Agile Data Management Could Reshape How You Work With Data

Your Data Should Be Agile, Too

The concept of agile software development has revolutionized the way code is written and deployed over the past decade. Could it also reshape data management and analytics? In short, yes. So, let’s talk about agile data management.

Agile Data Management Could Reshape The Way You Work With Data

Defining Agile

Within the context of software development and delivery, agile refers to methodologies, tools, and philosophies that help an organization stay flexible.

More specifically, an organization that is agile can:

  • Scale its software development operations up or down quickly, in response to fluctuations in demand.
  • Switch between different toolsets or development frameworks easily. Agile means not being locked into any single platform.
  • Take on new projects or change direction seamlessly whenever conditions change in the market or the company seeks to redefine its mission.
  • Respond effectively to customer needs and expectations.

The agile concept was introduced in the early 2000s. Later, it started going mainstream. Over the last decade, it has been the driving force behind the DevOps movement.

Today, a focus on being agile helps organizations to deliver faster, more reliable and more relevant software than they could a decade ago.

The agile movement also tends to lower software production costs. Rather than being constrained by rigid platform dependencies or requirements that are not cost-efficient, it frees development teams to leverage the tools and methodologies that best meet their needs.

Download our eBook: The New Rules for Your Data Landscape

Extending the Agile Concept to Data Management

To date, the agile concept has been applied first and foremost to the world of software development and deployment. Data storage, management, and analytics teams have not been part of the agile revolution

But it’s time for that to change. The data management world has much to gain from embracing the agile concept, too.

An organization that applies an agile model to the way it works with data should be able to:

  • Work with data no matter which format it’s stored in or which type of environment hosts it.
  • Use any type of analytics tools to interpret the data.
  • Move data quickly and easily between different servers or environments.
  • Work with data at any scale.
  • Communicate information effectively between different individuals who play a role in working with data – from storage engineers to analysts.

How to Achieve Agile Data Management

Those are the goals of agile data management. How do you achieve them?

Agile Data Management Could Reshape The Way You Work With Data

Part of the answer lies in cultural change. Just as agile software development and delivery requires the right cultural approach, agile data management requires developing a culture in which your organization is always open to change, ready to adapt to new challenges and eager to embrace new tools and technologies.

But tooling is a significant part of the solution for achieving agile data management, too. With the right tools in place, making the transition from a rigid approach to data management to a flexible, agile one is easier.

That’s where data management solutions like Syncsort DMX-h come in. By enabling seamless and automated data transformations, helping organizations move data easily from legacy environments into modern platforms of their choice and turning batch data into streams, Syncsort’s Big Data solutions facilitate an agile approach to data management and analytics. The result is greater business efficiency and more meaningful insights from data.

Data management is evolving. Discover the new rules for how data is moved, manipulated, and cleansed – download our new eBook: The New Rules for Your Data Landscape

Christopher Tozzi

Authored by Christopher Tozzi

Christopher Tozzi has written about emerging technologies for a decade. His latest book, For Fun and Profit: A History of the Free and Open Source Software Revolution, is forthcoming with MIT Press in July 2017.

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