5 Best Practices for Simplifying Data Management
Businesses have been managing data for decades. Tasks like dealing with data silos, keeping data secure and preparing data to be analyzed are nothing new.
What is new, however, is the scale and complexity of data management. The total volume of data that businesses manage is exponentially increasing. At the same time, more and more data is moving to the cloud, where data management often requires new techniques or tools (like configuring cloud IAM settings and networking rules to secure data) that don't apply on-premises.
But the fact that data management is constantly growing more challenging doesn't mean that managing data today has to be fundamentally more difficult than it was in the past. By thinking strategically about managing data – especially in the cloud – it's possible to simplify data management operations.
With that goal in mind, let's take a look at five actionable practices that can make data easier to manage, no matter how much data you have or how complex your data storage architectures are.
READ: Logging Blindspots: Top 7 Mistakes that are Hindering Your Log Management Strategy
1. Know your data
The first step in simplifying data management is to know which data you have to manage in the first place.
You may assume that you already know where your data is. But in modern, distributed environments that include a range of different data storage assets – like on-premises RAID arrays, network file shares, databases, cloud object storage and beyond – it can be easy to overlook data stores. You may also not be sure which types of data you have.
That's why it's important to step back and catalog or map your data. Document all of the locations where your data lives, as well as which types of information you store. This visibility provides the foundation for simpler data management.
You can document your data manually, of course. But to simplify the process, consider leveraging a tool like ChaosSearch, which can automatically create a searchable index for your data without actually moving the data. Once you have a data index, it becomes much easier to parse and analyze all of the information at your disposal, even if the data itself is stored in locations (like AWS S3) where the data is not structured or easily searchable.
2. Identify data silos
When you know where your data lives, you can also identify data silos. A data silo is a data set that is difficult to share across the organization. Typically, data becomes siloed because one business unit or department "owns" the data and doesn't configure it for easy accessibility by others.
You should determine where data silos exist, then take steps to increase accessibility. Although you may not want every data set to be accessible by everyone in the business, you also probably don't want some data to be hidden away by one department, with other business units deprived of access.
READ: Unlocking Data Literacy Part 2: How to Set Up a Data Analytics Practice That Works for Your People
3. Choose a cloud data platform
If you currently lack a unified, deliberate data management strategy, there's a good chance that your data is distributed across a sprawling set of on-premises and cloud-based infrastructure.
To simplify that data architecture, choose a cloud data platform where you can centralize storage and analytics operations for all types of data. Cloud data platforms allow you to store and analyze data in multiple forms – including structured data like SQL databases, as well as raw "data blobs," such as individual files that are not part of a filesystem hierarchy or database structure.
Although you may not be able to move every bit of data to a cloud data platform, storing and processing most data in a centralized, cloud-based location will simplify your overall data management operations by reducing the number of variables involved in data storage and analytics.
READ: Unlocking Data Literacy Part 3: Choosing Data Analytics Technology
4. Establish data tagging
Data is a lot easier to manage when it's labeled with tags that describe what the data is, who created it and which special requirements apply to it.
Toward that end, consider establishing a data tagging policy. Your policy should define which data needs to be tagged, and which information should be included on tags. These rules can become part of your broader data governance strategy.
While there is no guarantee that all data will actually be tagged, making data tagging a deliberate policy is a good way to reduce visibility gaps within your data architecture.
5. Track data lineage
Data lineage is information that records how data originated and which changes it underwent to reach its current form. For example, the data lineage for a database might detail who created the database, which columns were added since its creation and which merge operations took place over the course of the database's lifetime.
Tracking data lineage is another way to enhance visibility into your data and simplify data management. The more you know about where each data set came from and how it has evolved over time, the better your decisions will be about how to store, secure and manage that data.
Managing data is likely to become only more and more challenging over time. Data for the typical business will steadily grow in volume, and data will be spread across more and more locations.
Despite these trends, however, it's possible to tame data management complexity. When you have full visibility and searchability over your data, you can keep your management operations simple, no matter how complex the underlying data is.
Read the Blog: Managing the Mess of Modern IT: Log Analytics and Operations Engineering
Listen to the Podcast: Differentiate or Drown: Managing Modern-Day Data
Check out the Whitepaper: The New World of Data Lakes, Data Warehouses and Cloud Data Platforms