The Growing Importance of Real Time Analytics

Real Time Analytics is being considered a quantum leap in the realm of data analytics. But, what exactly is Real Time Analytics?

When a system is processing and analyzing data that was loaded instantaneously or not more than a minute before to generate meaningful information, then it is said to be performing Real Time Analytics.  This simply means, the system effectually processes data as soon as it arrives, without storing and retrieving it at a later time. The system functions dynamically, using the available resources to perform data-capture, integration, analysis and reporting, all in real-time.

Significance

For modern businesses that are data-driven, Real Time Analytics is a boon. Decision-making is an integral part of any business and data forms crucial part of this process. Be it planning, forecasting or testing, it’s the data that they need.

Real Time Analytics has a significant part in the acceptance rates and lifecycle development of Big Data, which is still in its nascent stage. The pace of real-time is partially accredited to the prescriptive and predictive nature of advanced analytics. The penchant for Real Time Analytics represents a decisive stage in the lifecycle of Big Data in the transition mode. As analytics turn more ubiquitous all through the Data Management landscape, their performances pace are credited to factors like in-memory computing, the multitasking competence of repositories to read and write data concurrently, along with functionalities of  search tools on Big Data sets.

Innovation

Improvement or innovation of new business intelligence technologies is imperative to capture this kind of data so that it can be quickly retrievable to support decision making. They have to cope with the huge scales of the data, and the role of Real Time Analytics becomes all the more exhaustive. However, the good news is that new and more advanced technologies are evolving to handle this kind of non-traditional and complex data. Software frameworks such as Hadoop, have led the way for disruptive technologies that can produce qualitative information from disparate sources, and are also evolving to the next stage.

In some cases while Real-time Big Data Analytics can maximize returns and reduce costs, it also heralds the era where machines can interact with each other over Internet of Things (IoT) using real-time information to make intuitive decisions on their own.

Implications

Institutions and businesses store massive data for years. While data in the past was arranged neatly in the forms of tables, the explosion of data today in the form of web logs, social media content, census reports, customer service records, etc., is mostly un-structured or semi-structured. Data-volume, data-velocity and data-variety, thus, all become essential to the data-scientists dealing with massive chunks of data, structured or un-structured, old or latest.

Evolution

The roots of real-time business intelligence technologies originated from dynamically-functioning sectors like aviation, defense and robotics. These sectors constantly evolved technologies that accessed live data in real-time to deliver highly responsive results. Some of the technologies include:

• Massively parallel programming (MPP)

This type of processing involved multiple processors to work in coordinated manner, by using their own individual processor, memory and OS. MPP allows a multitude of databases to be searched in parallel.

• Data warehouse appliance (DWA)

An innovative real-time architecture for data warehousing that was explicitly aimed at Big Data Analytics; it enables incredible performance for this type of workload.

• Processing in Memory (PIM)

This is an integrated chip architecture which minimizes latency, as the processor is incorporated into the memory chip.

Progression

Being an insistent process, RTBDA involves multiple systems and tools. It can ne divided into several stages, which specifically depend on the context.

Data Validation

This stage includes extracting relevant features of the data that qualify for data-modeling. Integrating the unstructured data from disparate sources, the irrelevant data can be filtered, while distilled data sets can be sent for processing.

Real-time scoring

Scoring is initiated by end-users using actions at the decision layer. Here, the arranged scoring rules are alienated from the data to be deployed separately.

Data revival

This stage constantly refreshes the changing data as the scripts run the data and construct models which can be re-used to refresh the models.

Application

• An ideal example of Real Time Analytics can be the Cloud-based CRMs which are capable of providing ultra-quick results. The up-to-the-minute information about customers and their demographics, purchase history, and preferences help make quicker business decisions. Real Time Analytics can refresh the sales and customer-support dashboards instantly drive enhanced customer experience.
• Also, Real Time Analytics will not just help you gauge your own metrics, but also allow you to assess your competitors’ or partners’ performance reports instantaneously.

With the advent of Big Data that involves massive unstructured data, the Real Time Analytics frameworks and tools can help gather, validate, analyze and utilize it more accurately and rapidly. When done in real-time as and when data arrives allows building of totally new modules of analytical/predictive applications. This enables business to make data-driven decision quickly and with much more confidence.

Benefits

1. Quicker Results

Classifying the data instantly when it is raw, enables queries to retrieve the suitable data, which allows to system to sort through it quickly. This helps in predicting the data trends and, thus, enables decision-making quicker and more efficient.

2. Less Expensive

It doesn’t mean the real-time technologies come cheap. However, the delays in receiving the information and usage of resources on dead-end results can be avoided. The multiple and constant benefits in the long run make it much more profitable.

3. Precise Outcomes

Big Data may require spending additional time on collecting data that might not be entirely relevant for the business. Real Time Analytics aims at focused outcomes using instantaneous analyses that are always useful.

Key Takeaways

• Data has been collected in various forms for thousands of years.
• The explosion of Internet has led us to Big Data and we are on the way to increase it further to unimaginable levels in the near future.
• Internet of Things (IoT) that involves massive unstructured data, Real Time Analytics frameworks and tools can help us gather, validate, analyze and utilize it more accurately and rapidly.
• When done in real-time as and when data arrives allows building of totally new modules of analytical/predictive applications.
• Real Time Analytics enables business to make data-driven decision quickly and with much more confidence.