Creating Value from Data – How to Convert Unstructured Data into Useful Insights
The business world is digitizing rapidly, and data is widely accepted as the most valuable commodity in the current corporate ecosystem. Extracting this data from a diverse selection of sources, consolidating it in a standardized structure, and using it to generate insights can be the distinguishing factor between successful and failed ventures.
The number of companies trying to harness the power of big data, analytics, and AI to maximize their efficiency and revenues is ever-increasing. Therefore, developing an effective data management strategy to streamline data streams is crucial. This involves various procedures including data extraction, data integration, data transformation, data mapping, and data quality checks.
Traditionally, enterprises hired experienced coders to extract information from unstructured sources, standardize it, and store it in a single database. However, modern businesses rely on code-free data extraction tools to quickly identify patterns from raw and inconsistent data and subsequently create formatted records to generate insights.
This blog will go over some of the most productive data extraction strategies and how they can easily be incorporated into businesses’ existing data handling protocols.
Once a suitable database architecture has been set up, data experts must choose how to extract it and where to store it. This process involves using an appropriate data extraction tool with built-in connectors that support connectivity to systems that are a part your enterprise stack. Some commonly used databases include SQL Server, Oracle database, Azure Cloud, and Amazon Cloud Service.
Businesses generally use data extraction tools with ETL (extract, transform, load) functionality to easily manipulate data and convert it into an appropriate format for their database. These tools help arrange and structure the data in a machine-readable format so that AI and machine-learning software can process it and generate insights.
It is vital to choose the right data extraction tool and techniques to liberate valuable information from a disparate range of sources, accumulate it into a unified database, and transform it according to the insights that the enterprise needs to produce.
Data extraction tools generally support template-based extraction from file systems such as PDFs, PDF forms, TXT, PRN, RTF, XLS, XLSX and COBOL. The latest tools come with enhanced features, such as, workflow orchestration, FTP/folder/email integration, built-in job scheduler, automated name and address parsing, and the ability to detect extraction patterns according to the data structure automatically.
Benefits of Data Extraction Tools
- Precision: Data extraction tools eliminate the need for manually picking out chunks of statistics from complex data streams, thereby significantly reducing the probability of human error (HEP).
- Productivity: Automated data extraction processes can free up the time of valuable developer resources that would otherwise be utilized on manual repetitive tasks. The development team is, therefore, able to contribute in more beneficial ways to the organization.
- Cost-efficiency: Although these tools are rarely free, the price point is self-sustaining and justifies itself as it saves the business the cost of hiring a dedicated team for data extraction.
- Support: While development teams are likely to run into dead ends when working with complex data sources, these extraction tools often come with 24/7 customer support. Users can generate a ticket to request the software team to solve any issues that arise during the process.
- Improved analysis: Powerful data extraction tools can process data in real-time and update the database accordingly. They also come with built-in analytics features that can assist managers in making policy-related decisions.
- Utility for business users: Highly intuitive and graphical user interfaces allow business users with no knowledge of coding languages to execute complex data operations in just a few clicks. This also eliminates the possibility of communication errors between the development team and business users.
Once valuable information is extracted from unstructured sources, enterprises need to choose how to produce insights from it. Data analysts consider whether their choice of software is suited to handle the volume of their enterprise data, has cloud support, the technical expertise of their analytics team, the price point of the solution, and the extent of support that is offered by the solutions’ team.
Data mining tools help clients perform complex transformations and manipulations on extracted data, to structure it according to their business needs. While traditional corporations devote hours of coding and in-house solution development to cater to their data needs, these tools are ideal for modern businesses that are focused on maximizing effectiveness.
Leading Fortune 1000 companies such as Facebook, Google, Microsoft, and Amazon have stated on multiple occasions that data is the most valuable currency of the twenty-first century. Utilizing data and streamlining data extraction procedures can potentially differentiate thriving companies from failed ones.
Turning unstructured data into valuable insights involves identifying, extracting, and mining the elements that might be detrimental to future growth and policy formulation. Data warehousing solutions, data extractors, and data integration software are used to perform complex manipulations and orchestrate an automated data accumulation workflow. While some companies still prefer developing their own solutions to cater to their data needs, robust data extraction tools guarantee higher efficiency and faster processing speeds within organizations.
It is safe to say that data will become synonymous with survival in the business world in the decades to come. Therefore, investing in a data extraction tool that can adequately deal with all your enterprise’s data requirements is more critical now than ever.