Big data technologies and how to use them are hot topics in the business world.
With organizations struggling to gain a competitive edge by any means possible, finding the value hidden in big data resources can be the key to an enterprise’s success. Using Big Data analytics can help to improve your business efficiency and productivity, make effective risks evaluation and prevention, deliver personalization and better customer service and much more.
The technologies used in big data processing need to address the characteristics of big data that set it apart from traditional information resources. We will look at what distinguishes big data and the available methods to process it effectively.
What is Big Data Technology?
Gartner defines big data as high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of data processing, enabling enhanced insight, decision-making, and process automation. These characteristics are known as the three Vs.
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Volume – The amount of data is the primary factor describing big data. Handling this amount of data can overwhelm traditional storage and processing methods. Solutions such as the Hadoop framework address these concerns and are contributing to the ability of organizations to make effective use of big data.
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Velocity – The speed at which information is created, collected, and acted upon also describes big data. Smart devices may need to process this data in real-time to provide the benefits inherent in the technology. The Internet of Things (IoT) is a major contributing factor to the increased velocity of big data.
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Variety – Big data comes in a variety of formats, making it more challenging to process efficiently. It is a combination of structured, unstructured, and semi-structured information gathered from diverse sources. Data contributing to this variety includes emails, social media feeds, video, freeform text, and large structured datasets.
Big data technologies and tools that are designed to work with this type of data are becoming instrumental in business. They offer ways to extract useful information from the many sources that contribute to the entity known as big data.
Popular Big Data Applications
Here is a rundown of some of the promising technologies for big data processing. They provide organizations with methods with which to derive competitive advantages from their data resources.
Analytics
One of the most prevalent ways that the immense amounts of information contained in big data can provide insight into trends that impact an organization is through analytics. There are main forms of analytics which are:
Predictive analytics – Statistics are used to model and determine future performance based on historical data in predictive analytics. It is an essential tool for marketers and a widely used tactic of the insurance industry.
Artificial intelligence (AI) and machine learning (ML) are technologies that are used when conducting predictive analytics.
Predictive analytics is widely used in retail, entertainment, manufacturing, cybersecurity, human resources, healthcare and is one of the main proptech trends.
Many freeware and proprietary tools are available that enable organizations to perform predictive analytics. Some examples are the Google Cloud Prediction API, IBM Predictive Analytics, and Apache Spark.
Prescriptive analytics – In this type of analytics, systems are used to analyze the way that specific situations can be addressed by taking into account factors such as past performance, current performance, and available resources. It is used to analyze potential responses to scenarios for long-term or immediate remediation.
Techniques like graph analysis, simulation, complex event processing, and heuristics are employed in prescriptive analytics. Tools like RapidMiner, Sisense, and Improvado are some of the prescriptive analytics solutions on the market today.
Descriptive analytics – Historical data is interpreted to understand business changes more clearly through descriptive analytics. Examples include year by year pricing changes, monthly sales growth, and monitoring the number of users accessing websites. This type of analytics has been used by organizations long before the introduction of big data sources to gauge their health.
Stream analytics is an additional form of analytics that is used to filter, aggregate, and analyze live data that is collected in many different formats. The goal is to analyze this data as it is created to allow insights to be provided in real-time.
Tools that are used for stream analytics include Azure Stream Analytics, Azure Event Hubs, and Amazon Kinesis. Through the use of these solutions data in formats such as video, audio, application logs, and website clickstreams can be thoroughly analyzed to provide businesses with timely insights based on their big data resources.
Big data analytics technologies are becoming an indispensable aspect for enterprise risks analytics and business strategies evaluation in many market sectors. Enterprises that ignore the advantages that analytics can provide are missing valuable opportunities for growth and optimization.
In-memory databases
An in-memory database (IMDB) stores data in memory as opposed to the disk storage used by traditional relational databases. This allows the information to be processed much more rapidly which is critically important when attempting to provide real-time analysis or responses when dealing with big data.
Many smart technology applications require near-instantaneous data processing to be worthwhile to their users. In-memory database solutions are becoming more widely available from vendors such as Oracle, Microsoft, IBM, and SAP. Sales of this technology are increasing and are estimated to reach over $6.5 billion by 2021.
Edge computing
Edge computing is a methodology that is becoming more popular in part due to its relation to the spread of the IoT. In an edge computing implementation, information is processed as close as possible to where it is created, or on the edge of the network. It is an emerging field that promises to increase in importance over the next decade.
By processing data at its source, tremendous savings in network bandwidth and latency can be achieved. As the field matures, more sophisticated analytical capabilities will be built into edge devices, reducing the need to transmit unprocessed data even further. It is an example of innovative big data processing technologies that are addressing the quantities of information made available in our connected world.
Security concerns and solutions
Security is an area where big data innovations require new solutions to protect information resources. The vast amounts of data held in big data repositories offer an inviting target for hackers.
Minimizing unauthorized access to these assets is of paramount importance for organizations that use big data. Strong identity and access management techniques are required to ensure only the appropriate personnel can get to the data stores. These initiatives are complicated by a variety of sources that generate big data. Businesses that are regulated have additional compliance factors to be considered when protecting their big data assets.
Conclusion
How organizations choose to use big data can have a significant impact on their ability to compete with their market rivals. The availability of new data streams presents almost limitless possibilities for transforming the way business is conducted. Companies that intend to be successful in the future need to start taking advantage of this innovative potential as soon as possible.