We’ve all heard the phrase “data is king.” And more and more data is being generated, both in our personal and professional lives.
Until now, data storage has often been an afterthought, and data creation has been a priority. However, organizations are finding it increasingly difficult to manage the growth in data they create.
We find that most organizations look at data generated over the past week, month, or quarter and explore data (such as reports) based on short-term requirements. Some data types may be used for year-over-year comparisons (think financial data). However, if left unchecked, this data sprawl can become unmanageable.
Backups, and more importantly restores, can be very time-consuming and disruptive. If you need to restore data in a production environment, the longer the process takes, the more likely it is to have a significant impact on your company’s brand and reputation. Therefore, it is important to restore it cleanly as soon as possible.
Data sprawl can also cause database queries used in applications and reports to be crawled. No one wants to wait an hour for him to run a report.
However, much of this data, including the oldest elements, still holds value and may serve a purpose. This is especially true today as AI models become more prevalent and companies look to retain and use data for training purposes. As even the oldest data is given new purpose, businesses must address the growing need to maintain and store data for longer periods of time. Therefore, it is important for organizations to critically evaluate their data and determine what they truly need to keep.
Dealing with data management dilemmas
A critical step is to ensure that your organization’s operations and development teams are connected and can work together effectively. The DevOps movement promises to enable this cross-functional harmony. This sounds great in theory, but it doesn’t always work in reality. Operations teams and developers have very different priorities. Development teams are primarily focused on feature speed and release frequency, while operations teams are focused on data management strategies (offloading, archiving, purging, etc. of old data). This disconnect can often lead to an impasse where the same old challenges persist without much change.
Therefore, it is important to identify and implement a data management strategy that segregates data based on utility and use case. After all, it is impossible to effectively manage data without knowing its value, and it is impossible to know its value without knowing its purpose. Therefore, any effective data management strategy, especially one focused on reducing sprawl, should make separation and classification a primary goal.
Effective use of metadata is one of the most fundamental steps to realizing such a strategy. To effectively separate and classify data, organizations must ensure that their metadata is consistent, detailed, and robust to ensure consistency across applications and to understand the data’s purpose and business use case. It must be possible to identify it quickly and accurately.
Data quality is another pillar of an effective management strategy. Inconsistencies caused by data silos, lack of standardized processes, and lack of effective screening and validation methods often undermine an organization’s ability to effectively manage data and reduce sprawl.
A data-driven world starts with company culture
Ultimately, prioritization is key. It’s important to ensure that old legacy data is archived or purged, and that the newest or most frequently used data is optimized, aligned, and as efficient as possible.
However, this brings us back to effective collaboration. Successfully separating data requires operations teams and developers to work together and maintain open lines of communication based on each team’s wants and needs. When pushed into silos, neither team can effectively identify and prioritize data. Cultural change is often the most powerful and important data management strategy an organization can adopt. DevOps provides a helpful paradigm, but ultimately most organizations must address cultural considerations in their own way.
Data generation and consumption are increasing exponentially, and artificial intelligence and machine learning are ushering in a future where even the oldest data can be given new life.
As such, the practice of simply “delete old” is quickly becoming a thing of the past, and today’s organizations must seriously consider prioritizing their long-term data management strategies.
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