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Big Data Analytics and You – Where It’s Going

by Samantha Kalany

Estimated reading time: 3 minutes

Big Data Analytics have taken over enterprises everywhere and hold a lot of value to VARs, End-Users, and ISVs alike. What is Big Data, you might ask? It’s actually a larger and complex version of traditionally captured data. Big data is both structured and unstructured as requiring a storage capability that outstretches beyond normal servers. Big Data is quite commonly defined as performing the following tasks: holds larger volumes of data, manages increased velocity of data being received, and contains new varieties of data formats. Big Data is leveraged to discover obscure correlations and consistencies in the data, that can lead to increased business value.

Traditional Data vs. Big Data AnalyticsBig Data Analytics

There are several differences between big data and traditional data, overall. Big data provides real-time results and updates, it’s found to be scalable, and holds benefits similar to that of machine learning tactics. Traditional data cannot handle that those immense volumes of information to such capacity. Over half of several large companies have adopted and embraced data analytics in some shape or form. AI and IoT hold a large meaning in paving a smooth path for enterprises that are interested in these data practices.

Following the Industry Trends

Up in the Cloud Managed Services 

The rise of Big data is persuading enterprises to adopt cloud managed services. Cloud computing was made perfectly for big data uses. Companies can securely refine any data in their processes. Some businesses hold stricter compliances must follow more streamlined guidelines for ensuring safety and privacy rules, and big data can help with that. From installing authentication and encryption setups, big data is very instrumental in that process.

Virtual Reality Kicks In

For simplification purposes, enterprises are jumping on the VR train. Virtual Reality applications can change the ways that industries and employees share and analyze data. Going beyond most analytical tactics, VR deliver individuals into a new world, made complete with richer data. VR allows employees to touch the lists of data with hands. It’s been proven to be more interactive in verifying such data.

Predictive Maintenance’s Role 

Big data is very common in the supply chain industry. In today’s age, predictive maintenance is the norm in the business realm. On the basis of analyzing larger data sets, employees are able to determine with articles of equipment seek maintenance, before going out of commission.

This activity is completed through the help of smart sensors embedded into the machine or device, triggering alerts on a situational basis. These alerts are then communicated to employees to make sure that everyone is on the same page. The development of predictive maintenance has allowed for companies to boost operational efficiencies, enhance customer engagement, and discover new revenue streams.

Strategizing Big Data

When it comes to leveraging data visualization tools, these resources are quickly becoming some of the most resourceful mechanisms in the industry. They are best known for comprehending, even the most complex data sets. Employees use machine learning to aid in enhancing data set results as well. Today’s tools can present the data in some of the most sophisticated ways:

  • Geographic Maps
  • Scatter Plots
  • Bubble Charts
  • Heat Maps

The Challenges at Hand

Inaccurate Data: Companies and Enterprises are adopting data cleansing tools, which can remove any incorrect data. Scrubbing and cleaning data is critical, especially for those businesses who have established strict compliances.

Data Silos: Storing data in different silos with a limited ability to scale all of the data is a major challenge. It’s common for issue amongst enterprises, especially those who are new to strategizing big data. Many businesses will settle with unified data platforms, which break down the data silos to gain a more advanced outlook on business insights.

Difficult Data Conversations: When raw data is collected, it’s then converted into analytics. This process isn’t the most efficient and could be time consuming. Data visualization tools can be leveraged as a remedy, in the end.

The future is bright for Big Data. The market is projected to surge as time goes on. AI is likely to play a large role in this progression, as will the blockchain technologies. 

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