Companies today produces a host of structured and unstructured data in enormous volumes. Extracting of details from them is an important process, therefore, in this article we will discuss the process, benefits, and some relevant tools related.
Data generation is continuous at an increasing pace. Every business generates a huge amount of structured and unstructured data regardless of size. This data is valuable for business growth as it holds significant information for understanding the parameters affecting the business. Manually handling data is tedious and unmanageable after a while as the accumulated data size grows. Organizations, therefore, look to invest in automated solutions consisting of powerful tools to process and manage this data, extract information from it and store it for future use.
Follow this article to learn more about the extraction of details and aggregate totals from data. Further, we will discuss their importance and the processes and tools currently available for them. We will also cover the benefits and challenges associated with these two vital data analytics processes.
In this section, we will explore what is meant by 'details extraction from data' and why it is crucial. Big data is a buzzword today. It is now the main focal point of business decision-making, provided it is relevant and valuable. As is well known, this data is readily not in a usable form due to 80% of it being unstructured with other vices like noise, missing values, etc. Further, the huge volume, velocity, and variety of generating make it difficult to analyze to extract meaningful and indicative parameters that help decision-making. After successful mining, the pre-processing step, like cleaning and formatting, makes it storable and ready for analysis. Decision-making is mostly done using appropriate models trained on available clean data using machine learning and deep learning. The extraction of features or important parameters that correctly help the model to be trained properly is crucial for the success of a developed model in its real deployment performance.
Along with the features, the aggregated values of different indicators that go into tabulation for statistical tool deployment are also very important. Manually handling this task is out of the question due to huge volumes from time and monetary constraints. Automating these requirements through currently available tools for extracting and aggregating large data is undoubtedly a compelling solution.
Businesses cannot leverage the full potential of data and make informed decisions unless they can extract all forms of data, including unstructured data.
Organizations often face challenges in managing and processing the increased accumulated data. Since data is acquired from various data sources, it may be in structured, semi-structured, or unstructured forms. Parsing or extracting required information from especially unstructured data is complex and time-consuming. This process of extracting the required data from the different input formats is known as data parsing. For proper analysis and improved decision-making, the retrieved data must be transformed into a legible and right format.
A simple example of this could be our everyday forms. These are used practically everywhere as they allow the collection of information under specific categories. Companies can use forms to survey and gather feedback, collect applicant information, and practically document any information that is important. So, how can we apply the extraction of details to a document? We will understand this better with an example. Let's say that the form is a simple restaurant receipt.
The image on the left is a sample receipt which, if parsed correctly by the tool, can fetch all the information highlighted in the red bounding boxes on the right image. Out of these, a few important values can be programmed to be automatically saved in the database.
Other examples could be an online filled form, a purchase order, an invoice, etc., where the extraction of details indicates gathering entered information. This information can be mentioned under specific keywords like name, address, contact number, email id, date, city, product id, qty., etc. Next the extracted information can be stored in different formats (. json, .csv, .txt, etc.).
Sometimes, the difference in the form layout and style can make it tedious for the software to retrieve the correct information from the form. Parsing handwritten forms is challenging as handwriting can be difficult for the machine to parse correctly. Thus, there is no straightforward method to extract details from a document. The same goes for images. In the case of images, the extraction of details pertains to the relevant tags for the image, such as 'sports,'' humans,'' sunset,' 'technology' etc. So, we rely on automated procedures to efficiently extract data from forms as these systems are less prone to mistakes. Tools like VisionERA can make extracting this type of data easier. You can find more information towards the end of this article.
The tools for the Extraction of details can be open-source or cloud-based tools.
The above tools can handle individual or batch processing of the extracted details.
Let us look at another important concept, 'Aggregate Totals,' in this section. Preparing a dataset for model training in AI is crucial, as the model's success depends on the prepared dataset's quality.
Aggregation refers to any technique that collects information and summarizes it. We all have summarized data, estimated totals, and averages at some point. When a summary of the available data is shared in the form of data rather than a report, it can be termed data aggregation. When data is aggregated, atomic data, which is frequently gathered from several sources, is replaced with totals or summary statistics. In place of groupings of observed aggregates, summary statistics derived from those data are employed. Aggregate data is typically found in a data warehouse since it can answer analytical queries and dramatically reduce the time it takes to query large data sets. Thus, aggregated data forms a basis for advanced or complex calculations or can be merged with another dataset to gain additional information for decision-making.
Data aggregation is commonly used to generate useful summary data for business analysis and statistical analysis for groups of people. Large-scale aggregation is generally implemented using software tools known as data aggregators. Tools for obtaining, analyzing, and presenting aggregate data are typically included with data aggregators.
It is possible to summarize data from various, diverse, and many sources using data aggregation, increasing the utility of the available data, which improves the value of the information. Advanced data integration solutions make tracking and auditing aggregated data easier and more reliable.
Further, Data aggregation allows researchers to easily access and evaluate large amounts of data. One or more rows of aggregate data can represent one or more atomic data records. When data is aggregated, it may be accessed faster than if all of the processor cycles were used to access each row of the underlying atomic data and aggregate it in real time.
Aggregating your data makes it simpler to identify patterns and trends that might not be immediately obvious. Making smarter judgments and improving product services and communications are made easier with quick access to data. Data that has been aggregated can support compliance with regulations. Aggregation may benefit the most important and often accessible data, making it easier to obtain as the amount of data collected by an organization grows.
Raw data can be aggregated over time to estimate statistics such as average, minimum, maximum, total, and count. Once the data has been aggregated and included in visualization or a report, it can be examined to gain more specific resources or resource groupings. Data Aggregation can broadly be classified into two types -
Let us explore data aggregation and how it works using an example.
Businesses often collect big data for their marketing needs using several open-source and commercial tools. Information about customer behavior, such as their purchasing habits, products they are interested in, where they like to shop, and other similar insights can be used to decide on tailored marketing strategies. Data aggregation can help organizations in data mining as well as conveniently structure their data in a more accessible way.
For example, here is a fictional dataset for different car brands related to the percentage market share for a particular year.
This data can be used to plot a pie chart to study the percentage shares. This can help to analyze the market size for a specific segment of cars and understand which brands are performing well. When such data is coupled with historical data, additional information can be extracted about any company's performance with respect to its product and services.
There are several tools available for the aggregation of data. It is possible to process and aggregate data using commercial Business Intelligence (BI) tools like Excel, Tableau, Power BI, etc. Similarly, equally powerful open-source tools like Python, Rstudio, Grafana, Apache Spark, MongoDB, etc., can handle big data. Depending on their project needs, companies can choose either a single tool or a combination for aggregation.
Applications of Extraction of Details and Data Aggregation.
Data aggregation may help with various decisions, including financial and business strategy decisions, product planning, pricing strategies, operations optimization, and marketing strategy formulation. Users include data scientists, analysts, warehouse administrators, and subject matter experts.
Aggregated data is widely used in statistical analysis to learn more about certain groups of people based on demographic or behavioral criteria such as age, career, education level, or income.
Data may be utilized for business analysis to produce summaries that help decision-makers make informed decisions. Users' IoT device browsing history, social media interactions, and other personal data may be integrated to offer organizations critical consumer insights.
A few examples of industries using data aggregation are -
In this article, we discussed the extraction of details and aggregate totals from data with examples. Here are the key takeaways from this article-
Looking to automate your invoice processing? Consider VisionERA , an IDP that simplifies everyday document processing functions and reduces dependence on manual processing. It is an industry and use case agnostic platform that can be modified as per requirements for different industries such as finance, healthcare, logistics, manufacturing, etc.
Contact us today to learn more about our invoice processing automation tools and see how we can help your business.