Big Data Analytics for OTT

Published in

mobiotics

12 min readMay 5, 2021

Big Data Analytics is a relatively new concept that provides many possibilities for businesses to improve customer experience. It can also be used in OTTconjunction with video analytics software to analyze how customers are watching videos and what they’re viewing the most. With this information, companies can make adjustments accordingly and offer more relevant content to viewers.

What is Big Data Analytics?

Big Data Analytics is the process of examining large data sets containing a variety of data types — i.e, Big Data — to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information.

Big Data Analytics is an advanced analytic technique against very large, diverse data sets that include structured, semi-structured, and unstructured data, from different sources and in different sizes. It is a term that is applied to datasets whose size or type is beyond the ability of traditional relational databases to capture, manage and process the data with low latency.

Characteristics of Big Data Analytics.

High Volume, High Velocity, High Variety, Mobile, Social, Internet of Things (IoT), Artificial Intelligence these characteristics are driving data complexity through new forms and sources of data.

Analysis of big data allows analysts, researchers, and business users to make better and faster decisions using data that was previously inaccessible or unusable. Businesses can use advanced analytics techniques such as text analytics, machine learning, predictive analytics, data mining, statistics, and natural language processing to gain new insights from previously untapped data sources independently or together with existing enterprise data.

Why is big data analytics required?

The term big data was introduced by very large databases (VLDBs) which were managed using database management systems (DBMS), big data falls under three categories of data sets — structured, unstructured, and semi-structured.

Structured Data sets — These are types of data sets that comprise data that can be used in its original form to derive results. The examples include relational data such as employee salary records.
Unstructured data sets — These are types of data sets which are having proper formatting and alignment. Examples of Google search result outputs include human texts.
Semi-Structured data sets — These are types of data sets that are a combination of both structured and unstructured data. The examples include XML data.

Key Technologies and Tools in Big Data Analytics

OTT big data analytics is a process that helps in extracting information by analyzing different types of data sets. This also helps in discovering hidden patterns, market trends, hidden patterns. This analytics helps in making organizational decisions making. There are many tools and technologies involved in OTT Big data analytics.

Born Digital Data — It is data information that has been captured by the digital medium. This kind of information has an expanding range since the system keeps on collecting different types of information from users. Examples of digital data include GPS Tracking, Web Analytics, Cookies.
Born Analogue Data — It is a type of information which is in the form of pictures, videos, and other similar formats which relate to physical elements of the world it is termed analog data.
Data Acquisition — Data acquisition has two components which are identification and collection of big data Identification of big data is done by analyzing the two natural formats of data — born digital and born analog.
Non-relational Databases — The databases which store these massive data sets have also evolved in how and where the data is stored. JavaScript Object Notation or JSON is the preferred protocol for saving big data nowadays. Using JSON, the tasks can be written in the application layer and which allows better cross-platform functionalities.
Apache Hadoop — Data Processing & Hybrid Data Storage Systems — Apache Hadoop is a hybrid data storage and processing system which provides scalability and speed at reasonable costs for mid and small-scale businesses. Hadoop can be run using readily available hardware which has sped up its development and popularity, significantly.
Data Mining — Data mining is a process that is used to extract usable data from a larger set of raw data. It implies analyzing data patterns in large batches of data using one or more software. The objective of Data mining is to use a single data set for different purposes by different users. Data mining can be used for reducing costs and increasing revenues.
In-memory Database Systems — IMDB system helps to overcome the hurdles that the big data processing data takes time by traditional databases to access and process information. IMDB systems store the data in the RAM of big data servers, therefore, drastically reducing the storage I/O gap. VoltDB, NuoDB, Apache Spark are examples of IMDB systems.

Big data analytics for OTT Platform

The key to great OTT service is to start with an understanding of the customer and responding to their needs promptly — whether it is for content, the user experience, or the business model. Since the ‘viewer’ lies in the heart of the business, OTT platform owners have to look at big data and analytics to enable actionable learning of customer behaviors and manage business rules.

Understanding customer churn — Since there are many OTT players the choice of providers also increasing for the customer. Customer churn is a real problem to solve and maintain profitability in the OTT universe. Big data analytics gives OTT providers the capacity to aggregate disparate data sets and develop a 360-degree customer view. OTT providers can use more accurate churn prediction models and use real-time and historical data, user data and user behavior, and other associated data to identify subscriber clusters with a high risk of churn. They also get detailed insights into the main causes of churn and can proactively take measures to solve this problem.
Crossing the content chasm with personalization — Personalized, relevant, and contextual content is what OTT viewers demand. OTT has now become mainstream, and the viewers want a lot of content on multiple services. with new streaming services that come online almost every other week, there is more content today than ever has been produced in history. Deep big data analytics also gives OTT providers deeper audience insights. It helps them understand genres of content that are in high demand, what content the audience demands at what time of the day, when do they pause, or what do they skip. Based on this data, OTT providers can make informed decisions on content dissemination.
Improve customer experience — An understanding of the territory-specific variation of user behavior and gaining insights into device demographics and platform infrastructure becomes essential as OTT providers look at wooing international audiences. Additionally, gaining granular insights into real-time across live and on-demand services also becomes essential to improve customer experience and stay on top of the OTT game. Big Data and Analytics play a significant role in providing deep insights into all the influencers of customer experience by looking at all the data intelligence. Analytics helps in getting a complete and multi-dimensional understanding of viewer experience and gives OTT providers information granularity to benchmark things that matter most, identify disruptions that impact engagement, and make smart business decisions without ambiguity influencing it. Using behavior-based audience insights and fan analytics enables OTT providers to profile the viewers accurately.

Big Data and Analytics are transforming the world of OTT by enhancing the user experience through more accurate and personalized recommendations. This insight gives more accurate predictions, understanding how to push and manipulate this data, and using the right analytics can help OTT providers with insights they need to design the best approaches that lead to customer satisfaction, customer retention, and profitability.

How applying data analytics helps OTT businesses

Helps to identify trends to stay competitive
Increases the productivity and dedication of staff in handling core tasks and issue
Promotes low-risk data-driven action plans
Identifies and acts upon opportunities
Promotes low-risk data-driven action plans
Validates decisions
Facilitates sensible recruitment of talent
Helps in selecting the target audience

Process in Data Analysis

Data Analysis is a process of collecting, transforming, cleaning, and modeling data with the goal of discovering the required information. The results so obtained are communicated, suggesting conclusions, and supporting decision-making.

Data Analysis Process consists of the following phases that are iterative in nature

Data Requirements Specification — It is the process that is used to identify, prioritize, precisely formulate, and validate the data needed to achieve business objectives.
Data Collection — Collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications.
Data Processing — Big data processing is a set of techniques or programming models to access large-scale data to extract useful information for supporting and providing decisions.
Data Cleansing — Data cleansing is also called scrubbing or appending is the procedure of correcting or removing inaccurate and corrupt data. This process is crucial and emphasized because wrong data can drive a business to wrong decisions, conclusions, and poor analysis, especially if the huge quantities of big data are into the picture.
Data Analysis — Data Analysis is a process of collecting, transforming, cleaning, and modeling data with the goal of discovering the required information.
Communication — The results of the data analysis are to be reported in a format as required by the users to support their decisions and further action. The feedback from the users might result in additional analysis.

Streaming Data Analytics

OTT Streaming services have made it possible for consuming content continuously without uploading the whole file. This type requires receiving and sending millions of data at any given time. Continuos transitions of data were named streaming, and it exists in various forms.

Streaming analytics is also called real-time analytics which is a type of data analysis that presents real-time data this allows to perform simple calculations with it. Working with real-time data requires different mechanisms as compared to working with historical data. It is called Stream Processing in this type it uses a specific type of processing large amounts of constantly updating data.

Mainly this type of analytics works on data flows without complex analytics tasks. The importance of this type of streaming analytics is to present the user with up-to-date information and keep the data updated regularly.

Batch Processing

In traditional analytics like Business Intelligence (BI) methods and technical infrastructure. Business Intelligence is a practice of supporting data-driven business decision-making. which mainly focuses on historical data. Historical data will be stored as a stable unit that can be divided into pieces. In the process of ETL and warehousing, the data is moved and processed in batches. A set of batches has to be queried by a user or a software program. Hence the system would understand when, how and which pieces to fetch data. and how to process it and present it to the end-user.

Stream Processing

Streaming is a moderately new concept. here streaming processing deals with data streams. A data stream is a constant flow of data, which updates with high frequency and loses its relevance in a short period of time. an example of this could be transactional data information from LoT devices, hardware sensors, etc. In this process data streams have no beginning or end, they can't be broken into batches. So there won't be the time required when data can be uploaded into storage and processed. Instead, data streams are processed on the fly. The application logic will be in a vicious circle because queries and processing are done continuously. data streams can be a source of information to collect historical data. in this scenario, an additional warehouse would be stored and this data can be formatted and further used in BI.

To carry concurrent processing of multiple streams, we need specific hardware and software. Stream processing is held by systems called Event Stream Processors (ESP) which are able to ingest data streams and process them with a small response time and no data loss.

Tools used for Streaming Processing

The key to data streaming for building a streaming analytics platform is to have dedicated technologies that make stream processors capable of fast computation and concurrent work with multiple data streams.

*Apache tools — Flink, Strom, Spark, and Kafka

Apache Kafka can be also integrated with Apache Hive, warehousing solutions, and Hadoop for batch processing of the stored data. Or also it can be used with Apache Spark, a big data processing engine. Both can also be used as an ETL tool or a batch processor integrated into Hadoop.

*Amazon tools — Kinesis, Kinesis Streams, and Firehose.

Kinesis offers a wide list of possible integrations with Apache services like Spark and Kafka. Kinesis Streams is a scalable and customizable solution for processing and analyzing data streams. Firehose enables you to integrate data streams into existing BI tools and analytical interfaces or a warehouse.

Real-time analytical instruments

Integration of data streams into data platforms is a significant step in creating your own real-time analytics solutions. In this stage, you will be able to receive the data and perform calculations for analysis. The users can view the information, create a visualization, and manage within dashboards.

Azure Stream Analytics — is a stream processing platform by Microsoft paired with its analytical interface Power BI.
Power BI — It is a general-purpose, business intelligence tool that can be used both for batch and real-time analytics.
Google Cloud Stream Analytics — It offers similar capabilities in terms of stream processing, as their product includes a dedicated engine for data ingestion, processing, and analysis. These operations with data can be handled by three instruments — Pub/Sub, Dataflow, BigQuery.
Oracle Stream Analytics — It is a cloud-based platform that offers an all-in-one solution for stream ingestion, processing, and visualization.
IBM Streaming Analytics — IBM Streaming Analytics is available for building real-time analytical applications. It’s powered by IBM Streams, a data platform for stream processing, data ingestion/transformation, and analysis.

Why should you consider streaming analytics?

It helps you in Creating targeted pricing strategies, Detecting fraud in real-time, Building customer loyalty and capturing market share, Finding operational efficiencies.

Advantages of Big Data Analytics

There are a lot of innovations in technologies that are changing rules when it comes to big data. Advanced application systems greatly reduce analytics time, giving companies the ability to make speedy decisions that help increase revenue, reduce costs and stimulate growth. This offers a competitive advantage to the brands that are able to work faster and target their consumers more effectively.

Here are some of the ways you may get an advantage with Big Data & Video Analytics are as follows.

Focused And Targeted Campaigns
Customer Acquisition And Retention
Innovative Products
Identification Of Potential Risks
Complex Supplier Networks

Introducing our Big Data & Video Analytics Software called — vAnalytics

vAnalytics — Big Data Analytics for OTT platform.

Here is the simple flow of vAnalytics for the OTT platform

vAnalytics offers — Custom OTT Big Data and Video Analytics, which is a Scalable, flexible Big Data Analytics Service to unify data sources and to process, query, visualize, report, alert, and mine OTT Data.

Here are the features of our vAnalytics product.

Unify different OTT data sources to form a Data lake including RDBMS, Analytics Data, Event Streams, Logs Files, and any other types.
Generic Query Layer and API for creating data tables, issuing general and custom queries, generating reports, and computing aggregate metrics.
Analytics SDK for multiple device platforms and Event Streaming layer to process and route data.
Setup Batch Jobs for reporting in the desired format and also for creating intermediate tables for visualization.
Set up thresholds for different data points to get Alerts about critical issues and events that need attention.
Detailed Metrics and Reports including queries, alerts, and data storage details.
Scalable and reliable SAAS built on AWS Cloud and Lambda. 24x7 Maintenance and Monitoring.

Coming to an end.

The world is forwarding technology towards a more connected future, and OTT Big data and Video Analytics play a big part in the automation and development of AI technologies. Many companies are already using Machine Learning processes for greater accuracy in delivering their services. As the world with technologies around the globe become more synchronous and interoperable, Big data will be the significant one to which connects them together. Therefore, companies using big data solutions need to keep up with its evolving nature while those still reluctant to invest should rethink their organizational policies.

Looking to launch Big Data Analytics for your organization? Mobiotics can get you all of that, Please explore our vAnalytcs offering — vAnalytics

Click here to book a 15 Min demo with us to know how vAnalytics works.

For more information about our product and services please feel free to get in touch with us

Call us at +91 9620209869, or write an email to the sales@mobiotics.com