5 practices for an efficient Cybersecurity strategy Jun 02, 2023 The number of cyber attacks continues to grow. Worldwide, according to researchers at Check Point Research, there was an increase of 28% in the third quarter of 2022 compared to the same period in 2021. Companies are obvious targets – the average number of attacks is now more than one per week. Learn more
An American Mass Media Company: Transforming Data Management Learn more Goals ● Serve as a central self-service portal and repository for specific analytical data● Allow for retrieval and analysis of data across the organization ● Cater to different stakeholders’ data needs and address varying levels of technical proficiency● Enable both quick business insights and deeper data science initiatives Challenges ● Current data exists in silos across the organization● Retrieval is not self-service ● Background and definitions require human interaction Workstream Pillars 1. Data Management The goal was to establish "data process" routines and platforms separated from "data access" methods and applications to increase readiness for analytic platform capabilities—Date Science & Machine Learning. 2. Business Intelligence and AnalyticsWe enabled self-service for data visualization using presentation layer tools like Tableau. We build a layer where minor items can be turned around quickly to enable any given business requirement. 3. Automation and Continuous Integration We established CI-CD driven data extraction, transformation, and load routines in conjunction with deploying automated testing, error tracking, and alerts for optimized performance. 4. Cloud Infrastructure We recommended establishing a Cloud infrastructure the was both economical and competitive. It provides a scalable infrastructure and high-performance ETL and Business Intelligence layers. Architecture Overview The architecture behind the Analytics Platform is made of custom components built on top of Cloud-based services and products. These components are responsible for managing a variety of data manipulation steps, such as extraction, transformation, and load, among others. They are built in such a way that they can be easily reused throughout the platform and connected to each other in order to create more complex pipelines.You can think of these components as independent services that migrate data from one place to another or that transform from one format into another format. The output of a step can be the input of another step and so on. Usually, all the pipelines are built so the data ends in a Cloud-based Data Warehouse solution, which enables businesses to query and work on top of that data—the main business goal of the Analytics Platform.