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The Evolution and Future of Analytics

Sudhir Menon Gangadharan
Cloud Evangelist and PAAS Specialist

We live in a world where things around us are ever changing.

Measurement metrics are just in time, predictive and need a lot of augmented intelligence; however, we're developing more complex mind analytics when it comes to buying patterns.

This new type of analytics can give us insight into how the customer feels and what he or she experiences.

Thus, the availability of smart information will emerge.

In the future, you may walk into a store and find one or all of the below, which can be built as solutions:
a) A robot welcoming you and taking over to interact with you using connected back end and analytics.

b) Natural language or human analytics that can automatically read your mood to ultimately improve customer satisfaction.

c) Historical data about you as a customer to help up sell or cross sell products based on your interests.
d) Automatic analysis about what you're doing to bring near real-time context of data; this will enable the retailer to build a mobile based intuitive presence or no billing architecture.
e) A personal assistant model to better serve you as a customer, empowering retailers to provide solutions to unsure customers.

f) IN product or things analytics to provide information about the product that makes things intelligent through RFID, intelligent tagging, sensors etc.

g) Discounts/coupons based on mixing historical buying patterns; post purchase analytics. 

h) Interactive dashboards that make augmented decisions about a few areas based on reviews; this would take expert reviews, phone calls, product management and more into account.
i) A store platform of grammar, syntax, semantics and data science grammar to create recurring patterns, challenges and build new solutions which are continuous in nature.

Based on the above, let's dive into different types of analytics available on the market. We'll look at how they will blend and intersect to develop more augmented applications for the future.

1) Historical Analytics

This is the traditional analytics of business intelligence focused on analyzing stored data and reporting. We would build repositories and create analyses and dashboards for historical data. Solutions would include Oracle Business Intelligence.
2) Current Analytics 
Here the analytics is measurement over current process. For example, we would measure the effectiveness of a process as it happens (business activity monitoring) using a stream that processes arriving data and analyzes it in real-time. 
3) Enterprise Performance Management

Here the objective is to focus on projections/what-if analysis with the current data and make projections for the future. An example would be a Hyperion or an EPM based solution which could help derive and plan reporting as projections. EPM today is also available as a cloud service.

4) Predictive Analytics

With the Big Data market growing, and with unstructured data adding parameters of velocity, variety and volume, the data world is moving on to more predictive analytics, with a blended mix of data. There is one world of data in the hadoop world and another in the classical data warehouse world. We can mix and match and do Big Data analytics.

Predictive analytics is more of a compass-like decision making with data analysis patterns. Oracle has an end-to-end Big Data solution from DW, Hadoop and analytics that can help develop predictive solutions.

5) Prescriptive Analytics
To extend the predictive analytics, we would also develop systems to make decisions once we have the prediction; i.e. sending emails and connecting systems as the patterns are detected. This is the basics of building more heuristics systems to make decisions about arrived patterns.
6) Machine Analytics 
Every device and machine is going to generate data. Machine analytics is a blended form of data that can be embedded into the standard source to enhance and improve the overall data pattern. Oracle provides IOT CS as a solution to connect, analyze and integrate data from various machines and enrich new applications like  ERP, CRM and more.

7) AI Based Analytics

AI or deep learning is the next gen analytics pattern where we can train the systems or any entity to think and then embed the analytics pattern in the solution.

8) IORT / Robotics Analytics
With Robots / Bots and personal assistant complementing solutions, there are a lot of patterns of thinking and execution distributed to multiple systems. IORT or robotics analytics is a new branch that will focus on how we can analyze the pattern from semi thinking devices.
9) Data Science as Service 
A new branch where the analysis goes deeper in terms of algorithms and storage and is also more domain-driven. Even though data science is used as one branch in analytics, you will see a lot of analytics development. Data scientists who specialize in identifying patterns will go a long way to build patterns that are more replicable.
10) Integrated Analytics
In the future, we can form an integrated view of the above. This could be ONE IDE and you would derive patterns based on business need and use case. Today, we have a fragmented set of tools to manage analytics and it would slowly get integrated into one view.

Oracle has solution at different levels; most of them are also available as a cloud service (Software as a Service, Platform as a Service).

It's imperative to build the right mix of solutions for the right problem and integrate these solutions.

  • Historical perspective you would use --> Business Intelligence 
  • Current processing  -->  Streaming (event processing) and Business Activity Monitoring
  • Enterprise performance management  --> Hyperion
  • Heterogeneous source of data and also large analysis of data --> Big Data Solution
  • Predictive and Prescriptive analytics --> R language and Advanced Analytics
  • Machine related --> IOT Solutions and Cloud Service
Oracle University provides competency solutions for all the above and empowers you with skill development and well-respected certifications that validate your expertise:

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Comments ( 1 )
  • erptree Monday, January 16, 2017

    Very well explained about the points mentioned in the blog and very good analysis about the programming. I personally like the point Data Science and Service.

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