Information Services
Actionable insights to make meaningful data connections
Information Services Overview
In the past five years, the information services industry has witnessed exponential growth in the volume of data generated and consumed. Consequently, data extraction, storage, transformation, processing, retrieval, usage, and delivery has become challenging for an information services provider.
In its raw form, data is virtually worthless. However, advancing technologies can turn it into a gold mine. Data extraction and annotation are critical in transforming data that can be fed into AI/ML models. At the same time, data can become "obsolete" quickly; therefore, defining the use cases for data collection and management is important.
Why is Data Extraction Critical?
Information services providers must collect data at scale, cleanse, and standardize them with domain knowledge. Data extraction is the first step in this process, for which subject matter expertise is vital for bringing a proper perspective to data curation, enrichment, and transformation of data into actionable insights.
Generally, information services providers need to extract data from multiple sources such as online web pages, government registries, regulatory bodies, directories, product catalogs, and various formats like documents, images, PDFs, and videos. Today, the information systems in organizations use external and alternative data to derive actionable insights. The challenge lies in effective data extraction and data annotation.
An effective data extraction process should be able to identify and extract data from any number of raw data sources. Further, data annotation is critical in labeling data found in images, scanned PDFs, and videos. It helps Artificial Intelligence (AI) models identify specific data types and deliver relevant output.
What are the Data Extraction Challenges?
Legacy information systems or existing web extraction solutions struggle to keep up with the more complex websites and security restrictions such as captcha, making collection sub-optimal. Most information services providers depend on manual data extraction processes to extract relevant data points from text-based documents. These processes are time-consuming and labor-intensive. Also, continuous monitoring is required to check the currency of data. If an information services company lacks proper data governance and management strategy, it will lead to data silos.
Straive Data Extraction Capabilities
Data Extraction and Enrichment: Our data extraction and enrichment modules have prebuilt AI and ML algorithms trained for data excerption. Information services providers can leverage these modules to extract data from annual reports, scientific literature, regulatory documents, and more.
Curation: Straive enables an information services company to deliver enriched and differentiated data sets by leveraging an AI-based auto-curation solution layered with deep domain expertise.
Knowledge Management: Straive enables information services providers to drive the discoverability and reusability of data by creating and managing taxonomies, ontologies, and graph technologies for knowledge management.
Product Development & Management: Straive empowers providers to identify and accelerate the market launch of new data products through end-to-end data identification, extraction, enrichment, and cleansing services to deliver differentiated datasets.
Research & Reports: Straive provides secondary research and report writing services such as industry and research reports, people profiles, competitive research, and market trends analysis.
Data Processing Audit: Straive allows deep forensic analysis of the data processing supply chain – people, process, and technology -- to identify gaps and recommend improvements.
Our Solutions: Straive provides information providers, across industry segments like Research, Education, BFSI, CPG, Retail, Logistics, Information Services, and Emerging Markets, with scalable data solutions across the data lifecycle to help uncover data intelligence and analytics from their data assets. Straive’s end-to-end data solution enables enterprises to perform data extraction and transform the extracted data into insights. It involves implementing data governance practices and strategies for extracting, enriching, storing, transforming, processing, retrieving, using, or making information available.
How does Straive Data Extraction Services Work?
Our proprietary Straive Data Platform (SDP) enables data extraction from multiple sources. By leveraging web crawlers and native data connectors, data extraction can be performed on websites, PDFs, blogs, RSS feeds, APIs, etc. Furthermore, SDP's intelligent engines clean, de-duplicate, and normalize the collected data. To create structured datasets, NLP and ML models are used to extract relevant data entities. Finally, Straive’s SME team of 500+ associates curates and validates the data to deliver high-quality datasets in structured or semi-structured formats such as CSV, Excel, Flat files, JSON, XML, and API for seamless integration.
Straive Data Solution Use Cases for the Information Services Sector
Below are some significant use cases that enable enterprises across industries to meet their business priorities and create analytics solutions.
| Industry | Use Cases |
|---|---|
| Research | Citation Managemen Taxonomy |
| Education | Rights & Royalties Data Enrolment & Alumni Data |
| BFSI | Alternative Data ESG Data |
| Information Services | News Data Companies Data |
| CPG, Retail & Logistics | Contracts Intelligence Geospatial Data |
| Emerging Markets | POI Data Clinical Trials |
FAQs
What are the types of information services?
Information services providers offer news reports, articles, pictures, public documents, photographs, maps, audio material, audiovisual material, and other archival material of commercial interest. According to a survey, the global information services market will reach $148.28 billion in 2022.
What are information services?
The increasing importance of data and information in decision-making has created an enormous appetite for data. Information services providers cater to this need by employing integrated data extraction methods to process, communicate, and store different types of information that improve the efficiencies and competitiveness of enterprises. The data covers structured, semi-structured, and unstructured alternative data for analysis and enabling data to drive decision-making.
What are the examples of information services?
In general, information services providers offer data on business intelligence, financial markets, legal, tax, and regulatory information, credit and risk management, and marketing information.
What are the benefits of information services?
There are multiple benefits to using the data provided by an information services company. For example, information on industries, products, and services helps identify market opportunities, stay competitive, and create new products. Further, new data is always in demand, and new types of analysis are needed to innovate and compete.
Why is data extraction necessary for information services providers?
Data extraction allows information services providers to consolidate and unify multiple data sets. Data extraction simplifies extraction of information from diverse sources. For information services providers’, data extraction is the first step in enabling their partners to access valuable data. Increasing volumes of data have made data extraction necessary. For information services providers, automated data extraction saves time, cost, and reduces errors. It is essential because it can be used to extract data from any kind of text. In short, data extraction is vital for using data in further analysis.
Challenges
Current web extraction solutions struggle to keep up with more complex websites and security restrictions such as captcha, making collection sub-optimal.
Extraction of relevant data points from text-based documents is manual and time-consuming.
Data become outdated quickly, requiring continuous monitoring to check for currency.
Lack of proper data governance and management strategy leads to data in silos.
Our Capabilities
Acquire and Enrich
Our data extraction and enrichment module has prebuilt AI and ML algorithms trained for data excerption from highly unstructured documents such as annual reports, scientific literature, regulatory documents, and more.
Curation
Straive enables information service providers to deliver enriched and differentiated data sets by leveraging an AI-based auto-curation solution layered with deep domain expertise.
Knowledge Management
Straive enables information service providers to drive the discoverability and reuse of data by creating and managing taxonomies, ontologies, and graph technologies for knowledge management.
Product Development & Management
Straive empowers organizations to identify and accelerate the market launch of new data products through end-to-end services of identifying data sources, automated collection, enrichment, and cleansing to deliver differentiated datasets.
Research & Reports
Straive provides comprehensive secondary research and report writing services such as deep company, industry, people profiles, competitive research, and market trends analysis.
Data Processing Audit
Straive allows deep forensic analysis of the data processing supply chain – people, process, and technology to identify the gap and provide or recommend actions for improvement.
Our Solutions
Straive helps organizations with highly scalable data solutions across the data lifecycle, allowing them to uncover data intelligence and analytics from unstructured and structured data assets. This end-to-end data solution aids an organization’s journey from data to intelligence. It involves implementing data governance practices and strategies right from data acquisition, extraction, enrichment, and transformation to ingestion and consumption.
How It Works
Leveraging our proprietary unstructured data platform, the Straive Data Platform, we enable clients to extract data from any source such as websites, PDFs, blogs, RSS feeds, APIs, etc., by leveraging web crawlers and native data connectors. The collected data is cleansed, de-duplicated, and normalized using SDP’s intelligent engines. Relevant data entities are then extracted through NLP and machine learning models to create structured datasets. Finally, Straive’s domain-specific SME team of 500+ associates curates and validates the data to deliver high-quality datasets.
Additionally, data can be delivered in structured or semi-structured formats such as CSV, Excel, Flat files, JSON, XML, and API for seamless integration with downstream systems.
Insights
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