It is estimated according to Bernard Marr, that by 2020, an estimated 1.7 megabytes of new information will be created per second, for every human being on the planet. That’s a huge amount of data.
Real-time and streaming analytics is gradually gaining importance in this rapidly advancing digital age. Several prominent companies are developing business intelligence platforms to fuel the growth of the global analytics market.
According to the Global Retail Analytics Market (2017-2023) report, the market for analytics will possibly reach USD 9.50 billion by 2023 at a CAGR of 20.50% during the period.
To fully appreciate the significance of real-time data analytics, let us assess the history of data analysis and the use of historical data in descriptive, prescriptive and predictive analytics.
What is Historical Data Analysis?
Historical Data Analysis is primarily focused on analysing data of the past. For historical data analysis, analysts attempt to analyse data by exporting relevant data from a past day, month, quarter or any earlier period of time. They will then perform one or more of three different types of analyses.
Descriptive analysis creates a concise story from a segment of historical data. The story will ideally have an overall theme. Some use cases of descriptive analysis is in the sales reporting in a specific period. Among various business intelligence activities, most analysts are familiar with descriptive analytics.
Descriptive analytics enables analysts to understand past events and also allows them to build predictive and prescriptive analytical models.
Predictive Analytics analyzes trends data to predict future events and occurrences. Analysts showcase likely scenarios with the help of Data Mining, Statistics and Machine Learning.
Predictive Analytics is most effectively seen in Amazon’s recommendation engine that analyses customers’ online shopping behavior, and suggest other similar or related products that the customers might find relevant and want to buy.
While Descriptive Analytics tells analysts what happened and Predictive Analytics tells them what might possibly happen if historical trends continue, Prescriptive Analytics seeks to inform data analysts about what to do. This process analyses the data and prescribes real-world decisions that analysts can adopt.
Possible Use Cases
The typical use cases for historical data analysis and descriptive, predictive, or prescriptive analytics can be:
- To expedite the critical operational and business decision-making process
- To create or modify predictive or prescriptive models on the basis of static, historical data
- To generate periodic reports, perform interactive data discovery, and perform “what if” modelling
According to Dataversity, stream processing otherwise called real-time analytics analyzes and performs actions on data as it becomes available i.e. real-time data, through continuous queries.
How Is Streaming Analytics Different?
In descriptive, predictive, and prescriptive analytics, analysts export a set of historical data for batch analysis. In real-time analytics (streaming analytics), analysts analyze and visualizes data in real time.
Use Cases of Streaming Analytics or Stream Processing
- Analysts are able to make critical operational decisions and apply them to business processes or transactions in real time and on an ongoing basis
- Stream Processing allows analysts to apply pre-existing predictive or prescriptive models
- Analysts can report current and historical data concurrently
- Streaming Analytics enables businesses to receive alerts based on certain, predefined parameters, thereby automating the data analysis process
- Real-time analytics allows marketers and analysts to visualise real-time displays or monitor dashboards in real time on constantly-changing transactional data sets such as the hourly sales of a set of regional grocery stores
Advantages of Streaming Analytics
Historical data tells us what has happened in the past while real-time analytics tells us what is happening in the present.
This makes real-time data a game changer is the data analytics field. Let’s look at a few advantages of streaming analytics.
- Data Visualization. A set of historical data can be represented on a single chart to communicate an overall point. But streaming data can be visualized in such a way that updates are received in real time to show what is occurring at that moment.
- Business Insights. Real-time analytics can effectively track the occurrence of critical business events. These events will be recorded in the relevant dashboard for analysts to view. If there is any sort of unusual activity that is reported, alerts can be triggered to inform the management, so that suitable action can be taken.
- Increased competitiveness. By tapping the potential of real-time or streaming analytics, businesses can analyze trends and set benchmarks much more quickly. This will allow marketers and analysts to use this data to stay ahead of competitors who may still be using the slower process of batch analysis.
Disadvantages of Real-Time Analytics
It is common knowledge that everything has a downside. This even applies to streaming data analytics. Here are a few reasons why certain companies may not want to use this analytics process:
- Hadoop is not compatible. Hadoop, an analytics tool that is widely used for historical big data analytics, is not designed to handle streaming, real-time data. There are more robust options available to analysts that include Spark Streaming, Apache Samza Storm or Apache Flink or MongoDB.
- Businesses must scale up analytics processes. If your business is accustomed to receiving a single batch of data insights periodically, then a constant inflow of real-time Big Data can prove to be overwhelming for business processes. Businesses therefore, must make efforts to scale up their analytics processes, to be able to process real-time data that will be received more frequently. The analytics workflows will need to be revamped appropriately.
- System failure is a real possibility. It is a common misconception that Big Data Analytics is easy to implement. However, if a business or organization is not used to handling huge volumes of data that are flowing in at rapid rates, it could lead to incomplete, or incorrect analyses — and even result in failure of analytics systems.
Streaming Big Data
The process of using real-time analytics to deliver information on business operations as and when they occur is Real-time Business Intelligence. The term “real-time” signifies minimal or negligible latency. In this process, information becomes accessible anywhere between milliseconds to five seconds after it occurs.
How Does Real-Time Streaming Analysis Bring Value To Businesses?
- Minimizing preventable losses. Streaming analytics prevents or minimizes any damage caused by events such as security breaches, manufacturing defects, customer churn, among others.
- Analyzing routine business operations. Operations such as IT systems, manufacturing closed-loop control systems, and financial transactions such as authentications and validations can be monitored in real time.
- Finding missed opportunities. The streaming and analysis of Big Data can help businesses learn from customers behavioral trends as well as immediately recommend, upsell, and cross-sell to them based on what the information presents.
- Create new opportunities. The existence of streaming data technology has resulted in the invention of new business models, product innovations, and revenue streams.
Use Cases of Real-Time Streaming Analysis
BuzzFeed uses MongoDB to analyze articles views and how they are shared to better understand how website visitors are interacting with more than 400 million news items that are published every month. BuzzFeed can then utilize these metrics to effectively devise ways to increase website engagement.
Marketing departments can effectively draw upon the potential of real-time analytics to conduct A/B and multivariate tests, keep track of digital campaigns in real-time, access results quickly, in an effort to offer personalized website experiences to users and audiences.
Future of Real-Time Analytics
The amount of data being generated by various businesses and organizations is steadily increasing. In the absence of real-time analytics, it is becoming more and more challenging to gain meaningful insights from this huge pool of data.
Therefore, open source real-time data analytics solutions are becoming increasingly popular as the future of business intelligence and analytics.
IQLECT offers a robust real-time predictive analytics platform and dedicated analytics apps to make data analysis effortless for your business.