RLD – Opportunities and Beyond Last year October Broadcast Audience Research Council (BARC) – sole TV Viewership Monitoring Entity in India – announced the release of Respondent Level Data (RLD) for Broadcasters. While it was accessible to Media Agencies before that, releasing the data to broadcasters was new. Before I explain what RLD is let […]

This article is for all Media Planners, Analysts, and Data Engineers dealing with digital campaign management platforms like Google Ads, DV360 or Meta. We have extensively worked on the reporting side of these platforms, and or experience says that creating a scalable data model for your reporting teams can turn out to be quite challenging. […]

Business Case: To understand spending patterns in advertisements through the four mediums of TV, Radio, Print and Digital across the two sectors of Retail and Service. Solution: Analyzing and comparing the spending across mediums and diving deeper into the sectors and the product itself. Technology used: Power BI and Excel Data Source: The report is […]

Business Case: Understand advertisement spends through the three mediums of TV, Radio and Print along with key metrics like GRPs, NGRPs, SOVs across multiple media channels and Dayparts. Solution: Analyzing the spending across mediums and diving deeper into the sectors and the product itself. We also want to understand how the key metrics impact these […]

Business Case: To analyze operations and volumetrics related to insurance premium and derive appropriate trend analysis from the data. To understand and dig deeper into the different types of policy. Solution: To create an overview that spans information about policies, premiums and gives a trend analysis in terms of relevant KPIs. Technology used: Power BI […]

As a DevOps engineer, the choice of cloud platform can significantly impact both workflow and project success. Amazon Web Services (AWS), Azure, and Google Cloud Platform (GCP) are three major players in the cloud services market, each of them offers a unique set of features and pricing structures. In this blog post, we’ll explore these […]

Posted on May 11, 2019 by Hrishikesh Joshi in AI | Media | Entertainment A few months back a paper published by Anacondas “Why Your Business Needs an AI Platform” caught my attention.     The Value creation Matrix in the paper describes the impact of AI ($billion) verses share of AI impact in total impact from analytics. What surprised […]

OVERVIEW: Communicating insights through data visualization is not easy, even with the best Business Intelligence Tools at our disposal. Many a times we find ourselves struggling to communicate clear-cut insights. One such use case is Customer or Market Segmentation problems. Scatter plots are often the go-to visuals in this case. However, they fail miserably if […]

Business Case: To analyze and tell a story about viewership data for the particular channel. Understanding and viewing trend patterns across markets, channels and content while also comparing them. A proper deep dive into every aspect of the necessary KPIs and GRP ratings.   Solution: To create a consolidated view of market, channels and performance […]

Posted on Aug 16, 2019 by Rhishikesh Joshi in AI | Media | Entertainment Problem: Let us start with explaining you in brief the business problem in Indian Media Industry that we intend to tackle. There are majorly 4 stakeholders viz. Advertisers, Media Agencies, Broadcasters and Broadcast Audience Research Council (BARC) – central rating agency. Advertisers hire Media Agencies to […]

Over the years we have had the opportunity to setup data lakes for our clients. We have worked on key cloud platforms like Azure, AWS and GCP. In this blog I am here to talk about the key considerations that you consider when you think of building a DataLake. I have consolidated my learnings into top 4 Points that talk about 4 key stages of DL development. 1.Environment Setup 2.Schema Setup 3.Policy Definition 4.Data Distribution Environment Setup: When you are in the whiteboard phase of your data lake design, ensure that you have provisioned your infra for development as well as production. A few things that you might want to keep in mind at this stage are: Selection on Cloud Service Provider – Cost, Features and Adaptability concerns – https://venanalytics.io/aws-vs-azure-vs-gcp-perspective-on-features-and-costing/ Data Sources – What is the nature of your data source? Structured/Unstructured/Semi-structured? Sizing – What would be the size of your dev and prod envs? Costing – How much is data ingress, egress, storage and backup costs? Data Duplication – What percentage of prod data will be held in your dev env? Retention Policy – How often do you plan to prune the dev data? Data Validation – What qualifies for a quality data? Define quality KPIs Answering these key questions will direct you in setting up your environments. Schema Setup: Before designing the actual schema, you need to finalize the type of data model you intend to build that will best solve your business case. There are broadly 3 types of Data Models – Dimensional Data Model – Data stored as Facts and Dimension Tables 3NF Data Model – Data stored in highly normalized tables Data Vault – Data stored in Satellite tables and connected by links Here’s a video that will help you understand these data models in detail – https://www.youtube.com/watch?v=l5UcUEt1IzM Once the schema type is finalized, only then start designing each component of the schema. At this point ensure that the right business stakeholders as involved for approvals and suggestions. Define Policies: Database maintenance is as important as the setup itself. The robust policies that we define at the beginning will go a long way in delivering quality data to the right people at the right time. A few policies can be: Access management policy Data purging and backup policy Policy pertaining to Database performance Time out policy Quality Control Defining a few SOP’s at the start can also go a long way. For example, if the end user requests to onboard a new data source – Important things to define would be TAT, Owner, Approval Matrix, etc. Data Distribution: In order to distribute the right data to the right people for the right duration is imperative. Reporting data can be distributed as data models rather than raw data itself. For example, team members can connect to published Power BI models thus saving hours in data modelling. This also ensures that the data quality is centrally governed and so is access control. Here’s a pictorial representation of the difference between the old approach (Direct Access to data base) vs access to Power BI Data Model. A screenshot of a computer Description automatically generated We believe that on an average we save up to 2-3 hours of time per analyst that he would spend in understanding and re-creating the data model. The 4 key points mentioned above provide you with a basic framework to initiate your DataLake Project. The above framework is ideal for reporting needs of a midsize organization.