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Successful companies thrive on data, but what happens when there is too much data? Too many organizations are drowning in a sea of ​​data from the information they create, collect, and receive from sensors and devices.

An overabundance of data ends up cluttering operations, making it difficult to get real value and creating high risks and costs.

Where once only data architects and database managers were solely responsible for data management, now it is everyone’s business. Data management is now shared by any professional who generates, shares, uses and stores large amounts of data every working day.

How can you help your employees understand the need for good data management to protect critical data from loss or theft? How can they tell the difference between valuable data and excess data? And how can they understand the full lifecycle of data as it traverses multiple services, devices, and people?

The answer lies in the ability to understand and practice intelligent data management. If your organization is struggling to stay afloat in a sea of ​​data, here are six steps to help your employees learn how to manage data and extract critical insights to help your business grow.

Know where and how to find data

Most of us know how to find data in our apps and shared file systems like DropBox or other cloud-based storage services. But often, these data are tribal and limited to our roles or services. According to IDC, for every 1,000 people in an organization, an average of $5.7 million in labor costs is wasted each year searching and not finding data.

A data assessment helps extend visibility beyond our primary roles and groups to uncover the amount of data in motion and at rest. This assessment provides a survey of data that can help highlight its value, reduce the risk of loss or theft, and estimate the costs, phases, and timeliness of any related project.

The assessment starts with discovering the main storage resources for databases and unstructured data silos across your organization. Primary storage can exist in on-premises servers, DAS/NAS/SAN resources, cloud-based data warehouses, and data lakes.

Unstructured data can exist in endpoints such as devices, shared drives, mail servers, files, emails, chats, and application data. In some enterprise-sized organizations, up to 80% of data is unstructured, can stay outside of a database, and never be parsed.

Identify your data

Once you have a clear idea of ​​where your data resides, you need to determine what type of data you are managing. Some of your data may be identified by your databases. But the most important discoveries are in pools of unstructured data.

Intelligent data management requires fast and efficient classification and identification of data across your enterprise. You’ll need to label data sources and items with metadata to provide context for how each piece of data should be organized and managed. By indexing your data with metadata tags, you’ll identify network addresses, geolocations, and key characteristics of each piece of data, such as filenames, timestamps, types, and sizes.

Practice basic data hygiene

Once you know your data, you can start cleaning it up. Data hygiene practices help reduce data proliferation that leads to unnecessary costs, process friction, and risk. This typically starts with searches through your data assets to identify duplicate files and orphan data.

You can then create data hygiene policies that assign purposes to complex searches. For example, a strategy might be to purge files from the Trash or delete duplicate files. The policy can produce a limited list of human-error-free data and narrowly desired criteria to enable further action.

Secure your data ecosystem

Your data security concerns likely revolve around compliance with industry regulations and cybersecurity threats. Smart data management practices can cover both.

Robust security event monitoring and authorization, identity and access controls are good starting points for securing enterprise data. But these tools also need to quickly notify data stakeholders of incoming threats, latent or introduced data vulnerabilities, and potential privacy or compliance issues.

Determine when and how data should be locked or securely deleted. Compliance and security concerns should share a decision workflow for data on-premises and in the cloud or through services. These decisions should cover what data should be retained, whether it is essential to the conduct of business or necessary to meet corporate compliance mandates. For example, financial data for a SOX audit must be retained for seven years, while GDPR statutes in Europe state that user data must be disposed of as soon as it is no longer needed.

Optimize your data

Most organizations leverage a variety of applications to move and store data. Their inventory may include, for example, cloud-based repositories, software-as-a-service (SaaS)-based productivity applications, streaming data services, or backup and recovery tools.

Instead of extracting and replacing essential tools, intelligent data management should fully index data within these sources and destinations to improve optimization.

Capitalize on your data

As a general rule, too much data leads to higher costs and greater risks. But we also know that data is essential for businesses to survive and thrive. To maintain this balance, you must extract the maximum value from your data, whether it is used to make employees more productive, improve our strategic insight for better decisions, or provide new and learning services to customers.

This forces you to align data with your organization’s most critical use cases and then optimize other critical processes. For example, a research-based pharmaceutical company may prioritize machine learning, while a property insurance company may rely on improved incident management and claims resolution.

Optimize your data to ensure high-performance responses to search and application queries that meet employee and customer demands in every use case, and achieve a more meaningful return on investment.

You are then free to define policies for copying, moving, archiving, recovering and deleting data, which are more adaptable and responsive to these workloads.

Intelligent data management can help you achieve greater ROI and socialize and share information with all stakeholders in your organization. With real visibility and knowledge, everyone can better understand the nature of the data with which they interact on a daily basis.

Adrian Knapp is the CEO and Founder of Aparavi.

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