We are in a digital economy. By 2022, more than 65% of global products and services will come from digitally transformed organizations that are heavily reliant on data and data-native workers for business success. According to an IDC survey, 80% of CEOs emphasized that using data in advanced decision models for performance and competitive advantage is “extremely important” to their organizations. This is because they see a strong correlation between data-driven insights and business outcomes.

As the value of data and data-native workers grows within enterprises, many are turning to DataOps strategies to improve the outcomes of data analytics, data science, artificial intelligence (AI), and machine learning (ML).

DataOps is a new discipline but is catching on quickly because it reduces data errors and application errors and enhances the speed and quality of data, giving businesses a competitive edge. According to IDC’s research, effective DataOps methods in use among enterprises today include data sandboxes, version control, feedback loops, and logic and data testing. As DataOps borrows the principles of agile and DevOps methodologies, it lends itself to data science, data visualization, and data warehousing use cases by breaking down the silos in data pipelines from ingestion to analysis and visualization. DataOps is a set of best practices enabled by platforms such as data-enabling backup platforms that help break down data silos.

By 2023, 60% of organizations will start implementing DataOps programs to reduce the number of data and analytics errors by 80% and to boost trust in analytics outcomes and efficiency of data-native workers. The DataOps focus on quality will improve the level of trust in data, data analytics, and data science as it helps organizations adapt to new business needs.

DataOps is not one tool but an entire pipeline made up of multiple tools and technologies that are connected or coordinated in DataOps processes. The key to success is to support more integrations and automate testing at various points within the data life cycle.

With SaaS applications such as Salesforce becoming central to business processes, leveraging historical data to identify business patterns/trends, anomalies, and potential is key. But accessing and using this data with traditional approaches will not yield the desired results.

Customers looking to leverage SaaS application data today are following inefficient practices:

• • Using historical reporting functionalities in the SaaS platform that have limited features. The Salesforce platform itself offers the Historical Trend Reporting feature, which is very popular. But it is subject to certain limitations, such as a cap of 5 million rows, limited ability to customize, and complexity in filtering data. In addition, the complexities of data APIs and extract, transform, and load (ETL) mean that users typically analyze data only within the Salesforce platform instead of combining or ingesting historical sales/customer data into modern data warehousing platforms such as Snowflake or Amazon Redshift. They are also not able to combine data from external sources into Salesforce for richer analytics. This limits success in DataOps strategies.
  • Storing all the history in the SaaS vendor’s production environment. This practice is not cost efficient, and it also increases the risks of errors/corruption and application overload in the production environment. Being in control of SaaS data by backing it up in the customer’s cloud environments of choice can allow reuse of SaaS data in a way that mitigates risk. Customers can take this valuable SaaS data closer to their core analytics environments and use it in downstream environments without ETL, heavy customizations, or homegrown connectors. Modern cloud strategies are all about adopting cloud on customers’ terms for business outcomes that matter to customers.
  • Migrating historical data to cold storage archives on premises or in the cloud. This practice is cost effective, but retrieving data from traditional cold storage is difficult and time consuming and incurs high egress charges. The secondary use cases of data such as analytics and data mining as well as for training AI/ML models are growing rapidly, and having easy access to archive data is critical to augment these use cases.

The first principle for analytics and AI success is having the ability to control, access, restore, and integrate all the relevant data into the analytics platform or data warehouse in an automated, integrated fashion. This can help maximize the value, speed, and quality of data programs.

Archiving cold data is a good starting point, but today’s cold data can be tomorrow’s active data for AI and analytics. This means having flexibility of data access is a paramount consideration when archiving SaaS data.

It is not too late for any organization to invest in the future of intelligence. Over the next five years, the ability to synthesize data, the capacity to learn, and the capability to apply insights at scale will define an organization’s success. SaaS data, including historical data, is at the heart of this intelligent future. But companies need to own this data and be able to access it per their needs.

This is where modern backup platforms that go beyond the static role of storing backup data become a powerful enabler of organizations’ AI and analytics journeys.

The value of SaaS data is only growing. As this data becomes pivotal for building an intelligent enterprise, leveraging a modern backup platform as a data source can make all the difference. It will help reskill backup and CRM professionals to DataOps roles, and automation will help the teams focus on strategic tasks such as data quality, metadata management, and data as a service.

More importantly, a modern backup platform delivers value to all the stakeholders of DataOps — IT operations (backup and governance professionals), business owners (CRM professionals), data engineers, data architects, and data scientists. It empowers them with data ownership, availability, and flexibility.

Data engineers and architects are at the heart of any business’ data-driven strategy because they are responsible for data quality, integration, data flow, ETL, and data warehousing. They develop the data pipelines and are the bridge between IT operations, software development, and data scientists who are responsible for analytics, data mining, and AI/ML strategies. Data engineers face multiple challenges such as inconsistent data APIs, fragmented data sources, and multiple data types. Addressing these issues is increasing the technical debt and making data engineering a bottleneck in data-driven strategies.

Having access to historical data from SaaS applications within organizations’ own cloud environments eliminates ETL complexities. The data teams within organizations can ingest the data into downstream environments in a consistent manner and accelerate the speed of data programs. Using historical data from modern backup to ingest into data warehouses in an automated way helps data engineers focus on more strategic tasks such as ensuring data quality, monitoring access controls, and quickly meeting data science needs. The core functionality of backup shouldn’t be compromised. It should still improve data resilience through swift restores and recovery in case of cyberattacks or accidental deletions. The archiving capability should help organizations adhere to compliance regulations via defined policies for data retention and a mechanism to deliver on their shared responsibility mandates. But viewing backup from the new lens of DataOps can bring much more value. The platform can help maintain history and data lineage. It can provide instant access to any data, blurring the lines between hot and cold data, and improve the trust in data and AI strategies.

It is time to take control of SaaS application data with modern backup and data-enabling platforms.

Consider the following example: A multinational pet healthcare company, with strong data-driven ambitions, was storing connected customer device data in AWS. It wanted to capitalize on features such as Amazon CloudWatch and ElasticSearch for intelligence, but it couldn’t integrate business-critical data in Salesforce and ERP environments with the connected device data. As a result, it couldn’t yield quality insights.

The company used a modern backup and analytics-enabling platform to capture and archive historical sales and service data and subsequently reused its historical Salesforce data alongside cloud data to achieve 360-degree customer visibility and inform key business decisions. The winning strategy was using a backup platform that acted as a data hub for data engineers to use standard Parquet formats to ingest data into existing analytics pipelines without custom engineering or dealing with complex data APIs. The company’s ecommerce revenue grew exponentially from 0% to 70% of total revenue in less than 18 months. The backup platform not only protected critical SaaS data but also facilitated the reuse of that data with minimal engineering and coding needed to deliver data-driven business outcomes.