In the 21st century, data is power. Although most organizations now understand the power of data collection, far fewer organizations realize the importance of removing certain kinds of data from your enterprise systems.
When data is not properly ingested, maintained, organized, deleted or archived, it can quickly become a vulnerability. Not having a well-designed, comprehensive data lifecycle increases your organization’s risk of compromise, and reduces the efficiency and effectiveness of accessing data to meet business objectives.
Many organizations spend a tremendous amount of time, money, and resources on protection strategies, such as cryptographic measures or disaster response and recovery solutions — but their data management processes don’t always address the challenges of data minimization.
This article will explore the root causes of this often overlooked data management challenge, and discuss why organizations should foster a cleaner and safer enterprise data environment.
The Data Lifecycle
In order to manage their data, many enterprises follow some iteration of a standard data lifecycle. By first understanding and documenting the data lifecycle, businesses can better understand, organize, and utilize their data in a reliable and sensible manner. There are five general stages involved in the data lifecycle:
- Generation and Collection — Establishing rules that standardize how all data types are gathered. This is essential for data management later in the data lifecycle.
- Usage and Analysis — Locating specific data, generating actionable insights, and making decisions according to these discoveries. Data publication is also an element of this lifecycle stage.
- Storage — Creating policies around which data becomes an active asset, and which data should become archived or deleted. Backups and data recovery should also be considered.
- Maintenance and Cleaning — Inspecting, validating, and enriching data to ensure the right data is in the right place. Some organizations also enact data integration or data synthesis during this stage.
- Deletion and Removal — Forming policies around which data is critical to keep around, as well as which data no longer serves a purpose and should be purged from the enterprise.
The data lifecycle allows organizations to determine what layers of protection are required for particular data, especially critical data, both while at rest and in transit. From there, leadership can take decisive action to implement the necessary measures and controls to protect the right data, for the right reasons. By effectively gathering, storing, maintaining, and cleaning their data, organizations remove unnecessary costs and reduce operational burden. This will also help them easily locate critical data and react quickly in times of crisis.
Why Organizations Struggle with Deleting Data
Documenting the data lifecycle enables repeatable and resilient data management processes, but many organizations struggle with data deletion. This is in part due to a misunderstanding of how essential it is to appropriately remove data from the enterprise environment once it is no longer required or useful.
We tend to think of certain data — particularly proprietary enterprise data, customer data like PCI or PII, and other sensitive information — as riskier than other data types. As a result, some organizations don’t believe their data is “important” enough for hackers to care about. They don’t see the value in regularly purging their database system, so they do the opposite: hoard their data indefinitely.
The truth is that any and all data existing in an enterprise environment is prone to cyberattack. Having a significant amount of old or obsolete data, most of which no longer holds value or relevance, can make accessing the critical data quickly and efficiently more difficult. Holding on to extraneous data also drains resources, time, and effort from security, risk and compliance teams.
By shrinking their data footprint, organizations can reduce the scope of assets that require protection. This promotes efficient protection strategies and reduces the risk of cyberattacks or compliance failures.
As the amount of data in enterprises continues to grow at an alarming rate, deleting inactive data becomes increasingly critical to preventing cyberattacks. IDC predicts that the Global Datasphere will grow to 157 Zettabytes by 2025, upwards from 33 Zettabytes in 2018 — a 430% increase over seven years. With every data point posing a potential threat, organizations should strive to remove as much data as possible.
Organizations must improve the ways in which they organize, store, and delete enterprise data in order to avoid unnecessary costs and to mitigate risk. By properly understanding and managing the stages of the data lifecycle, companies can better protect their enterprise data and databases.
How Can We Better Follow the Data Lifecycle?
Following the data lifecycle requires a company-wide mindset that prioritizes continuous data management, categorization, and deletion. By organizing and storing data appropriately, and removing data that is not significant, businesses can extract the right messages from the data and reveal valuable business insights.
Data lifecycles within organizations should be continuous, iterative, and sustainable. For effective and comprehensive data management, key stakeholders must be proactive when organizing data — how stakeholders name documents, share files, and label storage spaces, has a notable impact on data organization, which affects the later stages of the data lifecycle. Companies can also leverage tools already existing in the enterprise environment, such as Role-Based Access Control (RBAC), User-Based Analytics (UBA), and Data Loss Prevention (DLP) tools, to shrink vulnerabilities related to data access and management.
Organizations can use Enterprise Data Discovery and Classification tools to automatically classify data within common enterprise environments. These tools take a model file or repository, assign a data type, and then search for similar items elsewhere to classify. End-users are then given the opportunity to review and tag data, promoting machine learning so the tool becomes more accurate with time.
By taking the time to truly understand every client’s challenges, SEI supports organizations in picking the right tool for their requirements. We also assist organizations in implementing these tools, establishing communication between stakeholders, and ensuring leaders understand the importance of proper data maintenance.
Make the Most of Your Data With SEI
At SEI, we understand the importance of enacting proper data management, classification, and deletion. We’ve helped data owners across many industries gain important insights from their data by enacting all stages of the data lifecycle. These insights can then be leveraged to drastically reduce cybersecurity vulnerabilities, reduce data management costs, and to drive improved business results through more effective data analytics.
Establish a cybersecurity culture within your organization with SEI and extract the right information from your data while keeping it safe from malicious entities. Reach out to learn more!