?
What are the benefits/risks associated with Data Analytics companies specializing on a particular vertical vs. a broader horizontal platform?
How big are these vertical market opportunities and which ones are of particular interest from your perspective?
What are some of the main line-of-business applications that you have seen grow increasingly integrated with Data Analytics?
How will the future of Data Analytics change as more and more companies are growing more dependent on data for critical business operations?
How do you define machine learning and artificial intelligence? How are these technologies going to change the Data Analytics landscape, and where are we on market adoption?
From a vendor ecosystem perspective, who do you see as the primary vendor providing these solutions – (1) incum- bent analytics / data platform vendors, (2) vertical application providers, or (3) emerging horizontal solution vendors?
Who are the potential acquirers in this space that you find the most interesting/compelling?
Made with FlippingBook - Online catalogs