In a world of ever-increasing complexity and digital connectivity, the amount of data moved across industries is growing exponentially. No matter your size or the industry you are operating in, the most significant issue facing almost every company is how to efficiently and effectively move large amounts of data in a method that allows you to quickly access, analyze, and act appropriately.

For many companies, data mismanagement can lead to ineffective operations at best, and catastrophic data loss or exposure at worst. What your team needs to effectively collect and act upon large quantities of data can be found in a variety of technology-based applications that allow for high-level cloud computing of big data.

However, which technology is best for your data? When making decisions on how best to handle large amounts of data - especially in the cloud - enterprises must first understand the big-picture of cloud big data management: Data Lifecycle Management.

By fully understanding the entire data flow from initial collection to final archiving and purging, you can better understand what element of your data cycle needs to be addressed, and by what application.

Let’s dig into the Data Lifecycle Management system and uncover each specific element of the process. As you begin to see the big picture of your data, you can make the best decision to maximize your data usage.

Defining Data Lifecycle Management (DLM)

To better understand how big data moves throughout systems, it is essential to define the system itself. The term Data Lifecycle Management is a catch-all term referring to how organizations have commonly defined and structured the steps an enterprise uses to collect and act upon data.

The DLM process will look unique for each company, as different organizations collect and use various kinds of data for multiple purposes. However, the primary stages of data computing remain consistent regardless of the company. The ultimate goal of DLM is to help increase the ROI of collected data and maximize the usefulness and effectiveness of the information over time.

A typical DLM process will include the following steps:

  1. Collection
  2. Maintenance
  3. Synthesis
  4. Use
  5. Publication
  6. Storage
  7. Purging

To ensure that data follows each step of the process and maintains its integrity, automated applications will provide specialized computing resources. This automatic process will allow the data to be moved quickly and efficiently through the system without wearing down resources or wasting space.

To properly implement a DLM system, companies will make use of a variety of technology applications. However, not all applications are created the same, and many offer capabilities that your data may or may not require for effective analysis and storage. Regardless of your choice, understanding the full process will help you decide what big data cloud computing solution is best to take your data to the next level.

Breaking Down The Data Lifecycle Management Steps

To truly understand what the implementation of data lifecycle management implies for a company, it is necessary to know each of the phases that data goes through during its lifecycle.

1. Data Collection

Also known as data ingestion, the first stage of DLM involves capturing and collecting big data. The data to collect might not exist yet, but must be accounted for in cloud computing and storage. For your data to be useful at all, you must have a high-quality data ingestion process in place that can handle large amounts of traffic and be fully battle-tested to take anything thrown its way.

Successful data ingestion can be accomplished through a variety of industry-leading technologies such as Kafka and Hyprcubd. From an IoT set up to time-series database management, a powerful ingestion solution can ensure that your data flows seamlessly from source to database without losing any valuable information.

2. Data Maintenance

The next stage in a DLM is the maintenance of the ingested data. The data is hard to utilize unless it is cleaned, integrated, and enriched. By cleaning up the data before use and storage, you can ensure that you only store the best data in your limited and valuable resource space.

3. Data Synthesis

Not every company or enterprise will utilize a synthesis stage, but it is vital to understand as part of the life cycle. Automated computing resources are allocated to create and process valuable data through AI-based inductive reasoning programming in the synthesis stage.

Synthesis helps clean the data collected for AI data decision-making. The data synthesis process is often utilized for industries related to risk modeling, accounting, and those who are involved with investment decisions.

4. Data Usage

The data use phase of the DLM is the meat of the data lifecycle. At this phase, the data that has been collected and synthesized is now used for the purpose intended. It is necessary to have data retrieval technologies in place to access the data and act upon the information contained within.

High-quality data usage and retrieval application will allow for quick access. This requires that the storage and purging steps (included below) are followed to allow for better data access in your big data cloud database.

5.  Data Publication

Big data in the cloud can also be used by other entities or computing systems outside your business environment. In data publication, big data is sent or transferred from a business cloud database to another digital location for use by others.

This publication of the data must be completed with the necessary integrity and protections to ensure that the data is not degraded, lost, or put at risk. Having a fully-functioning data publication system in place can help your business better partner with others through your collected data.

6. Data Storage

Once you have effectively used the data you have ingested and cleaned, it is time for it to be stored. The storage phase of a DML is often the “beginning of the end” for the big data lifecycle.

The storage system is often based on the kind of data an enterprise is storing. Objects that are not changed often are best stored in an object storage system compliant with the S3 API. This provides durable and highly available storage based on the Reed-Solomon error correction algorithm. For files that are continuously changed, a distributed file system is a better choice. Great care must be taken when choosing a distributed file system as it can be costly in terms of license costs or very resource-intensive such as Hadoop’s HDFS.

An increasingly popular storage system for time-specific data storage is a time-series database system. This data storage system scales based on the timestamped information being frequently ingested by industries such as shipping or those that utilize IoT capabilities. Companies such as Hyprcubd can help companies develop and implement strong time-series data pipelines that scale with the size of data being ingested and stored.

7. Data Purging

After the data has finished the cycle and is no longer necessary for use or storage by a company, it may be purged to reclaim space. While purging may be a harsh term, sound data management systems must include a proper purging process to protect private data.

The purging process will often permanently remove the data from your storage database, freeing up valuable space and resources. However, purging also archives the data for future reclamation should you face an unexpected data loss or face a need to retrieve data for legal purposes.

Purging serves many benefits to big data management, including reducing the strain on your IT systems and allowing quicker access to cloud computing resources for current data. To qualify for increased data efficiency and more responsible data management, you must utilize a service that provides for consistent data purging as necessary.

Why Your Company Needs Strong Data Lifecycle Management

So why should your company invest in a robust, technology-backed DLM system? In short, businesses operate on data - the best companies operate on the best data. To ensure that your data works hard for you, it is imperative that you ingest, use, and store the data in the best ways possible.

Not only do robust DLM systems help increase your business effectiveness, but it will also protect your legacy from the risks inherent with big data computing. Handling big data responsibly and putting in the protections necessary to ensure proper data management will pay dividends for the future.

Consider just a few of the benefits of utilizing big data cloud DLM systems:

  • Allowing for all data management requirements for all sectors of your company or industry to be consistently met and exceeded - growing your integrity and reputation.
  • Building in a healthy data system protected from accidental or malicious incidents, and allowing for the reclamation of valuable data should the unthinkable occur.
  • Allowing for consistent and long-term big data cloud storage and maintenance for accessing data over the life of your business.
  • Helping your teams and data users access clean data that is easy to use and act upon.

Access The Best In Big Data Cloud Technology With Hyprcubd

Hyprcubd’s DLM data services can help you better ingest and utilize your data from start to finish. Featuring the latest in big data storage solutions, Hyprcubd’s team of data experts have the expertise and experience to help your team better collect and act on real-time data.

Hyprcubd offers a time-series database that allows for companies to maximize their data effectiveness across a DLM system. Not only can Hyprcubd help you increase your data effectiveness, but their big data expertise will help you better implement systems for your unique data needs across the data lifecycle.

Contact the Hyprcubd team today to learn more about DLM and various other big data cloud computing topics.  Complete the contact form, and a member of the Hyprcubd team will be in touch to discuss your options.

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