Deciding to Move to Big Data Solutions – 5 Factors to Consider

Although it would be an understatement to say that the amount of data, both structured and unstructured, present on a global level is almost too large to comprehend; once analyzed for insights, this data could lead to better decisions and strategic business moves. It’s here that big data services come into the picture. Conceptualized by Doug Laney in early 2000s, big data comprises of five inherent features: Volume, Velocity, Variety, and more recently added, Variability and Complexity. Combine big data with highly structured analytics, and any organization could accomplish tasks, that would otherwise deem unforeseeable.

Big Data Solutions

Reasons to Move to Big Data – 5 Big Advantages

This post discusses five factors associated with big data services, which are pertinent to every organization who wish to grow their business.

1. Data Management

Data processing platforms require industry analysts to collect, analyze, and sift through various types of data. Although it takes some technical insight to decide and define data collection and storage, business intelligence tools such as big data let users work with data without undergoing the complications of incorporating too many technical steps. This added layer of abstraction has facilitated numerous business cases to mine data in multiple formats so as to extract information. One such instance of data management is the real-time video processing during the 2012 London Summer Olympic Games, wherein 1,800 cameras were monitoring the Olympic Park and the athletes’ village, and teams of analysts used applications to process closed-circuit video data, to identify any potential miscreants. 

2. Scalability

Organizations often consider services of third-party cloud service providers, to provide storage and the computing power required to analyze data for a specific period. To substantially utilize large data, organizations must look towards Cloud storage, which offers three advantages. Firstly, it enables companies to avoid significant capital investment in hardware, which would then host the data internally before analyzing it. Secondly, as big data hosting platforms require newer skills and improved training, a cloud-hosted model tends to abstract the complexity and enables quicker deployment in the organization. Lastly, it allows developers in creating a preconfigured sandbox environment, which doesn’t require the setting up of the necessary configurations from scratch.

3. End Users Data Visualization

Incorporating the big data initiative requires next-level data visualization tools, which present data in easily readable charts, graphs and slideshows. Such applications must be able to offer processing engines that allow end users to query and manipulate information quickly, despite the obvious vast quantities of data being examined. Applications would need adaptors that connect to external sources for additional data sets as well. Access to charts, infographics and dashboards enables CFOs, CMOs, and other non-IT executives to leverage data, and imply strategic business moves. A shift to self-service analytics model from an IT-driven approach accelerates adoption and return on investment while expanding analytics’ to reach beyond report writers and technical end users. 

4. Business Opportunities

Nowadays, more users realize the competitive advantage of incorporating data analytics in their enterprise.  For instance, social media sites generate revenue from the data collected by selling ads based on an individual user’s interests. Identifying such opportunities allow companies to target specific sets of individuals that ideally fit a client profile. In the retail sector, big data allows the study of cases wherein studying consumer behavior in online stores or physical shopping centers provides important business insights. 

5. Data Analytics Improvement

Data comprises of not only numbers but also text, audio and video files. Aligning the right set of tools can help defining criteria and recognize specific patterns from these data elements, using natural language processing tools. These tools extract invaluable information by text mining, clinical language, sentiment analysis, and name entity recognition efforts. One example highlighting the use of audio analysis via big data is MatterSight. This tool used in call centers matches the incoming caller with the appropriate help-desk agent by using predictive behavioral routing. Audio analysis is performed to identify and score the calls and then customers are aligned with the best department. Such is the strength of the advanced capabilities which are developed through analysis of unstructured data analysis and big data capabilities.

Bottom Line

Big data initiative requires acquiring the right analysis tool along with the right talent. Data scientists working with big data, are in short supply and are being offered high salary packages by top firms so that organizations could keep a tab on the innovations which could transform the existing business models and identify potential revenue sources. Organizations planning to invest in big data initiative, must keep these five advantages in mind and put the right amount of resources and talent, they could easily stay a yard ahead of the competition. If you have any questions regarding big data or other related data center services, call us at +91-120-466 3031 or fill our contact form, and we will be happy to help.