How Business Intelligence is Changing with the Use of Real-time Analytics (1)

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

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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

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• 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.

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Real-time or streaming analytics

• Real-time analytics analyses and perform actions
  on data as it becomes available i.e. real-time
  data through continuous queries.
• Analysts are able to make critical operational
  decisions and apply them to business processes or
  transactions in real time and on an on-going
  basis
• Stream Processing allows analysts to apply
  pre-existing predictive or prescriptive models
• Historical data tells us what has happened in the
  past while real-time analytics tells 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 the hourly sales of a set of regional
  grocery stores

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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.

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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.

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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.

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Use cases of real-time streaming analysis

• 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.
• 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.
• Cyber security, weather forecasts, healthcare and
  manufacturing can use real-time data analytics to
  improve the performance.

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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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