From data collection to data destruction, managing terabytes of data can be difficult. Ever since companies started to store data on hard drives, the growth of data has been exponential.

According to the IDG Data & Analytics Survey, the average company managed around 160 terabytes of data in 2016, and that number is likely higher today. To handle the massive amount of data, organizations use data lifecycle management (DLM) processes to collect, clean, store, and destroy enterprise-grade data. This article will explore the DLM process and corresponding software categories to help automate the DLM process.

An imperative approach to managing proprietary data

As the availability of data continues to grow, many organizations will try to adapt DLM for data generation, storage, usage, and retirement. This includes data from applications, systems, devices, databases, and media. With the seemingly perpetual growth of data needs of businesses, proper DLM has become more crucial than ever.

An overview of the DLM process

The entire DLM process involves data collection, data storage, data preparation, data usage, data maintenance, and data destruction. 

Data collection

Businesses typically collect data from three primary sources: social data, machine data, and transactional data.

The most traditional data that all businesses use is transactional data. Sales, payments, receipts, inventory records, shipment tracking numbers, and others are core data used to ensure daily operations. Businesses can use market analytics and business intelligence software to make meaningful predictions based on combinations of transactional data.

With social media usage at its peak, the need for a business to harness and understand its company data has become a top priority. It is essential for social media managers and marketing teams to know how social media campaigns are performing across all social channels, how and why accounts are growing in followers and engagements, and even track how their competitors are performing. 

With social media analytics software, users can digest all of this data and metrics through easy-to-understand visualizations. This is beneficial for social media managers or marketers who do not have data analysis knowledge, as well as for their managers or chief marketing officers who may be judging the results of their work better. Having this data readily accessible is a big positive for businesses and employees alike.

Even more revolutionary data comes from machines and IoT. The internet of things (IoT) is transforming industries worldwide, and perhaps none more than the manufacturing industry. Smart sensors and well-programmed IoT applications are helping convert factory equipment into functioning IoT devices, allowing them to generate actionable data, communicate with other machines, and optimize production. IoT devices are also installed on smart vehicles, temperature sensors, retail tracking systems, etc. 

Data storage

When businesses collect data, they can store it in a data repository. Modern companies today tend to store all of their data in a data warehouse. Data warehouse technology is used as a storage mechanism that pulls data from multiple disparate data sources into a single data store in an organized and efficient way to enable analytics and reporting for better decision making. It is different from traditional database technology, which can only record data. 

Data warehouse software is designed with integration and analysis in mind, unlike other databases designed to be queried in various ways. This helps users without the knowledge of SQL or other common querying languages to extract information from storage.

Companies can also set up individual databases instead of a single warehouse. This increases flexibility and reduces costs for smaller-scale projects. Databases can be set up on-premises, in the cloud, or hybrid. There are three major types of database software. 

Data preparation

Raw data is rarely usable, so organizations have to clean up the data for standardization and making corrections. 

Businesses use data preparation software to discover, blend, combine, enrich, and transform data for data standardization. With the help of this software, large datasets can be easily integrated, consumed, and analyzed with business intelligence and analytics solutions. Modern businesses need to make timely, critical decisions in response to the diverse insights generated by these tools. These tools compile analytics about product users, sales numbers, system performance, and more in real time. The tools in this emerging space help streamline the data preparation process, gleaning precise information from large datasets. As a business’s data continues to pile up, data prep tools enable users to find important data points with the push of a button. This way, companies can leverage actionable insights immediately without sorting through hours of data.

For data correction, companies use data quality software to analyze sets of information and identify incorrect, incomplete, or improperly formatted data. After profiling data concerns, data quality tools cleanse or correct that data based on previously established guidelines. Deletion, modification, appending, and merging are standard dataset cleansing methods. Data analysts, marketers, and salespeople, among others, benefit from leveraging data quality solutions.

Data usage

The goal of going through all these hurdles is to use data analytics to generate actionable business insights. Companies use data to understand all aspects of the business, including hiring forecasts, which marketing campaign to use for targeting certain demographics, which sales prospects to target first, supply chain optimization, and so on. Each of these business aspects and the decisions made around them should first be vetted by using data and business intelligence tools. 

There are many data analytics tools. For the past few years, the most hyped data analytics has been “big data.” Big data analytics software can consume large, unstructured datasets from big data clusters. Subsequently, they can connect all company data sources into a single platform to make cross-department connections, visualize and understand company data, and encourage data-driven decisions.

Another popular data analysis technique is text analysis. Text analysis software allows users to visualize data from unstructured text datasets. These tools often use natural language processing to pull out sentiment analysis, syntax parsing, part-of-speech tagging, and entity classification. Data teams and analysts often use text analysis tools to gain insights from emails and phone transcripts, social media posts, or general documents.

The next generation of data analytics tools focuses on predictive analytics. Predictive analytics software allows users to perform data mining on historical data to determine future outcomes. With this tool, analysts can build models and algorithms that use patterns and trends from past data to plan for future possibilities. These solutions are critical when forecasting, identifying potential risks, or finding unseen opportunities within the business.

Data maintenance

Data can also be corrupted by human errors, natural disasters, and cyberattacks. Businesses usually back up their databases on-premises or in the cloud to avoid losing data. Database backup is the process of saving a copy of a user’s current database in another location. Users can roll back to the previous versions of backup when needed.

Data is constantly changing, which creates different versions of the database. Many businesses have adopted database backup software to manage their data, including functionality to add, edit, and remove data as and when needed. Backups can provide valuable information about past versions of data, meaning companies can track how their data has changed over time and isolate specific changes to find trends.

Data destruction

Business transaction data is key to managing many important commercial aspects. Business owners depend on this data to keep track of income and expenditure, inventory, and other sales info in a convenient and secure place. When the data becomes outdated or too risky to hold, disposing data at the end of its lifecycle is essential. For example, organizations often destroy sensitive data or other confidential records when necessary.

Data destruction is vital because it prevents nefarious data exploitation. Proper data disposal reduces security risks. It’s imperative to ensure that your retired IT data doesn’t endanger your business. In the long term, improved audit trails can lead to greater business outcomes.

Data destruction also plays an important part in global data protection and data privacy law compliance. For example, Article 17 of the EU's General Data Protection Regulation (GDPR) grants EU residents a right to data erasure, also known as "the right to be forgotten". 

Recital 66 of the GDPR states:

"To strengthen the right to be forgotten in the online environment, the right to erasure should also be extended in such a way that a controller who has made the personal data public should be obliged to inform the controllers which are processing such personal data to erase any links to, or copies or replications of those personal data. In doing so, that controller should take reasonable steps, taking into account available technology and the means available to the controller, including technical measures, to inform the controllers which are processing the personal data of the data subject’s request."

While the GDPR does not specify the exact technical methods to comply with an erasure request, data destruction software would be a means to do so.

Data integrity must be a priority for businesses

Since DLM covers different departments, companies should ensure that all employees are aligned with DLM policies and processes. They should be iterable and clear to everyone so all data is properly collected, stored, and cleaned for easy access while holding high integrity. A data contingency plan should also be included as well to prevent permanent deletion. 

In conclusion, there are many aspects of an effective DLM strategy, so it is important to take time to explore each stage of DLM to prevent business failure due to data mishandling.