Posted on : May 23rd 2022
Author : Gayathri N V S
Collecting, visualizing, and processing data now touches every professional field. Every aspect of a working life generates data and or depends on data. There is so much data chaos, with almost 80% to 90% of it being unstructured data. It is undeniable that we need data intelligence to end this chaos and create data value.
Businesses from numerous sectors worldwide invest heavily in Artificial Intelligence (AI)/Machine Learning (ML)-based data acquisition technologies. Collecting data based on the business requirements helps the companies analyze it to identify patterns and gain valuable insights to make better business decisions.
We have too much data lying around, but not everyone has the expertise to extract insights from unstructured data. Improving the use of data to gain business insights is the primary struggle faced by organizations worldwide. Turning data into insights and then those insights into actions excite the businesses, now more than ever. But, statistics show that between 60% and 73% ¹ of all data collected goes unused in corporations due to a lack of expertise and tools to achieve unstructured data intelligence.
Straive’s intuitive end-to-end data management platform, built with the best-in-class AI and ML technologies, helps enterprises acquire, extract, enrich, and analyze unstructured data. We pride ourselves in bringing clarity to the data chaos with our AI-driven data intelligence platform, the Straive Data Platform (SDP). It automatically extracts unstructured data from PDFs, emails, documents, images, and videos. Moreover, SDP delivers data in direct consumable formats in your analytics tools.
A smart and strategic approach is to use the data as a base to make informed business decisions, and we are here to help you with that. Our data solutions have proven results in every industry imaginable. From Finance and Information Technology to Insurance and Healthcare, we have deep expertise in making sense of unstructured data. So, let’s get you acquainted with them.
Sometimes, going with your instincts to make business decisions can be ok. Still, most business matters require metrics, facts, figures, or insights related to your business objectives, aims, or initiatives to achieve stability and consistency in the data operations. This is where the quest for better decision-making begins. Data-driven decision-making is precisely what you want. Leveraging the power of data insights and adapting the latest on-point data solutions will help make informed and verified business decisions.
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.
At Straive, we do what needs to be done, even if it is difficult. Tackling the giant pile of alternative and unstructured data, extracting the relevant data, enriching it with value-added processes, and analyzing it to pull out the valuable information that can help your business increase sustainable growth is exactly what Data-Driven Decision Making is all about. Although it is the need of the hour, conventional data management techniques are ill-equipped to transform unstructured data into actionable intelligence.
Substantial Quantity: The sheer amount of data generated regularly with every routine task and transaction makes it challenging to use it in the right way. Furthermore, data management is effective if this humungous quantity of data is handled in the cloud.
Multiple Formats: Your organization gathers disparate data in various formats from numerous different sources, which poses a problem with respect to sharing the relevant data with the appropriate department to analyze and gain insights from it.
Lengthy Turnaround time (TAT): Collection of data in multiple formats, from siloed data storage systems, along with the various policies governing the data causes delays. When it comes to successfully converting the raw data into valuable insights, turnaround time is important to ensure the timely availability of information to make better business decisions.
Acquisition From Multiple Sources: Speed of acquisition matters because disparate sources generate a humungous volume of alternative and unstructured data. These must be processed and delivered quickly to derive value from their insights.
Removal of Noisy or Unwanted Data Acquisition From Source: Unstructured datasets are inherently noisy. They contain spelling mistakes, HTML tags, special characters, etc. The quality of unstructured data has to be improved. Otherwise, it will affect the accuracy of the data models and outcomes. The importance of removing noisy and unwanted data can never be underestimated because various estimates show that data scientists spend 50-80 percent of the analytical process on data cleaning.
Aggregating the Data: Aggregating unstructured data in a timely and accurate manner manually is challenging because, with each source, the team must deal with a new format. In some industries, this process can take months and ultimately restrict a company’s agility and revenue-generating opportunities.
Transforming and Enriching the Data: Cleaning, enriching, and formatting unstructured data is a considerable challenge to a company undertaking data transformation tasks. It is a time-consuming process. Manpower needs time to oversee the processes, and machines and software need time to work through the process. Furthermore, depending on the volume and type of data, it can put immense stress on an organization’s infrastructure and resources.
So now that we’ve covered the challenges faced by the organizations let's surf through the various practices that guide you in the right direction to make better business decisions. There are, however, multiple different ways to go about it, so let's have a look at a few of them and understand the fundamental working principles.
Data, the right way: Data is the key to data-driven decision-making, so identifying, extracting, and using the right data is essential. Let us explain, for example, that our innovative data platform SDP provides the perfect extraction services that can cleverly maneuver through chunks of unstructured and structured data. It also has built-in modules to scrape online public sources to identify the well-suited source and select accurate data for your business needs.
Purpose-built tools: Finding cost-efficient, speedy, and performance-centric analytical tools are pertinent when converting massive business data into actionable insights that eventually lead to disruptive progressive business decisions. The tools should be on-point and efficiently collect, store, transform, and analyze data to the optimum level that empowers your employees across all departments in the organization to make better-informed decisions.
AI & ML integration: Data analytics tools that are AI-driven and ML integrated will maximize the value of your data and promote enhanced in-depth insights to achieve long-term business growth. As one of the most disruptive technologies, ML boosts operational efficiency, optimizes ongoing processes, helps to develop new & improved products, and achieves stable revenue growth. Incorporating AI and ML equips your organization to offer personalized customer experiences.
One-to-one focus: Personalization is something that can set you apart from the competition. Transitioning from one-to-many customer marketing and embracing one-to-one is essential for dynamic business decision-making. Focusing on customer-centric data with pertinent data solutions that cater to your business needs can drive flawless personal experience via dynamic content, product recommendations, discounts, offers, and much more.
Honestly, we could go on about the countless ways to help you make better decisions. Trust us; we know it because that’s all we do. Helping businesses like yourselves to pave the way through heaps of business data, and mind you, almost 80% to 90% of it is unstructured data and leading them to make record-breaking business decisions.
How do we do it, you ask. If we were to say it in short, with our unparalleled innovative Text Intelligence, Public Data Intelligence, and Visual Intelligence solutions, we extract, transform, enrich, and convert the mounds of data into invaluable insights, which accelerates the quality of informed decision making for you. Let’s not forget the endless benefits your organization will enjoy. For example, it saves you the hassle of doing it all by yourselves and incurring massive expenses.
On a more practical note, we help you gain a more competitive advantage, unlock the power of unstructured data, and gain a number of valuable insights efficiently, effectively, and economically. So, what are you waiting for? Connect with our Data Solution Experts here.
¹ Gualtieri, M., & Cicman, J. (2017, July 10). Hadoop is Data's Darling for a reason. Forrester. Retrieved May 16, 2022, from https://www.forrester.com/blogs/hadoop-is-datas-darling-for-a-reason/
The process of data extraction involves identifying and recovering alternative and semi-structured data from various data sources such as files, XMLs, JSON, etc.
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.
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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