Big Data Versus Smart Data

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Big Data 

Most organizations recognize the promise of data as a transformational decision-making tool, but far fewer are realizing the benefits of big data analytics software. What’s holding them back?

According to a survey conducted by the Business Application Research Center, only 6 percent of surveyed companies report no roadblocks to using big data. The rest - companies  around  the  globe  and  across  different  industries - identify  various barriers. Up to 53 percent of the survey respondents say inadequate technical or analytical  know-how  within  their  organizations  is slowing  adoption. Among  38 percent of companies, the lack of compelling business case or cost are issues. And nearly one-quarter struggle to make  big data digestible for end users. Overcoming these obstacles will help organizations forge a path to improved performance and growth.

big data survey

5 Vs Big Data Challenge

Why is working with big data so difficult? It comes down to several factors, which research and advisory leader Gartner first introduced as the Vs of big data in 2001.

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Volume

90% of data globally has been generated in just the last two years. Given perpetually increasing volume of data available, identifying the data your organization needs is akin to searching for a needle in a haystack.

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Velocity

Big data analytics tool supports rapid analysis, making adoption of predictive analytics or machine learning more important than ever if organizations want to realize competitive advantages from data in real time.

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Value

Embark on big data initiatives to answer a clear question, organizations can turn data into measurable value - from optimizing processes and improving customer service to predicting risk and identifying emerging opportunities.

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Variety

80 percent of the world’s data is unstructured, making it more difficult to harness the data without big data technology. Consider the potential value of unstructured data found in social commentary to inform marketing plans or sensor data from a production line to optimize operations.

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Veracity

Volume, variety and velocity combine to create another challenge for organizations trying to leverage data effectively. Big data analytics software use advanced analytics techniques like semantic analysis, entity linking, applied metadata or other data enrichments, along with data normalization, makes it possible to tap into data from a wide range of sources

Nexis® Data as a Service

Nexis® Data as a Service addresses the challenges of the five Vs of big data with its four main advantage.

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Comprehensive

A source universe offering controlled volume and a wide variety of datasets to meet diverse uses

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Optimal

Flexible Bulk, RESTful and controlled content APIs deliver normalized, structured data at the volume and velocity needed for big data applications

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Robust

Smart data, organized and enriched through a combination of expert human curation, advanced analytics and topic tags, for greater veracity

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Experienced

A partner with 45+ years of aggregating content, plus patents in clustering and machine learning, for dependable value

After 45+ years  providing  solutions that  help  organizations harness  the power of information, LexisNexis remains dedicated to developing innovative tools to support data-driven decision-making. Our commitment extends beyond comprehensive content and outstanding search technology to world-class client service support, ensuring that our clients gain maximum insights and value from LexisNexis solutions.

Frequently Asked Questions

Answers to some popular questions

What is big data?

According to a survey conducted by the Business Application Research Center, only 6 percent of surveyed companies report no roadblocks to using big data. The rest - companies  around  the  globe  and  across  different  industries - identify  various barriers. Up to 53 percent of the survey respondents say inadequate technical or analytical  know-how  within  their  organizations  is slowing  adoption. Among  38 percent of companies, the lack of compelling business case or cost are issues. And nearly one-quarter struggle to make  big data digestible for end users. Overcoming these obstacles will help organizations forge a path to improved performance and growth. Read more

What are Big Data Challenge?

Why is working with big data so difficult? It comes down to several factors, which research and advisory leader Gartner first introduced as the Vs of big data in 2001.

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