What Is Data Management? Strategies & Examples - Data Management 101

Eighty-two percent of companies make decisions based on stale data—data that is no longer useful. An organization must have the right processes and tools in place to use its data effectively; this is the process of data management.

But for many, data management is constantly becoming harder. Data increases in both volume and complexity; products are sold throughout the world. A comprehensive data management strategy makes it easier for an organization to ensure that the data it collects—such as its product analytics data—is accurate, complete, and secure.

What is data management?

Data management helps organizations get the most value from their data by ensuring it’s available, accurate, and well-secured throughout its lifecycle. Organizations may use data to analyze products and user behavior or improve customer experience. Generally, an organization wants to get the most use out of its data without compromising it.

Without proper data management, an organization may be taking action on poor data—and that could be worse than no data at all. It will also be at a greater risk of a data breach because its data will not be adequately protected or controlled.

What is the data lifecycle?

Like an organism, data goes through a lifecycle: collection, storage, usage, archival, and destruction. Organizations must have a data management strategy for each of these stages.

Let’s look at the data lifecycle using a go-to-market launch as an example.

• Collection: A marketer is planning the launch of a new product but wants to determine which distribution channels they should prioritize. The marketer collects information from each of their social channels, third-party marketplaces, and their own web analytics.
• Storage: The marketer then has data but needs to store it. The marketer consolidates the data into a single analytics suite and sets the appropriate permissions to control access to the data.
• Usage: The entire product marketing department analyzes the marketing data within the consolidated platform, looking at which platforms provide the most engagement, reach, and commitments.
• Archival: Once the go-to-market strategy has been developed, however, the data is archived; it may contain personally identifiable information about customers.
• Destruction: After the go-to-market strategy is no longer relevant, the data can be safely destroyed, although anonymized reports may remain.

Collection and storage are sometimes referred to as “Extract, Transform, Load” (ETL)—extracting enterprise data from data sources, transforming it into usable content, and loading it into a data management system, data lake, or data warehouse. For a marketer, ETL platforms may be essential, as product data may be spread across multiple channels.

Different types of data must be managed differently throughout their lifecycle. Sensitive, personally identifiable information (SPII) must be strictly controlled—while public information can be shared more freely.

Common data management challenges

Today, organizations must manage multiple channels and secure their data across multiple apps and systems. The world of data management (and data analytics) is becoming more complex—but the right strategy, processes, and tools can simplify it.

Data analysis

Not only do organizations need to track raw data, but that data has to be analyzed and formatted correctly. When data isn’t analyzed properly, it can be misleading.

A baker wants to know how effective his marketing strategies are. He knows he has walk-ins, Facebook visitors, Twitter followers, and a good deal of organic search engine traffic. But how does he know what channel is most effective?

Our baker has 1,000 daily active organic visitors, but only 20 users through Twitter. With that data set alone, he might conclude that SEO is more valuable than Twitter. Digging deeper, though, he may have gained business from 10 Twitter users and only five from organica channel visitors. Basic web analytics alone often can’t tell the whole story.

Data analysis software consolidates, normalizes, and analyzes information to yield better results and more consistent strategies. Data management tools could include business intelligence suites, product analytics software, and search engine optimization platforms.

Data privacy and security compliance

Data privacy laws require organizations to keep customer data secure. But businesses of all sizes are mixing on-premise and cloud-based systems to create a hybrid architecture, and increasing system complexity creates potential security gaps.

A brick and mortar retailer changes her point-of-sale system, and she needs to bring over all of her old client information from one system to another. How does she ensure that data is safe?

Our retailer transfers her customer information (including names, email addresses, phone numbers, and physical addresses) by downloading it as an Excel spreadsheet. She saves that on her computer desktop and then uploads the spreadsheet to her new data management platform.

This is risky—if her personal computer is not properly secured, all of that PII could be compromised. And that will happen regardless of how secure her old and new point-of-sale systems are. Ideally, she would use a migration system that shifts data directly from her old system to her new system. This should be part of her broader data management strategy.

Implementing data management

Every organization needs a data management strategy—a strategy that governs how the organization stores, processes, and destroys its data. Not only is this essential for managing and controlling the processes, but it also protects the organization from liability in the event of a breach by demonstrating due diligence.

A data management strategy must include:

• Business processes - How is the organization’s data controlled throughout its lifecycle? How is it analyzed, and when is it archived or destroyed? How does the organization guarantee its data quality and the competency of its data modeling?
• Accountable people - Who is responsible and accountable for data management? Who ensures that permissions are set appropriately? Who will make key decisions regarding how data assets are handled and respond in the event of a breach?
• Data management tools - What tools are used to collect, control, and protect information—machine learning, artificial intelligence, or automation? What data management system does the organization use for storage?

It’s a best practice to further improve a strategy by regularly auditing and improving it. During an internal audit, the organization may identify data management gaps or find opportunities to increase efficiency.

Ideally, a strategy should be optimized; the organization doesn’t just need to control its data but also use it as effectively as possible.

Enhance your data management with better tools

Rather than struggling to create a data management strategy from the ground up, consider using tools with data management already built in.

Amplitude Analytics collects and consolidates product-driven data—protecting, analyzing, and securing it throughout the data management lifecycle, so you can use it without worrying about it.

• Analyzes user-driven data to create a complete customer journey, with key insights into the relationship between customer and product.
• Offers built-in collaborative solutions to keep your team on the same page and moving in the same direction.
• Secures data with best-in-class security and compliance standards, including encryption and role-based access.

Start boosting your data management strategy today with Amplitude Analytics. Sign up to get started for free.