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What does modern data analytics mean for your business?

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Mackinnon Giddings
Mackinnon Giddings
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Data applications and other data-centric workloads are no longer the Wild West of the enterprise system world. Gone are the days of rogue data scientists spinning up siloed environments with shadow IT to run experiments that may or may not have executive support. Now, smart companies like yours are integrating advanced analytics techniques and the modern data platforms that support them into corporate strategies at the highest level.

In this first part of my blog series, I explain what modern data analytics means for your data storage and management needs, your engineering staff, and your business. And I tell you how NetApp can help.

Modern analytics: Access to data isn’t enough

In pursuit of improving sales, operations, and product offerings, businesses across sectors are developing data-centric strategies. And it’s no wonder why, when IDC has predicted that by 2025, the volume of data created every year will be over 160ZB. With numbers like that, it’s easy to see that access to data isn’t enough. Modern data platforms must also fulfill three characteristics: self-service, agility, and flexible scaling.

Self-service

Individual teams across functional groups within your company need to use your business data, without the gatekeeping access that your data scientists or business intelligence professionals have. The necessary level of access requires a platform that is not only accessible, but also intuitive to use. Your individual teams, whether they’re marketers, financial professionals, or part of the legal group, should be able to perform basic analysis, derive insights, and understand the context of the data that they have gathered.

Agility for groups across siloes

Software development has been agile for 2 decades. But legacy data management systems are complicated. Getting access, making changes, understanding context— simple functions become nearly impossible on old systems. A modern data platform focuses on two fundamental principles: availability and elasticity.

Flexible scaling

A modern data platform separates compute from storage. As a function of agility, flexibility in your infrastructure is key. As your projects grow, the data supporting them inevitably grows too. You need an infrastructure solution that’s robust enough for an enterprise system, but flexible enough to meet your data demands as they evolve. By using data lake technology, a modern data platform can store enormous amounts of data for a lower cost while being connected to the cloud.

NetApp and data analytics

NetApp provides innovative, industry-leading modern data analytics solution strategies that help you maximize business benefits, overcome data analytics challenges, keep pace with technology trends, and beat the competition. With NetApp® solutions, you can quickly move your legacy architecture to cloud and cost-effectively handle modern data analytics workflows, no matter what your industry or use cases are. We work with many valued technology partners, integrating our expertise with theirs to develop solutions that meet your specific needs.

With NetApp technology, it’s easy for you to manage and to move your big data, and the NetApp Active IQ® Digital Advisor tool helps you monitor and proactively improve system health, availability, and security. Your developers, data scientists, and data engineers can use NetApp DataOps Toolkit, a Python library, to clone a data volume or development workspace to provision a new one almost instantaneously. Our solutions also support big data architectures from Hadoop to Spark, and your software-defined storage

Whether your data analytics workloads require containers, enterprise data management, archiving, or tiering, NetApp has a solution to help. Our strategies promote simplicity, availability, high performance, and manageability of your data to meet the ever-increasing demands of modern AI and analytics workloads.

Start making the moves to a more agile, flexible, and self-service platform for your analytics workloads. To learn more, check out our hub page.

And be on the lookout for part two in this blog series to learn more about NetApp’s role in a modern data platform.

Mackinnon Giddings

Mackinnon Giddings

Mackinnon은 2020년에 NetApp 및 솔루션 마케팅 팀에 합류했습니다. 그동안 그녀는 엔터프라이즈 애플리케이션 및 가상화에 중점을 두었지만 인공 지능 및 분석에 대한 열정을 발견하게 되었습니다. 현재 마케팅 전문가로 일하고 있는 Mackinnon은 진정한 인간 경험과 혁신적인 기술의 교차점에 초점을 맞춘 메시징 및 솔루션을 제공하기 위해 노력하고 있습니다. 소프트웨어 개발, 패션, 소규모 비즈니스 운영 등 다양한 산업 분야에서 경력을 쌓은 Mackinnon은 참신한 외부인의 시각으로 AI 주제에 접근합니다. Mackinnon은 볼더 콜로라도 대학교의 Leeds School of Business에서 경영학 석사 학위를 취득했습니다. 그녀는 여전히 콜로라도에 거주하고 있으며 잠꾸러기 그레이하운드와 함께 지내며 빈 마고 와인 병을 수집하며 살고 있습니다.Mackinnon Giddings의 모든 게시물 보기

다음 단계

What does modern data analytics mean for your business?