Posted on : August 29th 2022
With more and more organizations transiting their businesses online, data has become the lifeblood of a successful business. In this digital era, enterprises harness the flow of data to achieve business objectives, such as developing innovative products and services, delivering exceptional customer experiences, and optimizing the efficiency of internal operations. However, we can agree that acquiring, extracting, and transforming data is the key to making data useful for any type of analytics that can eventually lead to business intelligence.
The key step in making data suitable for analytics is data extraction and transformation from diverse/alternative sources. Simply put, the data extraction process gains significance when you contrast it with the fact that 43 percent of the data collected by an enterprise is never used. This is a serious concern. Without a way to extract all data types from diverse/alternative sources, enterprises would find it challenging to leverage the full potential of information and make the right decisions.
The process of data extraction involves identifying and recovering alternative and semi-structured data from various data sources such as files, XMLs, JSON, etc.
Data extraction makes it possible to enrich, transform, and deliver data in formats that can be integrated with any data workflow or ingested directly into analytics tools for deriving insights. By extracting essential information from text, images, videos, and audio, enterprises can use them for various purposes. Chief among them is data-driven decision-making. A good quality dataset is also critical for enabling high-performance Machine Learning (ML) models.
Considering that data extraction is critical for business growth, it makes sense to do it with the right people, such as Straive, the global leader in content technology solutions. Straive’s data solutions for identifying and extracting relevant data based on business needs revolve around its proprietary Straive Data Platform (SDP).
As part of Straive’s data extraction solution, diverse user-generated content such as news feeds, social media messages, corporate documents like emails, contracts, etc., are ingested into the SDP using data connectors.
Moreover, built-in modules in SDP could be used to scrape datasets from conventional sources like databases, spreadsheets, online forms, medical devices, network and web server logs, Online Transaction Processing (OLTP) systems, sensors, SEC filings, quarterly financial statements, etc. Similarly, SDP can help acquire alternative datasets from social media, weather reports, credit card transactions, app usage, Environmental, Social, and Governance (ESG) reports, mobile devices and Internet of Things (IoT) sensors, satellite imagery, and web traffic data.
Data extraction is the first step in gaining business intelligence. The capability to extract data from multiple formats and diverse sources enables enterprises to obtain a 360-degree view of a business by providing predictive, current, and historical data. Enterprises can use this data to get a consolidated data view, which drives better data-driven business decisions.
Many enterprises leverage data platforms such as SDP for data management and convert unstructured data into a structured format. Moreover, enterprises can leverage the data extraction capabilities of these platforms to break data silos, combine data from diverse sources, and transform it into a client-desired format. Subsequently, the data can be directly ingested into data workflows. By leveraging the right technique, information trapped within disparate systems can be standardized and transformed so data analytics teams can easily derive insights from data.
There are instances where real-time data is essential. To analyze inventory levels, enterprises need real-time data extraction from supplier invoices to ensure that their customers always get what they want. An automated data extraction solution such as the one provided by SDP would be able to extract real-time data and ensure that inventory levels are continuously optimized.
Data extraction solutions can also help extract critical financial data from disparate sources and give asset managers a detailed view of company financials. In fact, auditors can analyze and extract the required data instantly from various financial documents using SDP’s taxonomy mapping capabilities and ML models.
Today, there is a deluge of digital health data that can be obtained from Electronic Health Records (EHR). Furthermore, according to the Journal of American Medical Informatics Association (JAMIA), unstructured real-world data has a high predictive value. Hence, even though most digital health data extraction involves extracting normalized structured data elements, the massive volume of unstructured digital health data cannot be ignored. Data platforms can help simplify the complex process of extracting digital health data, and help augment health-related studies and influence regulatory decision-making.
Thanks to data extraction capabilities, real-estate enterprises are already analyzing critical influences and making data-driven decisions. Many switched-on commercial and residential real-estate enterprises perform property valuation, track vacancy rates, calculate rental yields, forecast industry trends, etc. Data platforms such as SDP use Artificial Intelligence (AI), ML, and Natural Language Processing (NLP) to extract addresses, images; property reviews; agent profiles; and pricing information for real-estate enterprises to gather intelligence in real-time and in the required format.
There are various other examples of data extraction across industries. For instance, manufacturers must extract data for competitive landscape analysis and research. Data extraction from patents helps identify chemical structures and properties for drug discovery. The process could play a significant role in detecting and preventing adverse events of medicine/medical devices.
In today’s digital economy, the scope of global technological change and business intelligence has changed. Today, the information does not merely add efficiency to the transaction; it adds value. Enterprises leveraging the speed and ubiquity of data will have the advantage of making important decisions in near real-time.
In the traditional system, decisions could take months. In contrast, today, there is a compulsion to decide within days or even hours. This challenging and complex need to make data-driven decisions can be largely simplified with a combination of technological and human expertise. Data extraction is an integral part of the data workflow. It initiates the process of transforming raw data into meaningful information, which subsequently becomes fodder for business intelligence tools.
Since we have established that data extraction and business intelligence go hand in hand, we must still cover critical parameters that make or break the deal. Not to brag, our SDP covers all the critical parameters to the dot. To elaborate, SDP uses custom-made search queries to aggregate the sources and rank them based on the following parameters:
With the exponential growth in data published directly by governments, businesses, publishers, and individuals on the internet, it could be challenging to identify the right source and select data that is accurate and free to use. We can track the right source for any data-related activity through our SDP’s source discovery process, which involves a multi-source approach to drive accuracy and efficiency.
Data extraction can help enterprises grow by providing highly usable and valuable informational resources – data – that can enable better decisions, increase productivity, or gain insight into the market. Suppose your enterprise deals with a large data flow that consists of files in multiple formats stored in different locations. In that case, you need to use data extraction solutions to maximize its value. You will be able to analyze customer behavior, create buyer personas, and increase profit margins by customizing your offers and improving your products and customer service based on this extracted data.
Our SDP platform has been successfully deployed at scale for multiple clients. It is currently scraping data from 12 + million web pages. It has systematically collected over 50 million data points around companies, people, products, and locations. Last but not least, SDP monitors around 610,000 web sources daily. So, it is time you request a demo at www.straive.com/data-extraction-process-demo
Capital markets are an excellent example of a perfect competition. The nature of the market is such the participants have to be competitive and result focussed. For instance, brokerages and investment banks have to deliver passive gains for their clients and, at the same time, earn a margin for themselves.
Today’s ESG analytics require processing data, patterns, and hidden connections to provide insights that investors, asset managers, and companies need. For example, Straive deploys advanced machine learning algorithms to analyze reams of documents to collect evidence across executive statements for signs of vagueness or obfuscation.
Talking about using data to gain insights is easy. But actually doing it will uncover a newer set of challenges, especially when it comes to unstructured data.
Integrating ESG data into commodities trading operations requires structured, easy-to-consume data. By their nature, ESG data resist such integration, and highly scalable data solutions across the data life cycle are needed to allow stakeholders to deploy end-to-end data solutions for a successful data-to-intelligence journey.
Alternative ESG data - Often associated with financial analysis, alternative data is a term that typically refers to externally sourced information about a particular company to gain additional business insights.
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