How Business Intelligence is Changing with the Use of Real-time Analytics

How Business Intelligence is Changing with the Use of Realtime Analytics

Data and analytics  Data is growing faster than ever before and by the year 2020, about 1.7 megabytes of new information will be created every second for every human being on the planet.  We are seeing a massive growth in video and photo data, where every minute up to 300 hours of video are uploaded to YouTube alone.  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 a report, the market for analytics will possibly reach USD 9.50 billion by 2023 at a CAGR of 20.50% during the period.

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 Analytics - Predictive Analytics - Prescriptive Analytics

Descriptive Analytics  Descriptive analysis creates a concise story from a segment of historical data. The story will ideally have an overall theme.  Descriptive analytics enables analysts to understand past events and also allows them to build predictive and prescriptive analytical models.

Predictive Analytics  Predictive Analytics analyzes trends in data to predict future events and occurrences.  Analysts showcase likely scenarios with the help of Data Mining, Statistics and Machine Learning.

Prescriptive Analytics  Prescriptive Analytics seeks to inform data analysts about what to do.  In this process analyst analyses the data and prescribes real-world decisions that businesses can adopt.

Real-time or streaming analytics

 Real-time analytics analyses and perform actions on data as it becomes availab real-time data through continuous queries.  Analysts are able to make critical operational decisions and apply them to busin processes or transactions in real time and on an on-going basis  Stream Processing allows analysts to apply pre-existing predictive or prescriptiv models  Historical data tells us what has happened in the past while real-time analytics us what is happening in the present.  Streaming Analytics enables businesses to receive alerts based on certain, predefined parameters, thereby automating the data analysis processes.  Real-time analytics allows marketers and analysts to visualise and monitor dashboards in real time on constantly-changing transactional data sets such as hourly sales of a set of regional grocery stores

Advantages of real-time analytics Data Visualization  Streaming data can be visualized in such a way that updates are received in real time to show what is occurring at that moment.  It makes easier to visualize the data in real-time so that quick actions can be taken to improve the business performance.

Business Insights  Real-time analytics can effectively track the occurrence of critical business events.  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.

Streaming big data and BI  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 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 etc.  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

ďƒ˜ BuzzFeed uses real-time streaming analytics 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. ďƒ˜ 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. ďƒ˜ Cyber security, weather forecasts, healthcare and manufacturing can use real-time data analytics to improve the performance.

Final words  The prominence of real-time and streaming analytics is increasing every year.  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.

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