Cloud Data Management Strategy: 3 Keys for Simplification

Cloud Data Management Strategy: Keys for Simplification

This is part of Solutions Review’s Premium Content Series, a collection of contributed columns written by industry experts in maturing software categories. In this submission, ChaosSearch VP of Products Greg Goldsmith offers a cloud data management strategy overview and essential keys for simplification.

SR Premium ContentEffective cloud data management has always been essential for organizations. Data is crucial for discovering new growth opportunities, addressing operational inefficiencies, and enhancing business decisions that can boost bottom lines. But managing the vast variety and amount of data organizations produce is no easy feat–and it’s only going to get more challenging in the coming years.

The scale and complexity of data management has increased dramatically, particularly as data volumes continue to grow exponentially worldwide. In response to this growth, data storage capacity is increasing, with IDC estimating that the data storage sphere will more than double between 2022 and 2026. Unfortunately, more data storage doesn’t necessarily translate into robust data management. Organizations still need strong data management processes if they’re going to leverage all of the data at their disposal. This will become even more critical as more data moves to the cloud, which will require new data management techniques and tools from the ones used to manage on-premise data.

Here are three steps every organization can take to navigate complex, growing data environments and effectively manage that data to inform operational strategies:

Cloud Data Management Strategy Overview

Conduct Regular Data Audits

To manage data successfully, companies need to know what they have and where they got it. While this may seem obvious, the reality is that many leaders don’t have an up-to-date view on their organization’s data – Gartner forecasts cloud spending to reach half a trillion in 2022, yet only 40 percent of companies reported that they were managing data as an asset. There are often too many data sources and use cases that can emerge across different departments. That’s why the first step to simplifying data management is to conduct regular data audits.

Data auditing involves taking inventory of data stores and data types. Teams should know where they keep all their data, as well as what data silos exist in the organization. Data silos have the potential to hinder collaboration and keep teams from making the most informed decisions possible. Furthermore, it’s imperative to evaluate data processes and data sources to ensure both are secure, yet accessible to the right people

Ultimately, thorough data audits help teams optimize data infrastructure for modern applications. They provide more insight into overall data management effectiveness and set the stage for the other two best practices described below.

Establish a Consistent Data Tagging System

Knowing what data the organization has is useful, but it’s only part of the solution when it comes to data management. On top of conducting audits, data teams today should tag data in a way that is consistent and descriptive to others throughout the enterprise.

The purpose of a detailed data tagging system is to show where data originated, what it describes, and any special conditions that users should keep in mind. Tagging also increases data visibility, which can help data teams address any gaps in data architecture. This enables companies to organize data efficiently, expedite search queries, and increase time to insights.

To keep data tagging consistent throughout the enterprise, organizations should establish policies that clearly indicate what data needs to be tagged and how. These policies can fit in nicely with broader data governance strategies that aim to maintain data integrity across the entire business.

Know the Lineage of Your Data

The first two cloud data management strategies provide data teams with snapshots of their organization’s data at a moment in time. To really bolster data management, however, organizations should track how their data changes over time – also known as recording data lineages. This requires expanding data retention practices significantly.

Keeping more data for longer periods of time opens up many new opportunities for modern organizations. Companies can identify market trends or patterns that would otherwise stay hidden. Moreover, data lineages allow data teams to see how specific data types evolve. As a result, leaders can draw cleaner connections between past data-driven decisions and current performance to inform future strategies.

To give an example of this concept in the real world, a company setting up a database might want to record the following pieces of information: the originator of the database, the initialization date, any columns that have been added or deleted since, and any changes the data has gone through up until this point. Having this information on hand enables organizations to maintain a continuous view of their data, regardless of how teams and priorities change.

How a Centralized Cloud Data Platform Makes These Strategies Easier

Together, these practices improve data visibility dramatically and facilitate processes that simplify data management. But employing them in the real world is harder than it sounds without the right technology.

Organizations today need a centralized cloud data platform that enables unlimited data access and storage, without any data movement, transformation, or pipelining. Conducting audits, tagging data, and tracking lineages is easier when everything is in one secure and scalable location. The cloud frees companies from having to rely on distributed or hybrid environments, and it does so in a way that can handle the data explosion we’re experiencing (and are likely to continue to experience).

The key is choosing the right cloud platform, as well as cloud-native tools that streamline data storage and analytics. Data teams know they’ve found a viable solution when the practices outlined here are easy to implement. Companies need to rethink their data management strategies and tools in order to keep up with the complex data environments and growing data volumes that exist today. Collecting, processing, analyzing, and using data at scale to its fullest potential will be critical when navigating dynamic markets and future disruptions.

Greg Goldsmith
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