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Media has come a long way from the days of “12 channels, take it or leave it.” As technology has transformed consumer expectations, media has become a two-way channel with a massive amount of user data to inform personalization, sales and retention strategy, and R&D.
Cloud technology is ideally suited to collect, process, analyze and store the data driving enhanced consumer experiences. With ever-increasing competition and alternative streams, content creators in the media and entertainment (M&E) sector are under constant pressure to produce and distribute original content more frequently, across multiple channels, and more quickly and reliably than the competition.
Statista reported that people spent 482.5 billion hours live streaming in 2021, and the market is expected to reach $223.98 billion by 2028. Gaming and esports are perhaps predictably the most popular live-stream subjects, but we also see that 80% of live-stream viewers would rather watch than visit a brand’s blog.
Back in 2018, Cisco had predicted that video streaming would make up 82% of Internet traffic by 2022 – and that was before the pandemic hit. Fast forward to 2021, and Netflix had amassed a whopping 221.84 million subscribers. It’s not just boredom driving the trend. Video accessibility is playing a part in the increased popularity of streaming, with people watching on multiple devices.
The improvement in video quality is an important driver, as well, not only for consumers but also for B2B/B2C. Forbes sees live streaming as a way for brands to breach the “divide between the physical and digital worlds.” It provides opportunities for two-way communication, brand engagement, and introducing your brand to prospective buyers who might not seek you out otherwise.
As anyone who has sat in a glitchy video conference can attest, video and sound quality are essential to delivering a positive viewer experience, whether you are a movie company, a content creator, an “influencer” or a marketer.
The cloud powered platform is helping media brands stay agile so they can quickly recognize and act on new opportunities to connect with audiences. One of the challenges for media content creators is consistently delivering reliable content, regardless of demand. Cloud-native technology is designed to automatically adapt to volatility in demand, providing consistent flexibility and scalability. It can receive, manage, and deliver huge amounts of digital content in a cost-effective, agile manner.
Hyper-personalization of TV Media
By 2025, consumers will expect white-glove service for all, or hyper-personalized "care of one," as McKinsey puts it, whether that means the freedom of choose-your-own bundles or enhanced service of resolving outages and slowdowns before customers are even aware there’s an issue.
Back in the day, traditional M&E companies had a stranglehold on the distribution of cable and broadcast television. In recent years, however, new trends in the way television is broadcast and low-cost OTT (Over-the-Top) companies that sell/deliver streaming audio, video, and other media over the internet directly to consumers have challenged the traditional models.
Transformation of Development & Go To Market
As demand for high-quality content creation increases, so does the demand for ways to stream the content across multiple channels, including Smart TV or streaming devices, mobile devices, internet streaming, or gaming. This is transforming the development of OTT applications and putting pressure on the time to develop, test and go to market.
In order to take full advantage of the new technology, M&E companies need to migrate to cloud technology, and that requires planning and careful execution. Most configuration management databases (CMDBs) may not be robust enough to make contextual decisions about prioritizing application migration, which can lead to project delays and budget overflow.
GlobalLogic has several OTT accelerators that can help businesses to test and roll out streaming services at a faster pace, including the below.
OTT Digital Accelerator
GlobalLogic has developed the OTT Digital Accelerator, a platform-agnostic/ device-agnostic application testing solution that provides developers with the opportunity to test applications on the most common consumer streaming services, as well as multiple devices, including gaming consoles, set-top boxes, smartphones, PCs, and tablets. This allows for the development of best practice frameworks and a faster go-to-market.
OTT Test Lab
In addition, the GlobalLogic Cloud-native OTT Test Lab enables developers to schedule automated tests for their OTT channels and applications on any device, in any location, at any time. The Test Lab has a repository of over 3000 devices, which provides access to developers without the need to purchase the device, manage inventory or juggle testing schedules.
It also facilitates testing as soon as the channel development project begins, and jump-starts continuous integration/continuous delivery (CI/CD) pipelines. Developers enjoy a simple KPI setup and viewing with easy app adjustment capabilities based on results.
Revolutionizing Business As Usual for Media Brands
As the global pandemic proved, business as usual is a thing of the past. The media and entertainment sector needs to innovate and evolve to stay competitive.
For OTT players, this means personalizing and monetizing content and scaling for a global audience. This will require the development of high-quality M&E software, from the front-end UX to the back-end processes. It also means highly personalized, white-glove service for each and every customer.
In addition, OTT players will require agile, scalable solutions for:
- OTT multi-platform engineering and multi-application development
- Content discovery & recommendations
- Content monetization
- Back-end services
- Billing & operations
- Subscriber management
For broadcasters and studios, this means transitioning to the cloud, building new tools, and automating processes and workflows. It means distributed content creation that can be shared across multiple platforms, including smart televisions, mobile devices, and internet and gaming systems, regardless of the operating system.
For ad tech providers, it means adopting modern ad channels, formats, and even business models. Placing the same ad across multiple channels will no longer be an effective business model. Instead, ad tech providers will need to develop targeted ads, develop end-to-end solutions, including dynamic ad placement and video ad platforms. Real-time bidding systems and ad exchange & server solutions will help providers compete in the more challenging, dynamic marketplace.
For digital marketing, live stream interactions will see more integration with messaging apps and will become a valuable marketing tool. Video streaming will become the de facto digital marketing platform to enhance the customer experience by helping consumers troubleshoot product/service issues, and IoT video streaming will dominate the Home Monitoring Segments, as well.
For journalism and publishing, we expect to see an upward trend in subscription and membership models with new-age workers dominating this space.
Conclusion
Media and entertainment businesses must innovate now to remain competitive. Cloud-native technology is helping brands realize this need thanks to its flexibility, scalability, and support for a wide range of content creation. Cloud powered technology can adapt quickly to unpredictable viewer use, seamlessly switching resources to ensure a consistent viewing experience.
Consumers will continue to expect and demand white-glove, personalized service, which includes accessing streaming across a variety of platforms. The explosion of OTT providers means traditional cable and television providers need to proactively anticipate customer needs and wants, or they will be left behind by new players in the market.
All signs point to continuing highs for OTT, wherein providers will have to work overtime to find ways to retain popularity and provide niche, original and high-quality content.
Is your brand positioned to capitalize on new opportunities as they arise?
GlobalLogic helps media and entertainment companies across the globe leverage sophisticated technology to deliver more engaging, data-driven viewing experiences. Get in touch today and see what doors cloud adoption can open for your media business.
In my previous post, “Security Training for the Development Team,” I shared the experience of building a security training program for the development team.
In this part of my Secure Engineering blog series, you’ll learn about another essential step of securing engineering: threat modeling. This blog provides an overview of threat modeling. It’s an important method, and some companies use its basic approaches to secure platforms and applications.
More recommended reading on Secure Engineering:
- Secure Development Lifecycle: Importance & Learning
- Security Training for the Development Team
- Security Requirements For The Development Team
Threat modeling is a process of identifying and mitigating potential threats. You can apply it to software, networks, business processes, and real-life situations. In the context of software security, it not only ensures security while developing the software but also develops the security culture and mindset of the team.
Traditionally, threat modeling is a complicated and time-consuming process that is document-centric and manual, which is why most teams try to avoid threat modeling. But understanding and mitigating threats are becoming a critical part of the software development process and can no longer be avoided. Furthermore, if executed correctly, threat modeling pays for itself by reducing the number of security bugs, security patch releases, and attack possibilities.
Teams should iteratively complete threat modeling during the design phase, although there isn’t a perfect threat model; threat modeling is never “done.” The team must find the right balance for threat modeling according to the security requirements and threat environment to create a ‘good’ threat model.
A team can start threat modeling as soon as the basic design is ready. Generally, threat modeling has five steps (DCTMD):
- Define scope
The team should first define the scope of threat modeling. Teams can use threat modeling for the entire platform, product, sub-system, or a single module or feature. Our recommendation is to start at the single module or feature level as the team does the detail designing because completing threat modeling for the complete platform or product can be overwhelming. Teams can use module or feature level threat models to easily build the platform or product threat model.
- Create DFD
The data flow diagram will help the team visualize data flow and trust boundaries. Without a good DFD, it is difficult to understand all threats, and the team can miss some crucial threats. DFD details and complexity can be dependent on the security requirements and risks.
- Threat Analysis
Once a team has DFD(s), they can start with identifying and analyzing threats. There are many different threat modeling methods. Some of the most used include:
- STRIDE
- PASTA
- LINDDUN
- CVSS
- Attack Trees
- Persona non Grata
We recommend using STRIDE as it is easy to use, most mature, and focuses on identifying mitigation techniques and threats.
| Category | Definition | Applicability |
| Spoofing | Pretend to be someone or something else to gain access or trust. | Identity and Process |
| Tampering | Deliberately modifying data. | Process, Data storage and Data flow |
| Repudiation | Not able to track users’ actions. | Identity, Process and Data storage |
| Information Disclosure | Leakage of private and confidential information. | Process, Data storage and Data flow |
| Denial of Service | Making system or information not available | Process, Data storage and Data flow |
| Elevation of Privilege | Elevate the system access | Process |
Not all threats are equal, so a team needs to prioritize them. We recommend using likelihood and impact for this:
- High Priority Threats = High Likelihood and High Impact
- Medium Priority Threats = Medium Likelihood and Low Impact
- Low Priority Threats = Low Likelihood and Low Impact
- Mitigation
Once a team identifies all threats, the next step is to mitigate each valid threat. A simple STRIDE table can help a team with it.
| Category | Control |
| Spoofing | Strong Authentication (like MFA) |
| Tampering | Encryption of Data at Rest, Data at Motion and Data at Use |
| Repudiation | Logging, Tracing and Monitoring |
| Information Disclosure | Encryption |
| Denial of Service | Site Reliability |
| Elevation of Privilege | Authorization |
- Documentation
Documentation is the last step of effective threat modeling. A team doesn’t need to create extensive documentation, but a team should create documentation they can refer to in future threat modeling or during design changes. Without good documentation, a team may need to complete another round of threat modeling. We recommend documenting the following:
- Valid threats
- Test cases for valid threats
- Mitigation details
Threat modeling is a specialized area, but the above details can help a team to have a basic understanding of what’s involved and begin the threat modeling process. Please feel free to contact us if you would like assistance with threat modeling.
Data platform challenges with data processing and analysis are many. Today, data engineers are using the Domain-Driven Design (DDD) through the Distributed Data Mesh architecture to help overcome these obstacles. The Data Mesh approach includes four important pillars: domain-driven data ownership, data as a product, self-serve infrastructure as a platform, and federated computational governance.
It’s crucial to thoroughly understand these components of Data Mesh before implementing them into an organization. Learn about Distributed Data Mesh, the current analytical data platform challenges, and how Data Mesh can overcome them by creating cloud and on-premise solutions through data storage, self-serve tooling, and more.
This paper addresses specific building blocks for approaching a data platform journey for architecting. By understanding the essential elements of architectural design considerations, organizations can aid their decision-making process when evaluating value realization outcomes associated with modern data platform goals.
Read more to learn how to apply these proven methodologies to improve at-scale benchmarking, cost modeling, and operational efficiency. Building and operating a data platform has never been more accessible with the right tools, modern technology, and efficient workflows.
An Unforgettable Experience at Adaptive Spirit 2022
What does it mean to adapt? In business, we have unlimited examples from the past two years. For the U.S National Paralympic Ski and Snowboard Team, they have a lifetime of exceptional adaptations. I recently had the opportunity to meet some of the athletes firsthand—fresh from their stellar performance at the 2022 Winter Paralympics in Beijing. I heard their stories of resilience and adaptability; I witnessed their athleticism that knew no mountain was unsurpassable, and I left with a deep appreciation and respect for them as well as for the telecom industry who makes this event possible—every year for 26 years now!
Adaptive Spirit, held annually in Vail, Colorado, is a rare combination of networking, giving, and business; it’s the premier networking event for Telecom which also raises funds that allow the U.S. Paralympians to remain the top adaptive ski team in the world. In short, it’s good for business and good for the Olympians!
GlobalLogic was honored to be a Silver Sponsor at the 26th annual Adaptive Spirit event. Yes, I connected with customers and vendors throughout the interactive agenda. Indeed, I built business relationships. But most importantly, we gave back. Over the last 25+ years, Adaptive Spirit, a non-profit trade association, has raised millions of dollars for these athletes. This was GlobalLogic’s—and my—first time attending the event.
Getting to Know the Athletes Who Had Just Returned from Beijing
The timing of the event could not have been more perfect. The Paralympic Ski and Snowboard Team had just returned from Beijing and the 2022 Winter Paralympics with a host of medals. As part of the three-day event agenda, we got to interact with them socially and on the slopes. I can personally say now that I have met and mingled with a gold medalist!
Everyone was thrilled as the athletes showcased their amazing talents throughout the weekend. On Friday, they offered pointers and encouragement during a race clinic, followed by a Youth Race for kids 15 and younger who were able to ski alongside the Olympic athletes. And Saturday was Race Day down the Golden Peak Race Arena course.
Interacting with the athletes was definitely the high point of the event and hearing their personal stories of overcoming daunting odds was truly inspiring. For example, the story of Oksana Masters is one of adaptation and resilience. She was born in Ukraine after the Chernobyl nuclear disaster with several radiation-induced birth defects. She was adopted by an American speech therapy professor and began training for competitive sports at the age of 13, winning medals for rowing, cross-country skiing, and cycling at multiple Paralympic events. She brought home three Gold medals and four Silver medals from the 2022 Winter Paralympics!
Masters’ teammates have similarly motivating stories, such as Aaron Pike, who was shot in a hunting accident at the age of 13 and still competes in cross-country skiing. He recently finished second at the 2022 Boston Marathon. Another such story is Ian Jansing who was born with cerebral palsy but went on to become a ski racer and competitor at various Paralympic Games and championships.
Image from L to R: Ed Clark, AVP Client Engagement, GlobalLogic | Ian Jansing, USA Paralympic Athlete | Maneesh Muralidhar, AVP, Client Engagement, GlobalLogic
Image from L to R: Oksana Masters, USA Paralympic Athlete | Poorvi Tikoo, Freshman, Hopkinton High School | Sameer Tikoo, SVP, Communication Services BU, GlobalLogic
Image from L to R: Poorvi Tikoo, Freshman, Hopkinton High School | Aaron Pike, USA Paralympic Athlete | Sameer Tikoo, SVP, Communication Services BU, GlobalLogic
A Movement That Matters—Join Us
The Adaptive Spirit Annual Event lived up to its promise. On the business front, we had the opportunity to network with the world’s leading communication service providers (CSPs) and network equipment providers (NEPs). We were able to share our over twenty years of experience in Communications and our portfolio of cutting-edge solutions in the telecom industry using 5G, IoT, AI/ML and more.
But most importantly? We were able to recognize, reward, and support individuals who didn’t allow challenging circumstances to stop them from fulfilling their dreams, and, instead, used these circumstances to inspire others.
Adaptive Spirit is the perfect complement to our Corporate Social Responsibility (CRS) program, The GlobalLogic Foundation, where we focus on Education, Environment, Health & Wellbeing, and Community Service. We also invest in Human Capital, cultivating a multi-faceted culture with DEI at the forefront of our efforts. In fact, our CEO, Shashank Samant, signed the CEO Action of Diversity & Inclusion Pledge, joining over 2000 CEOs committed to advancing diversity and inclusion in the workplace.
As we work towards making a long-term, positive impact across the globe, we look forward to attending and sponsoring the Adaptive Spirit Annual Event for years to come. Co-chair and founding member Steve Raymond told Light Reading that Adaptive Spirit’s success has taken the “work of countless people who felt passionate about the Paralympic movement,” and we encourage other businesses to join us at next year’s event. Donations can also be made to the U.S. Paralympics Ski and Snowboard Team.
We’re excited to be a part of this movement that matters. To learn more about what GlobalLogic stands for and about our consulting and software engineering partner services, contact us today.
Together, we’ll build the exceptional.
In part 1 of this blog series, we looked at the data and analytics evolution across data platforms, data processing technologies, and data architecture. Here in part 2, we’ll take a look at the evolution of the data and analytics space across application development and storage aspects.
Data Application Development Evolution
| Programming based → Scripting → SQL like → Low/No Code UI |
Initially, data engineers used programming languages like Java to develop most of the data applications on initial big data ecosystem projects like Apache Hadoop. This was because these frameworks provided interfaces to create and deploy data applications using the Java or Scala programming language.
Soon after, data engineers and analysts could easily use custom scripting languages like Apache Pig for Hadoop or Scalding for Cascading to develop jobs in a more user-friendly way without writing programs in the underlying language.
Due to the widespread use of SQL amongst the data analyst and data scientist communities, SQL and SQL-like frameworks such as Apache Hive for Hadoop, CQL for Cassandra, and Apache Phoenix for HBase became prominent and continue to be widely used by data engineers and data analysts alike.
Currently, with a shortage of data engineers and analysts, enterprises are increasingly looking at user interface based development that can reduce the implementation complexity and improve productivity. Therefore, the trend for the future is to move towards low code or no-code user interface based applications like AWS Glue, Azure Data Factory, Prophecy.ai, and GlobalLogic Data Platform that minimizes the learning curve for data engineers and accelerates the development for enterprises.
Data Formats Evolution
| Text / Binary Formats → Custom Formats → Columnar Formats → In Memory Columnar & High Performance Formats |
In the beginning, analysts stored most of the data in the Hadoop Distributed File System (HDFS) as text files or in binary formats like SequenceFile or RCFile. While some formats like text and JSON are readable to the bare eye, they consume a lot of storage space and are not performance friendly for large volumes of data.
Subsequently, engineers developed many open-source data serialization formats like Apache Avro and Google Protobuf to serialize structured data. They provide rich data structures and a compact, fast binary data. These formats continue to be used frequently for storing data.
Then engineers developed columnar formats like Apache ORC, Apache Parquet, Delta, and Apache Hudi that support better data compression and schema evolution handling. The columnar formats like ORC, Delta, and Hudi can also support ACID transactions to handle data updates and change streams.
The columnar data formats and storage systems are already the most used across enterprises. The trend for the future will be to use in-memory columnar formats like Apache Arrow or high-performance formats like Apache Iceberg or Apache CarbonData that provide efficient data compression and encoding schemes with enhanced performance to handle complex data in bulk. Internally, these formats still use ORC or Parquet to store the data making them compatible with the existing data stored.
Data Storage Evolution
| HDFS → Hive → NoSQL / NewSQL → Cloud Data Warehouses + Blob Storage |
HDFS was the initial distributed file-based storage system that allowed engineers to store large amounts of data on top of community hardware infrastructure. For example, engineers run the MapReduce programs on top of the files stored in HDFS.
Apache Hive and HBase frameworks followed this development, providing a table-like view of the underlying data and allowing developers to run SQL-like queries on the underlying data.
Soon after, several NoSQL databases were developed with different characteristics like wide-column, key-value store, document store, graph database, etc., to support specific use cases. Some popular NoSQL databases include Apache Cassandra, MongoDB, Apache CouchDB, Neo4J, Memcached in open source and Amazon DynamoDB, Azure CosmosDB, and Google Cloud BigTable, among commercial versions.
During this period, engineers introduced an integration of traditional RDBMS with NoSQL as NewSQL that seeks to provide the scalability of NoSQL systems for online transaction processing (OLTP) workloads while maintaining the ACID guarantees. Some NewSQL databases include Amazon Aurora, Google Cloud Spanner, CockroachDB, and Yugabyte DB, among others.
Most of the cloud storage is HDFS-compliant, and together with the serverless nature of this storage, enterprises are increasingly using them as the blob storage systems. Therefore, the trend for the near future will be to use cloud blob storage like Amazon S3, Azure Blob Storage/ ADLS, and Google Cloud Storage as the landing zone for ingesting data. The data will then be processed and aggregated data will be persisted in Cloud data warehouses such as Amazon Redshift, Azure Synapse SQL Data warehouse, Google Cloud BigQuery, Snowflake, or Databricks DeltaLake.
Engineers will continue to use the NoSQL databases for specific data use cases as applicable.
This concludes the second part of this blog series. We’ll continue to explore the evolution of the data and analytics space in subsequent blog posts in this series in the coming months.
Introduction
A data platform is one of many parts of an enterprise city map. Even though it's not the only platform, it's a significant piece of an enterprise city map that helps teams meet different business objectives and overcome challenges.
When dealing with a data platform, finding the hidden meaning, relationships, and embedded knowledge can still be challenging when attempting to realize the data's value.
Handling big data or real-time unstructured data presents challenges across collection, scalability, processing, management, data fragmentation, and data quality.
A data platform helps enterprises move information up the value chain by helping lay the foundation for powerful insights. Not only does a data platform pull data from external and internal sources, but it also helps to process, store, and curate the data so that teams can leverage the knowledge to make decisions.
The central aspect of leveraging a data platform is to consider it as a horizontal enterprise capability. Teams across the organization can use the data platform as a centralized location to aggregate data and find insights for specific use cases.
On its own, a data platform cannot realize its full potential. Are you setting it up for maximum impact?
While the goal of a data platform is to remove silos in an organization, it is difficult to do so until the organization enables a complete data platform. Then different units can leverage the platform functions so departments will have easy data sharing capabilities.
In this post, we discuss the principles that help ensure teams can optimize their data platform for use across the enterprise.
At GlobalLogic, we refer to these principles as the ‘Synthesize and Syncretize Paradigm’ for implementing data platforms.
These principles help weave together composability aspects into the data platform and lakehouse architectures. Additionally, it utilizes data mesh and data fabric principles with appropriate governance. This paradigm allows the implementation of a 360-degree data platform with enablers for easier adoption and uses across the enterprise as it facilitates the synthesis of platform components for syncretic use.
Principles
Enterprise Data Platform as the Core Foundation
The core data platform will form the foundation and own all the capabilities and technology stack to enable the following:
- Data storage
- Data ingestion interfaces for ingesting data into the storage layer
- Data processing during the ingestion and post-ingestion phases to transform and enrich the data
- Data access interfaces
- Endpoints for data ingress and data egress
- Orchestration and scheduling
- Data governance and data cataloging
- Control pane, monitoring, and security
- Data querying and data analytics
Teams will need to enable continuous delivery of new data platform features with centralized governance.
The Interplay of Domains & Data Products
Domains must be first-class concepts in the entire setup.
Teams can link domains to business aspects, data origin, use cases, source data, or consumption. Additionally, teams can enable particular feature sets within domain systems depending on the need.
Domains will vary from organization to organization since businesses closely tie domains to their organization's structure and design.
The core data platform foundation must be compatible with data products and domains. Teams can build their own data products for a domain on top of the core data platform foundation. Teams can also deliver data products in an agile fashion for incremental business value realization.
Microservices Based Architecture
The core data platform foundation will have a decentralized microservice architecture. This architecture provides API, messaging, microservices, and containerization capabilities for operationalizing data platform features.
The decentralized microservice architecture will enable the enterprise data platform so teams can use it as a central base with a decoupled architecture.
A team can leverage these capabilities to ensure the platform is resilient, elastic, loosely coupled, flexible, and scalable.
This will allow different domain teams to operationalize the data and features across the enterprise for their feature sets.
They also enable data and decision products in a domain on top of the unified data platform to access reliable data ubiquitously and securely.
Composability
The ability for teams to select the tools and services in a frictionless manner for their data products within a domain is crucial since it allows teams to assemble the required components. In addition, a composable architecture will enable teams to fabricate the necessary elements to deliver data and decision products.
This architecture paradigm will utilize both the infrastructure aspects as well as microservices.
A microservices-powered composable architecture for infrastructure, services, and CI/CD processes will allow separate teams and domains to utilize the same data platform infrastructure stack. The key to delivering a composable architecture is when the team focuses on DevOps and automation practices.
This will also enable dynamic provisioning with the definition of scalability parameters during the provisioning process itself.
Self Serve Data Platform Infrastructure
Teams should be able to use the data platform technology stack, features, and infrastructure. Teams can use a “No Code” or a “Low Code” approach with portals and self-service capabilities to enable these functions.
This principle will help teams reduce difficulties and friction when using and provisioning their environment. This will also help teams leverage the data platform to become a first-class asset across the enterprise and become the source of accurate data.
Discoverability & Data Sharing
Discovering and utilizing the platform and data assets elements is crucial to enable ease of synthesizing the right set of necessary components.
Data management is essential to catalog and manage data assets and datasets. Another important component is automation. It’s crucial to use automation for auto-discovering, tagging, cataloging and profiling data, and data classification with relationship inferences. This will enable teams to discover and utilize data assets efficiently.
Similarly, another key to discovering the capabilities is a catalog of available platform elements and features. This can cover the data connectors, existing data pipelines, services, interfaces, and usage guides.
The data platform also needs to have mechanisms for data exchange to ensure teams can effortlessly share data with appropriate access controls applied.
Centralized Governance
Centralized governance is a pillar to enable interoperability between various domains and teams and their data products. It will also ensure proper controls on new data platform features development and operationalization based on the actual needs of the teams so that they can quickly realize business value. This will act in conjunction with the data governance processes, data stewardship, and data management to ensure teams can access and share datasets in a controlled manner.
360-Degree Data Platform to power business with GlobalLogic
A data platform that leverages the above principles enables frictionless platform use and thereby accelerates utilization of the platform capabilities across an organization and value realization.
At GlobalLogic, we help our partners implement end-to-end modern data platforms with our big data and analytics services. Reach out to the Big Data and Analytics team at practice-bigdataanalytics-org@globallogic.com – let’s explore your data platform implementation options and how to drive the adoption of data platforms across your organization.