Reconstructing analytical data is the essential process of restoring, recreating, or rebuilding data from partial or incomplete information. The process is needed when data has been damaged, lost, corrupted, or fragmented, yet the original information is needed for data analytics or other business processes.
Data reconstruction is necessary in various scenarios when data is lost or damaged. For example, in telecommunications, data shared over a network may become fragmented. The reconstruction process uses the pieces to rebuild the data to its original form.
Reconstructing Data for Business Processes
Techniques for rebuilding data to make it useful for business outcomes include leveraging:
- Redundancy and error codes. If data is stored with redundancy or error correction codes, that information can help with the rebuilding process. You can leverage the codes to recover the missing or corrupted data. For instance, if you’re using a redundant array of independent disks, better known as a RAID system, data is distributed across many of those disks. If one disk fails or damages the data, information from the other disks can help you reconstruct the data.
- Redundancy in computer networks. In distributed systems or computer networks, data can be replicated across many different nodes. If one of those nodes fails or is unavailable, that node’s data also becomes unavailable. Using the replicated copies of the data stored on other nodes lets you reconstruct the inaccessible data.
- Backup restoration processes. You’re probably backing up your data, and that will be a key advantage when you need to restore lost or damaged information. A common approach to recovering data is to leverage your most recent backup. It’s usually a straightforward and common method for restoration—you simply use the backup to restore your data to its original state.
- Data recovery software. This specialized software lets you restore data from your computer, mobile device, or storage media such as a hard drive, memory card, or USB drive. You can recover data that’s missing or damaged as a result of a hardware or software failure, deletion, outage, cyberattack, or someone overwriting an essential file. The software scans the storage devices, locates lost or deleted data, then works to recover and piece the data together.
- Interpolation techniques. Interpolation reconstructs data by estimating or using approximations of missing and damaged information based on surrounding data points. These techniques are often used to reconstruct image or audio data by levering the parts of data that are available, and to “smooth out” irregular data.
- Database transaction logs. These logs do not directly reconstruct data, but they do provide critical information that allows the database to recover and rebuild data. Database transaction logs record changes to the data in database systems. When a failure occurs or data is corrupted, the database can be restored to a previous state by using the transactions that are recorded in the logs.
- Manual reconstruction. Sometimes, reconstructing data must be done manually, especially when the data is in non-standard or unique formats. The process involves piecing together data from a variety of sources to estimate the missing or corrupted data points. Manually reconstructing data can be time consuming, and the data may not be as accurate or complete as data that’s reconstructed using automated methods. Likewise, the process may require specialized tools and expertise.
Integrate Reconstructed Data to Enable Your Business
Missing or damaged data may contain important details that your business needs for decision making, data analytics, or other uses. Reconstruction is one of many processes that help unlock the full potential of analytical data and make it ready to use for analysts and other business users. Other essential processes include data cleansing and data integration. Data management is also key to ensuring your data, including reconstructed data, is governed and stored in a way that makes it easily accessible when you need it.
When data is in the right format, integrated with other data, and managed properly, it can serve your business needs. These needs include informing business decisions, predicting business outcomes, identifying trends, improving customer experiences, and more.
Better Data Leads to Better Business Outcomes
A modern data strategy is needed to bring together and leverage all essential data for the business. This helps break down data silos while promoting a data-driven culture. A plan for reconstructing data should be part of the strategy because there’s always a likelihood that data will become lost or damaged at some point, even with strong data governance processes in place.
The Actian Data Platform can help with all of your data needs. There’s a reason it’s called “The Easiest Cloud Data Platform on Earth.” You can use it to integrate, transform, orchestrate, and store your data in a single, easy-to-use platform that can be deployed in cloud, on-premises, or hybrid environments.
Give the platform a free 30-day trial to see how it can help you drive intelligent business growth.