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Optimize Data Management

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Events over the past years, such as a surge in remote work and ongoing digital transformations, require businesses to make investments that contribute to a massive explosion of data in volume, source, and complexity. Because of that large amount of telemetry, organizations struggle to efficiently collect, manage and store their data, and are challenged to make best use of it for their monitoring and troubleshooting needs. As a result, ITOps teams often miss key insights hidden within volumes of data - leading to longer incident response, while also having to deal with increasingly rising costs associated with the high telemetry volume.

To gain efficiency and accelerate insights, organizations must collect data from diverse sources, and apply the right analytics to transform data into actionable metrics for complex analyses and correlations. With proper data management, data is in the right shape to troubleshoot faster, enabling seamless, enterprise-wide visibility into data in motion across a multitude of environments, optimized for cost-effectiveness.

How can Splunk Enterprise, Splunk Cloud Platform, and Splunk Infrastructure Monitoring help with optimizing data management?

Convert events into efficient formats for flexible and cost-effective monitoring

Organizations need solutions that simplify the data management experience while retaining control over how their data is shaped, stored, and accessed to reduce overall expenditures and maintain high performance. Ingest Processor is a SaaS data pipeline processing solution that allows ITOps teams to manage data with more flexibility. Practitioners filter, enrich, transform, and route just the data they want from the edge or cloud into the Splunk platform or send it to third party data lakes, such as Amazon S3, to optimize their access at the right performance for each use case they tackle. ITOps can also convert their voluminous logs into lightweight metrics. With log metricization, businesses are able to more easily and quickly surface key trends about their application and infrastructure, while streamlining their data volume, KPIs and costs.

Control what data leaves your environment and ensure data compliance

Intelligent data tiering and summary indexing help you optimize storage, processing costs, and data value – providing choice and efficiency without sacrifice. Tailor your data flow by filtering, redacting, and transforming data to suit specific needs before routing it within your ecosystem or to external storage solutions, such as Amazon S3. Gain granular visibility and control over data to meet compliance needs with customizable retention policies, audit trails, and federation capabilities.

Federate data management with a single unified platform across teams

The Splunk platform enables ITOps teams to tackle data sprawl that is driven by digital transformation initiatives. ITOps teams can collect and index data from virtually any source and location, and do it at scale while managing cost. Data is stored in the Splunk index without sampling, allowing customers to analyze current and historical incidents. The Splunk platform helps ITOps teams optimize cloud infrastructure usage and spend with easy monitoring to pinpoint unusual spikes or trends.

In addition, we’ve revolutionized federated access to data to help ITOps teams get insights from data wherever it lives. Federation ensures that you’re not creating new data silos and that you have easy access to all relevant data for unplanned investigations and longer term needs like audit or compliance. Teams can import reference data or trained AI and machine learning algorithms from relational databases, data warehouses or the data lake for a complete business view.

Grow your observability practice with your metrics under control

Centralized tools teams can ensure that their budget is efficiently utilized with enterprise cost controls with Metrics Pipeline Management in Splunk Infrastructure Monitoring. With Metrics Pipeline Management, platform engineers can scale observability while controlling costs by aggregating metrics data, and filtering out or archiving under-utilized data.