Self service analytics are becoming increasingly popular and essential in this data-driven world. For many businesses, there is a growing need for their internal departments to access their data and business intelligence and harness its power themselves.

Traditionally, business intelligence processes are the purview of IT teams and data specialists. Non-technical users must rely on these departments to perform queries, create visualizations, and generate reports to make data more digestible and insights readily available.

Such an approach can take a few days to several weeks. In hyper-competitive business landscapes, immediate access to data means more speed and agility. This then translates to faster time to market and resolution. Spending days waiting for IT teams to turn in their data analytics reports is simply no longer viable.

The advent of self-service analytics changes all that. Self-service data analytics tools enable nontechnical users to securely perform their own queries, create reports, run queries, and generate visualizations with nominal IT assistance.

Big data self-service analytics effectively address two critical issues faced by companies that rely on big data: the growing volumes of data and the glaring shortage of data scientists to capture, manage, and analyze it all.

For instance, many DevOps teams depended on IT to help make sense of their data to improve app development and other processes. With self-service analytics, DevOps can conduct data analytics themselves and quickly discover what’s required to support their applications, workloads, and more.

Why Are Self-Service Analytics Important?

Enterprises must recognize that self-service analytics are not the end goal. Rather, it is the impact they bring to their operations that matters.

Today’s businesses operate in an extremely rapid and unpredictable landscape. New products and apps are launched regularly alongside new workloads, experiments, and updates. Self-service analytics enable DevOps teams to access and leverage data quickly and securely so they can keep pace.

Simply put, DevOps teams self-serving their data analytics needs let them sustain their adaptability and become more data literate. When DevOps teams function with sustained adaptability and data literacy, they can deliver apps/workloads to the users faster and with better quality.

When businesses turn to self-service analytics, they also bolster the security of mission-critical information. Self-service BI tools eliminate the need for consultants and external parties to access and analyze enormous amounts of data.

An estimated 70% of business intelligence users are casual users. Self-service BI solutions help companies further enrich in-house data knowledge, which is critical to achieving competitive advantage by developing internal users’ knowledge and skills.

Enterprises unable to optimize their big data cloud can find themselves spending 40% more than their initial budget. Self-service analytics can also decrease cloud costs, especially when used alongside big data and application performance optimization solutions like Pepperdata.

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Self-service BI tools feed optimization platforms with insights that can help identify optimal configurations and improve resource utilization of their big data apps and processes, resulting in greater efficiency and big cloud savings. In addition, companies can meet their SLAs for businesses and IT, a positive outcome for all stakeholders.

What is a Self-Service Dashboard?

Many organizations are adopting self-service analytics tools to fully harness their data and gain maximum value. One of the best practices involving self-service data analytics is to combine self service with modern big data performance solutions to create a comprehensive big data analytics self-service dashboard.

Pepperdata provides users with a performance analytics dashboard that displays important metrics and correlates them to give users a deeper understanding into the performance of each application workload and cluster. Unlike other tools that only provide summary dashboards, Pepperdata dashboards provide visibility and operational insight.

With data from self-service analytics tools, Pepperdata can generate the most optimal configurations for your big data cloud infrastructure. More than that, Pepperdata offers powerful automation. Your infrastructure, processes, and applications are automatically right-sized and optimized once changes are recommended. This automation is critical for our users managing modern big data architectures.

The Challenges of Self Service Analytics

Many business intelligence experts say that the biggest challenge for self-service analytics revolves around data governance. Allowing different users to access large volumes of enterprise data through self service could hamper the quality of data and compromise data security. Different teams within the organization could create and execute their own data models and KPIs, resulting in data silos. 

To address data governance, enterprises must invest in a different tool. But that can also be a challenge. Companies require a solution that solves not only data governance but can also be deployed on prem and on the cloud, or both, as well as across different applications and frameworks.

Self-Service Analytics: Here to Stay

One study has found a 95% correlation between enterprises that make smart business decisions and financial success. In a world where fast access to data is essential for successful business outcomes, self-service analytics should only continue to grow in importance and use.

Self-service analytics enable non-technical users to utilize data to make strategic decisions. They put powerful resources into the hands of those with the greatest need to know. Self-service analytics allows organizations to grow and scale while meeting SLAs. Discover how you can maximize self-service analytics with our webinar. Watch Big Data Self-Service Performance Analytics: Best Practices with Pepperdata Field Sales Engineer Kirk Lewis now.

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