Redefining Analytics Anywhere: Platform & Data Recovery in 10 Minutes

Modern organizations leverage data analytics for all decision-making processes, both internal and external. A critical aspect of the organization's analytics landscape is data and data lakes. To truly achieve analytics everywhere, enterprises need the capability to move their data between platforms seamlessly and in as little time as possible.

At Datamotive, we're enabling analytics anywhere with our Hybrid, Mutli-Cloud Workload Portability platform. Continue reading to learn how a 10-minute recovery time of your entire workload in your choice of cloud -can make analytics anywhere a reality!

What is Analytics Anywhere?

To understand analytics anywhere, we should first explore the topic of data analytics further.

Enterprises use data analytics to generate actionable business insights. Let's check out three examples:

• Sales Analytics – Metrics could include average revenue per customer (ARPU), lifetime value (LTV), net promoter scores (NPS), and cost of customer acquisition (CAC).
• Workforce Analytics – Metrics could include absenteeism rates, employee turnover, profit per employee, and employee wellbeing or satisfaction.
• Infrastructure Analytics – Metrics could include resource utilization, digital service uptime or availability, networking latency, and mean-time-to-remediation (MTTR) for incidents handled by the IT department.

Traditionally, analytics in these areas is reserved for specialist employees or stakeholders. This results in a top-heavy environment where employees are left in the dark, and unable to derive their own insights.

Let's weave in Analytics Anywhere now.

With Analytics Anywhere, any employee can leverage existing business intelligence (BI) tools, in any office department or remote location, to tap into any data source. Direct connections to the data source enable maximum analytics performance. Analytics anywhere eliminates barriers to accessing and generating intelligence, so anyone in the business can generate insights to guide their work approach—no matter their location or device.

At a technical level, analytics anywhere is all about being, platform-agnostic, and application-agnostic. There is no dependency on one platform or application—just the data itself. Since existing security, compliance, and data governance rules are applied natively at-source, employees gain new analytics capabilities without the risk of data over-provisioning.

Overview of the Analytics Landscape and Components

Components of the analytics landscape include the storage medium or location, the overarching platform that hosts the data, and applications that connect to the data platform to conduct analytics workflows.

This is where most "analytics anywhere" solutions get it wrong.

Analytics anywhere solutions focus too heavily on the data being stored, and its availability for analysis in business intelligence (BI) tools. And what about the data platform and analytics applications? They aren't prioritized. Instead, data pipelines are used to fulfill movement from the source to the destination. This means the three components are not fully integrated.

While data pipelines are a viable option, enterprises do encounter challenges:

• Right Time, Right Place – Enterprises must determine the "right time" to acquire the necessary data, as fetching data has a direct impact on production.
• Disconnected Components – A dependency on pipelines, rather than native data movement features, means new platforms and applications require re-configuration and re-deployment of data pipelines. This takes time and effort which increases the time for data to become available.
• Duplication – Datasets are often replicated multiple times across internal business groups. An assortment of datasets in different business departments increases the management burden across the entire analytics landscape. Duplicated files also drive up storage and data transfer costs.

Analytics Anywhere Requires Full Data, Platform, and Application Integration

The critical importance of data is growing rather than shrinking. Enterprises depend on data to generate actionable insights that guide business decisions and new ventures. To accommodate modern data needs, today's approach must change so that data, the platform, and applications work in harmony.

To get there, all three components—data storage, the host data platform, and business application—need to run in real time. There is no true definition for "real time" at present, and enterprises need this to be defined in exact time in order to progress further.

At Datamotive, we define "real time" as within 10 minutes. This means all three components—data, platform, and application—can be recovered to any destination in the analytics landscape within a 10-minute time frame. A full list of benefits with Datamotive includes:

• A definition for the undefined – real-time recovery means "within 10-minutes."
• No component left behind – data, platforms, and applications.
• One data copy, and only one copy – various business groups access the same copy of data via the application pipeline, rather than creating their own duplicate copies.
• Reduced effort and infrastructure expense – eliminate the need for infrastructure DevOps during the recovery process.
• Secure, Non-Intrusive, Incremental Replication – Production environments remain secure with ZERO impact on operations. With Datamotive replication is incremental and at a block-level, which provides an exact copy of your entire workload, including your security profiles, and compliance controls, to your choice of cloud.

With Datamotive, organizations can replicate and recover their data, platforms, and applications as one cohesive unit. Standing up your workloads, in your choice of cloud is always within 10 minutes—guaranteed—so enterprises can make data-driven decisions in actual real time.

To get started, request a demo of Datamotive today.

Author:

Author: Yogesh Anyapanawar, Founder and CEO of Datamotive