We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 – 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Register today!
Successful businesses thrive on data, but what happens when there’s 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 ultimately clutters operations, making it difficult to achieve real value and creating risks and high costs.
Where once only data architects and database managers were solely responsible for managing data, it’s now everyone’s business. Data management is now shared by any business professional who generates, shares, uses and stores expansive amounts of data every workday.
How can you help your employees appreciate the need for proper data management to protect critical data from being lost or stolen? How can they know the difference between valuable and excess data? And how can they understand the total lifecycle of data as it traverses across multiple departments, devices and people?
The answer lies in the ability to understand and practice intelligent data management. If your organization struggles 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 applications and shared file systems like DropBox or other cloud-based storage services. But often, that 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 every year searching and not finding data.
A data assessment helps expand visibility beyond our primary roles and groups to discover how much data is in motion and at rest. This assessment provides a survey of data that can help highlight its value, reduce the risk of it being lost or stolen, and help estimate the costs, phases, and timeliness of any associated projects.
The assessment starts with discovering primary storage resources for databases and unstructured data silos throughout your organization. Primary storage could 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, email servers, files, emails, chats, and application data. In some enterprise-sized organizations, up to 80% of data is unstructured, can sit outside a database, and never be analyzed.
Identify your data
Once you have a clear picture of where your data resides, you’ll need to determine what kind of data you’re managing. Some of your data can be identified by your databases. But the most significant discoveries are found in pools of unstructured data.
Intelligent data management requires rapid and effective classification and identification of data across your enterprise. You’ll need to label data sources and elements with metadata to provide context into how each datum should be organized and handled. By indexing your data with metadata labels, you’ll identify network addresses, geolocations and essential characteristics of each datum, such as file names, timestamps, types and sizes.
Practice basic data hygiene
Once you know your data, you can start cleaning it up. Data hygiene practices help curtail data sprawl that causes unnecessary costs, process friction and risk. This usually begins with searches against your data assets to identify duplicate files and orphaned data.
You can then create data hygiene policies that assign goals to complex searches. For example, one policy may be to purge trash files or delete duplicate files. The policy can produce a limited list of data free of human errors and closely desired criteria to enable further action.
Secure your data ecosystem
Your data security concerns probably revolve around compliance with industry regulations and cybersecurity threats. Intelligent 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 should also quickly inform data stakeholders about incoming threats, latent or introduced data vulnerabilities, and potential privacy or compliance issues.
Determine when and how data should be securely locked or discarded. 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 needs to be retained, whether it is essential to conduct business or needed to meet the company’s compliance mandates. For example, financial data for a SOX audit must be held for seven years, whereas GDPR statutes in Europe dictate that user data should be eliminated as soon as it is no longer required.
Optimize your data
Most organizations leverage a variety of applications to move and store data. Their inventory might include, for example, cloud-based repositories, software-as-a-service (SaaS)-based productivity apps, streaming data services, or backup and recovery tools.
Instead of ripping and replacing any essential tools, intelligent data management should fully index data within these sources and destinations to improve optimization.
Capitalize on your data
Generally, excess data drives higher costs and more significant risks. But we also know 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 insight for better decisions, or deliver newer and learner services for customers.
This requires you to align data with your organization’s most critical use cases and then proceed to optimize other essential processes. For instance, a pharma research company might prioritize machine learning, whereas a property insurance firm may lean on improving incident management and claims resolutions.
Optimize your data to ensure high-performance responses to searches and application queries that meet employee and customer demands in every use case, and achieve more significant ROI.
You’re then clear to set policies for copying, moving, archiving, retrieving and deleting data, which is more adaptive and responsive to those workloads.
Intelligent data management can help you gain more significant ROI and socialize and share insights with all stakeholders in your organization. With real visibility 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.
Welcome to the VentureBeat community!
DataDecisionMakers is where experts, including the technical people doing data work, can share data-related insights and innovation.
If you want to read about cutting-edge ideas and up-to-date information, best practices, and the future of data and data tech, join us at DataDecisionMakers.
You might even consider contributing an article of your own!
Read More From DataDecisionMakers