How Content Engineering is Transforming Corporate & Major Enterprise (CME) Organizations

Editor’s note: This article was originally published in CXO Today on July 26, 2023.

CXOToday engaged in an exclusive interview with Avnish Singh, SVP – Head of Content Engineering, GlobalLogic.   

Please elaborate on how Content Engineering is revolutionizing the corporate and major enterprise (CME) industry through enhanced collaboration and knowledge management. 

Response: Major Enterprises have large amounts of data in silos that get created due to geography, business function, scale, and other reasons. Over the last several years, these enterprises have taken conscious and elaborate steps to make this data available to everyone across the organization. Content Engineering practice plays a pivotal role in bringing in technologies and data experts who understand the data, consolidating it onto a common platform, and enabling enhanced collaboration by making it more searchable and accessible. 

An important aspect is to make this data easily searchable and bring in the ability for employees to access relevant information quickly. This can be achieved by applying high-quality data tagging and labeling techniques when setting up the common data platform. Improving the search and accessibility of the information across the organization enhances collaboration by ensuring that there is always a single source of truth, that has structured information on which the employees can collaborate. 

The organized approach benefits large enterprises with dispersed systems as it helps in breaking the silos and drives better knowledge management, and faster decision-making. Furthermore, the advantages of well-organized data extend to market growth and customer service. Organizations with multiple product/service lines can provide a seamless experience to their customers through properly tagged and centrally accessed data. 

This can help drive customer sentiment and hence retention. With the advent of generative AI, the use of content engineering teams becomes much more important. The data and domain experts will continue to enable organizations in creating their LLMs, to help power knowledge management and collaboration across the organization.   

How does GlobalLogic distinguish itself from other companies in the Content Engineering sector, and what sets it apart in terms of innovative approaches and implementations? 

Response: Our company DNA is product engineering, a capability that distinguishes us in our industry. This gives us a deep understanding of the complexities of the product lifecycle and its inherent dependencies on accurate and timely data. We recognize that such data is not merely incidental, but a crucial driver in shaping the customer’s experience and the organization’s evolution. The value it imparts is far-reaching, driving strategic decisions, refining product development, and propelling market positioning. 

Our approach to content engineering is profoundly influenced by our understanding of data in the product lifecycle. We go beyond mere data management and strive to unleash its full potential in terms of usability, accessibility, and impact. 

To us, data and content are not mere digits and letters but invaluable assets that can shape the trajectory of the organization and create rich, meaningful experiences for its customers. We ensure the integrity and validity of data at all stages of its lifecycle, from inception and collection to processing, storage, and deployment. Our stringent quality control measures guarantee the accuracy of data and the credibility of the content we present. By doing so, we ensure that our content is not just informative but also reliable, consistent, and geared toward delivering the intended impact. 

Not only that, but our content engineering services also drive digital transformation for clients, covering concept to platform to insights. Data and content are vital in the product life cycle that helps in aligning their journey with product evolution, ensuring true engineering value. Our expertise ranges from content digitization to machine learning, enabling diverse digital platforms. Through partnerships, we’ve built a strong cross-functional lab, supporting design, development, and maintenance. 

Additionally, we provide full lifecycle digital product development services to our customers covering requirement analysis, development, testing, and maintenance for completed customized solutions, deployment, and integration. These reflect across multiple aspects such as Talent Acquisition understanding, Operations & Process excellence, Competitive Pricing/Volume Discount/Innovation Fund, Content Localization and Multilingual capabilities, Data Security, and Adoption of Emerging technologies.   

Could you share examples of notable projects or case studies where GlobalLogic’s expertise in Content Engineering has significantly enhanced customer experience and achieved tangible business outcomes? 

Response: Some notable case studies that resulted in enhanced customer experience and tangible business outcomes for our customers: 

Case Study 1 – Enhancement of Navigation Maps for a leading ride-sharing platform company 

Challenges: Our client was using third-party commercial maps, which posed a few business challenges. The third-party maps were not designed and did not have all the features required for ride-hailing services. This led to a compromised experience for both drivers and customers due to routing and ETA issues. Additionally, maps service downtime directly impacted revenue, leading to skyrocketing costs as the business grew, adversely impacting the bottom line and margins. 

Business Outcome: Due to these challenges, the customer engaged GlobalLogic to help create their maps. We quickly set up a core team that understood the unique requirements for map creation for the ride-hailing service. The team then delivered excellent quality maps for 7 countries, processing road geometry of 659,000KM (adding 217,000KM new roads) with an accuracy of 99.61% for road geometry and 99.70% for navigation. 

This led to the enhanced customer experience and the customer experienced multiple benefits such as: 

• Enhanced Customer and Driver Experience through the improvement of overall route planning, excellent accuracy of pick-up/drop-off locations, and reduced navigation errors. 
• Increased business value for customers and drivers through enhanced routing efficiency through optimized routes, reduced travel time and costs. 
• Expanded service coverage through the addition of new roads leading to access to new riders leading to business growth Elimination of 3rd party maps license and subscription costs leading to improved bottom line and margins Case Study.

2 – AI-driven remote detection of medical conditions for a leading healthcare provider 

Challenges: The customer, a leading provider of nutrition and therapeutic health products, launched a dermatology product for remote assessment of various skin-related diseases. But given the remote nature, the diagnosis of the diseases was not very effective. Further, the doctor’s and patient’s session time was much longer as the diagnosis process was long. 

To solve these challenges, the customer wanted to use AI to identify various skin ailments. However, they did not have the required training dataset for this purpose. They tried to use their teams, but the process was taking very long. This is when they engaged GlobalLogic to help train their AI model with an appropriate machine learning training dataset. 

Business Outcome: We deployed a team of experts that included AI Content Engineering experts and Doctors with MD Dermatologist expertise. This team developed two machine learning training datasets. The first dataset was worked by AI content engineers who annotated the thousands of images provided by the customer to label (image quality, body part, skin type/tone Fitzpatrick scale, lesion detection) the ROI (region of interest). The team of doctors then did the ROI evaluation on this first labeled dataset to identify the skin disorders. The customer then used these two datasets to train their AI model with very good accuracy, making their product a great success in the market. 

This led to tremendous customer experience improvements for both the doctors and the patients as the time taken during the session was brought down by more than 50% in many cases and the AI-assisted identification of diseases led to much better accuracy of remote identification of the skin disorders.   

How does GlobalLogic maintain the quality and precision of the structured data it delivers through Content Engineering? Are there specific processes or methodologies in place to ensure accuracy? 

Response: GlobalLogic follows very stringent quality processes containing both manual and automated quality workflows. This is to ensure the quality and precision of the structured data. The quality workflow structures are customized based on the client’s requirements and expected deliverables. Our standard workflow includes: 

Data Validation: We implement comprehensive validation rules to ensure that data entered into the system meets predefined criteria. This includes format checks, range checks, and consistency checks to identify and reject invalid or inconsistent data. 

Data Cleansing: Once the data validation process is completed, we then clean and correct data to remove errors, duplicates, and inconsistencies. Furthermore, we also use automated tools, and scripts to identify and fix issues such as misspellings, incomplete records, or incorrect formatting. 

Recommended reading: Continuous Testing: How to Measure & Improve Code Quality

Documentation and Metadata: We maintain comprehensive documentation and metadata about customer structured data. This includes recording the source, meaning, and context of each data element. Clear documentation helps prevent misinterpretation and ensures accurate usage of the data. 

Regular Auditing: Periodic audits of customer data are conducted to identify and rectify any inconsistencies, inaccuracies, or missing values. This involves comparing data across different sources, verifying data against known benchmarks, or performing statistical analyses to identify outliers or anomalies. 

Quality Assurance System: GlobalLogic has an in-house solution for Quality Assurance which is tailored as per the customer requirements. This system can be used with any type of process workflow. 

Regular Data Backups: Regular data backups are performed to ensure that in case of any data loss or corruption, we can restore the data to its previous state. This minimizes the risk of losing valuable information and allows customers to maintain the integrity of their structured data. 

Continuous Improvement: Our focus remains on continuous monitoring and improvement of customer data management processes. Feedback from users is collected to promptly address any data quality issues, and we regularly review and update customer data quality procedures to adapt to changing requirements and emerging best practices.   

What are the primary technologies and tools utilized by GlobalLogic in its Content Engineering solutions, and how do they contribute to providing comprehensive support to customers? 

Response: We leverage multiple in-house content engineering solutions and third-party solutions to deliver services to our customers. These are divided into the following categories:

Data Extraction and Web Scraping: We have built our Web Scraping tools using Python, BeautifulSoup, and Scrapy for extracting structured data from websites. 

Extract, Transform, Load (ETL): Our Inhouse ETL solution provides features for extracting, transforming, and loading structured data from various sources into a target database or data warehouse. 

Optical Character Recognition (OCR): Leveraging third-party OCR tools such as Tesseract and PDFMiner helps to extract structured data from scanned documents or images by recognizing and converting text into machine-readable formats. We also have our in-house tool named Dark Data Solution. 

Data Quality and Precision: OpenRefine (formerly Google Refine), Google Sheets (With Apps Script), and a few other tools leveraged by us to help in cleaning and standardizing structured data. These tools automate tasks like removing duplicates, correcting formatting issues, and reconciling inconsistencies. 

Labeling, Annotation & Classification: GlobalLogic has built its tool named LabelLogic that caters to all types of training data requirements, for next-generation ML models, through labeling, annotation & classification. 

We leverage multiple accelerators, including Project Management Tool, Data Collection App, SLA Management Tool, and Auto Redaction of PI, while custom developing additional accelerators as needed. Our expertise in various tools and technologies like Python, Scrapy, Selenium, AWS, Google Cloud, Docker, Git, and more, further enhances our capabilities in delivering efficient solutions.

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