Smart data management recipe

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

An abundance of data ultimately obfuscates operations, making it difficult to achieve real value and creating high risks and costs.

When only data engineers and database administrators were solely responsible for managing data, it is now everyone’s business. Data management is now shared by any business professional who creates, shares, uses, and stores massive amounts of data every business day.

How can you help your employees appreciate the need for appropriate data management to protect important data from loss or theft? How can they tell the difference between valuable data and redundant data? And how can they understand the overall lifecycle of data as it passes through multiple departments, devices, and people?

The answer lies in the ability to understand and practice smart 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 important insights to help your business grow.

Learn 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 storage services. But often, this data is tribal and limited to our roles or departments. According to IDC, for every 1,000 people in an organization, an average of $5.7 million in labor costs is wasted each year looking for and not finding data.

Data assessment helps extend visibility beyond our basic roles and groups to discover how much data is moving and at rest. This assessment provides a survey of data that can help highlight its value, reduce the risk of its loss or theft, and help estimate the costs, phases and timing of any associated projects.

The assessment begins with the discovery of primary storage resources for databases and unstructured data silos throughout your organization. Primary storage can be located in on-premises servers, DAS/NAS/SAN resources, cloud-based data warehouses, and data lakes.

Unstructured data can reside in endpoints such as devices, shared drives, email servers, files, emails, chats, and application data. In some enterprise size organizations, up to 80% of the data is unstructured, can be placed outside the database, and is never analyzed.

Select your data

Once you have a clear picture of where your data is, you’ll need to decide what type of data you’re managing. Some of your data may be recognized by your databases. But the most important discoveries are found in unstructured data sets.

Intelligent data management requires fast and efficient data classification and identification across your organization. You will need to label the data sources and items with metadata to provide context on how each reference is organized and manipulated. By indexing your data with metadata labels, you’ll identify network addresses, geographic locations, and basic properties for each reference, such as file names, timestamps, types, and sizes.

Practice basic data hygiene

Once you know your data, you can start cleaning it up. Data health practices help reduce the spread of data that causes unnecessary costs, process friction, and risks. This usually begins with a search of your data assets to identify duplicate files and orphaned data.

You can then create data health policies that set targets for complex searches. For example, one of the policies might be to clear trash files or delete duplicate files. The policy can produce a limited list of data free from human error and closely desired criteria to enable further action.

Secure your data ecosystem

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

Strong monitoring of security and authorization events, identity, and access controls are good starting points for securing enterprise data. But these tools must also quickly inform data stakeholders about incoming threats, vulnerabilities inherent or presented in the data, and potential privacy or compliance issues.

Determine when and how data should be secured or safely discarded. Compliance and security concerns must be involved in the decision workflow of data on premises and in the cloud or through services. These decisions must cover the data that needs to be kept, whether it is necessary to conduct business or needed to meet the company’s compliance mandates. For example, SOX audit financial data must be kept for seven years, while Europe’s GDPR laws state that user data must be deleted as soon as it is no longer needed.

Improve your data

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

Rather than copying and replacing any core tools, intelligent data management should index all of the data within these sources and destinations to improve optimization.

Take advantage of your data

In general, excess data leads to higher costs and greater risks. But we also know that data is essential for enterprise organizations to survive and thrive. To maintain this balance, you must extract maximum value from your data, whether it is used to make employees more productive, improve our strategic vision to make better decisions, or provide newer and educated services to customers.

This requires you to align the data with the use cases that matter most to your organization and then proceed to improve other core processes. For example, a pharma research company may prioritize machine learning, while a property insurance company may rely on improved accident management and claims resolution.

Optimize your data to ensure high-performance responses to searches and application inquiries that meet employee and customer requirements in every use case, and deliver a more significant ROI.

It then becomes clear that policies for copying, moving, archiving, retrieval and deletion of data are set, which are more adaptive and responsive to these workloads.

Intelligent data management can help you get more meaningful ROI, socialize, and share insights with all stakeholders in your organization. With real insight and knowledge, everyone can better understand the nature of the data they interact with every day.

Adrian Knapp is the CEO and founder of Aparavi.


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