Are you making decisions based on outdated data? According to recent studies, a staggering 82% of companies are. It’s time to unlock the power of your data by implementing effective data management strategies. In today’s fast-paced world, where data volume and complexity are increasing, having a comprehensive data management strategy is more crucial than ever.
Understanding Data Management
Data management is the process of ensuring that data is available, accurate, and well-secured throughout its lifecycle. With the right data management strategy, organizations can utilize their data to gain valuable insights, improve customer experience, and make informed decisions without compromising data integrity.
Without proper data management, businesses risk making decisions based on poor data, which can be worse than having no data at all. Additionally, they face a higher risk of data breaches due to inadequate protection and control measures.
The Data Lifecycle
Data, like living organisms, goes through different stages in its lifecycle: collection, storage, usage, archival, and destruction. Each stage requires a well-defined data management strategy.
Let’s illustrate the data lifecycle using a go-to-market launch as an example:
- Collection: A marketer gathers information from various sources, such as social channels, third-party marketplaces, and web analytics, to determine which distribution channels to prioritize.
- Storage: The collected data is consolidated into a single analytics suite and access permissions are set to control data access.
- Usage: The product marketing department analyzes the data to identify the most engaging and effective platforms for their go-to-market strategy.
- Archival: Once the go-to-market strategy is developed, the data is archived. This data may contain personally identifiable information about customers.
- Destruction: After the go-to-market strategy is no longer relevant, the data can be safely destroyed, though anonymized reports may still remain.
The collection and storage stages are often referred to as “Extract, Transform, Load” (ETL), where data is extracted from multiple sources, transformed into usable content, and loaded into a data management system, data lake, or data warehouse. ETL platforms are essential for consolidating product data spread across various channels.
Different types of data require different management approaches throughout their lifecycle. Sensitive, personally identifiable information (SPII) must be strictly controlled, while public information can be shared more freely.
Common Challenges in Data Management
As organizations manage multiple channels and employ diverse applications and systems, data management and analytics continue to grow in complexity. However, the right strategy, processes, and tools can simplify the data management process.
Data Analysis
Tracking and analyzing raw data is crucial, but without proper analysis and formatting, data can be misleading. Basic web analytics alone often fail to provide a complete picture.
Let’s imagine a baker who wants to assess the effectiveness of their marketing strategies. They have walk-ins, Facebook visitors, Twitter followers, and organic search engine traffic. How do they determine which channel is most effective?
At first glance, they might conclude that SEO is more valuable because they have 1,000 daily active organic visitors compared to only 20 users through Twitter. However, by digging deeper, they might discover that they obtained business from 10 Twitter users and only five from organic visitors. Data analysis software consolidates and normalizes information to provide better insights and more consistent strategies. Business intelligence suites, product analytics software, and search engine optimization platforms are valuable data management tools.
Data Privacy and Security Compliance
Data privacy laws require organizations to protect customer data. However, the increasing complexity of system architectures, often a mix of on-premise and cloud-based systems, creates potential security gaps.
Consider a brick-and-mortar retailer who needs to transfer customer information from an old point-of-sale system to a new one. While downloading the customer data as an Excel spreadsheet and saving it on a personal computer may seem convenient, it poses security risks. Even if the old and new systems are secure, a compromised personal computer could expose sensitive customer information. Employing a migration system that directly transfers data from the old system to the new one is a safer approach.
Implementing an Effective Data Management Strategy
Every organization requires a data management strategy that governs how data is stored, processed, and destroyed. A well-defined strategy not only ensures proper data management and control but also demonstrates due diligence, protecting organizations from liability in the event of a breach.
A comprehensive data management strategy includes:
- Business processes: How is data controlled throughout its lifecycle? How is it analyzed, archived, or destroyed? How does the organization guarantee data quality and the competence of data modeling?
- Accountable people: Who is responsible for data management? Who sets appropriate permissions and makes critical decisions regarding data assets and breach response?
- Data management tools: What tools are used to collect, control, and protect information? Are machine learning, artificial intelligence, or automation employed? Which data management system is utilized for storage?
To continually improve the strategy, organizations can conduct internal audits to identify any data management gaps and opportunities for increased efficiency. It’s essential to optimize the strategy to not only manage data but also use it effectively.
Enhance Your Data Management with Better Tools
Rather than starting from scratch, consider using tools that have data management features built-in. Amplitude Analytics is one such tool that collects and consolidates product-driven data, ensuring its protection, analysis, and security throughout the data management lifecycle.
Amplitude Analytics:
- Analyzes user-driven data to create a complete customer journey and provides key insights into the relationship between customers and products.
- Offers built-in collaborative solutions to keep your team aligned and moving in the same direction.
- Secures data with best-in-class security and compliance standards, including encryption and role-based access.
Boost your data management strategy today with Amplitude Analytics. Management