Data’s impact on business is only gathering momentum. Key trends in business intelligence for this year include a focus on the Internet-of-Things (IoT), highly improved cognitive solutions and the rise of insight-driven organizations. With such growth in potential uses, sources and types of data, enterprises of all sizes are now facing increasing complexity when it comes to collection, management and analytics. Increased complexity, in turn, makes for new challenges for decision-makers and data scientists.
The rapid rise of connected devices and business ecosystems has resulted in an increasing number of disparate data sources, coupled with a growing diversity of data types, all leading to the increasing complexity of data.
A recent whitepaper published by Aberdeen and Sisense on integrated single-stack business intelligence solutions points out the challenge of data complexity, citing the following trends among survey respondents:
-- 93 percent of organizations cite significant data growth over the past year.
-- Respondents use an average of 30 unique data sources on a regular basis.
-- 40 percent of respondents analyze unstructured data from both internal and external sources.
While larger data sets can provide deeper and more impactful insights, these also have major implications on the resources needed by organizations, especially when it comes to time-consuming and potentially expensive data collection, storage and analytics involved. It can be extremely heavy on resources to manipulate and visualize this data into formats that are quickly and easily understood for decision-making. As a business’s data begins to grow, the more likely it is to experience these challenges.
Implementing strategies to properly manage complex data is not an easy task, either. Enterprises often spend millions of dollars each year on dedicated infrastructure and software solutions to crunch numbers and process information. In addition, these big organizations have traditionally hired specialists and data scientists to regularly maintain their complex pipelines.
However, the current trend is moving toward self-service analytics platforms, which promise ease-of-use through visual approaches to managing data. The downside is that not all of these solutions are capable of handing complex data sets, which often require parsing vast amounts of data from multiple sources.
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