Data Reliability Engineering: Ensuring the Consistent Delivery of Trustworthy Data in Modern Cloud Environments
Why data reliability is critical
Today, hybrid and multi -cloud environments are the norm. Most enterprises have partially or
completely moved their applications and mission-critical workloads to the cloud for cost control,
app reliability, anticipated on-premises resource reduction, and more.
The transition to cloud has raised the importance of site reliability engineering (SRE), a software engineering approach to IT operations where teams use soft ware and automation to solve problems and manage production systems. The main objective of SRE is to keep applications and the complex cloud infrastructures they run on performing well.
Now, a similar treatment needs to be directed to the data organizations use to run their operations. Specifically, as enterprises become more data-driven, new attention is being paid to the emerging field of data reliability engineering. Similar to the goals of SRE, data reliability engineering brings the same philosophy to data workloads. Specifically, data reliability engineering ensures high-quality data is consistently available in complex hybrid and multi -cloud environments.
Factors driving the need for data reliability engineering
With digital transformation, the growing use of IoT, and the general trend of businesses becoming more data-driven, there needs to be a strong focus on data. There is more pressure on enterprises to manage their data in a way that enables them to leverage it to achieve business beneﬁts. That means enterprises need a way to help their data stewards take targeted actions to improve enterprise data reliability.
Why? Data is rapidly growing and being produced at faster and faster rates than ever before. According to IDC1, the Global DataSphere is expected to more than double in size from 2022 to 2026. And the portion of enterprise data will grow more than twice as fast as consumer data over that time.
To turn such data into actionable insights, data consumers need access to the most up-to-date, correct versions of relevant data in real time. Lines of business and data analysts do not have time to wait for someone to look at the data and ensure that it is accurate or to provide access to it.
That is just one issue driving the need for data reliability engineering. There are other things that data stewards must do where data reliability engineering can help, such as:
- Simplify data issues that arise with cloud migrations and the increased use of hybrid and multi -cloud environments: Enterprises are looking to data reliability engineering to help increase enterprise eﬃciency by enabling managers to drive the business forward with self-serve data.
- Make good quality data consistently available in complex application environments: Data reliability engineering can help reduce complexity by providing data observability and self-heal engineering capabilities across data pipelines allowing enterprises to scale innovation.
- Improve operational performance and reduce cost with eﬀective, accurate, and consistent data: Data reliability engineering can help businesses ensure that the right data, the most up-to-date data, and high-quality data are accessible for applications and that those properties apply to the lifecycle of the data.
Why data reliability is needed to complement traditional approaches
Advanced technologies and concepts are being brought to bear to ensure maximum application reliability and security in cloud environments. Why? Apps such as support chatbots, product recommendation engines, inventory management, ﬁnancial planning, and more are critical to an enterprise’s success.
Any problem with the performance of such applications will signiﬁcantly impact a business outcome. For example, poor performance or outages can result in lost revenue, lower business credibility, and poor customer experiences.
The same negative impacts can result if there are issues with the data and data pipelines behind these critical business applications. A ﬁnely tuned and optimized data analysis routine that provides insights is only useful if the required data reaches the systems that perform the analysis without any delays or compromise.
That is leading to a change in thinking. Enterprises must elevate data reliability to the same level of importance as they have treated site reliability, application performance, and security.
Failing to perform data reliability checks at all stages of data pipelines across any form of data (data-at-rest, data-in-motion, and data-for-consumption) and the inability to automate incident troubleshooting and resolution can make downstream service computation and machine learning models inconsistent or inaccurate.
For example, consider a recommendation engine that relies on a constant stream of data from social media channels, sentiment analysis output, user activity on a site, and more, all fed into a machine learning model. Suppose the ML models are retrained weekly, and the data pipelines bringing the data together fail without an enterprise’s knowledge. In that case, the models will be trained on outdated data or lack some essential data. As a result, the recommendations would be oﬀ .
The same thing can happen in many application areas. That can lead to misanalysis resulting in incorrect insights, higher operating costs, increased operational ineﬃciency, and reduced data team productivity.
The inability to ﬁ x data issues as soon as they occur and as data goes through enhancement stages such as integration, transformation, and processing can aﬀect enterprise performance, causing toil and a delay in getting data-driven insights into the hands of decision makers. That can create a potential risk to the business.
Failing to provide a seamless and intuitive way to access and analyze data regardless of its structure or whether it is stored on-premises or in the cloud results in a time-consuming process where data must be requested from IT and other data experts. That leaves data consumers waiting for days or weeks for the needed data. Compounding matters, the lack of data or access delays means enterprises are not optimizing AI/ML algorithms eﬃciently. Why? Data scientists and analysts must spend time on the data aspects of their work, cutting into the needed time for analysis.
Delivering accurate, consistent, and trustworthy data with data reliability engineering
Enterprises in diﬀerent industries struggle with persistent data quality issues across the data landscape. Using unreliable data defeats the intended beneﬁts of its use.
Simply put, enterprises need a way to ensure the reliability of their data. To do that, they must invest in data reliability engineering, bringing data observability and quality together. A genuinely successful eﬀort will additionally make use of best practices.
Key elements of a modern data reliability engineering eﬀort must include:
- A holistic methodology to ensure that the data system is robust, reliable, relevant, and scalable to manage data integrity and withstand any unforeseen challenges or disruptions.
- A data experience that extends DevOps to data through engineering-led automated self-healing data operations. Similar to what is being done in enterprises with respect to DevOps, the goal is to embrace an approach that tries to anticipate problems and issues that can impact data delivery and quality. This is a so-called “shift left ” observability mentality. It can be accomplished with automated data testing to help make data teams proactive by preventing data quality issues from occurring in the ﬁrst place.
- A self-optimized data ecosystem that democratizes data via self-serve data tools. The goal is to allow users to explore data without the need for IT or other interventions. The data consumers can then create their analyses, reports, and visualizations on their timeline and not be dependent on others to provide the data.
Team up with the right partner to optimize your enterprise data outcome
Many enterprises are short or lacking in skilled staff and resources to ensure data reliability. And even if they have skilled staff, many lack the advanced technologies, real-world experiences, and a knowledge base to manage a data reliability effort successfully.
That is leading businesses to partner with global providers that have the technologies, expertise, skills, staffing resources, and more needed for data reliability engineering. Such providers can quickly bring advanced technology to ensure apps run reliably, are resilient, and use modern and emerging data reliability engineering practices far beyond outdated traditional methods.
These are all areas where Hitachi Vantara can help.
Hitachi Vantara provides professional services, tools, and other resources for data reliability engineering to help enterprises jumpstart their data reliability engineering journey.
Hitachi Data Reliability Engineering Services enable enterprises to automate and scale data operations for a self-serve experience by balancing speed, data reliability, and data integrity. The services let data teams gain end-to-end visibility, so they can overcome modern data management challenges and ensure that data is always reliable, secure, and available when needed.
Specifically, Hitachi Data Reliability Engineering Services is a comprehensive suite of tools, technologies, and processes from Hitachi Vantara. The services enable enterprises to build automated and optimized data ecosystems to improve data quality, reduce downtime, enhance data observability, strengthen security and compliance, and increase overall decision-making efficiency. Offered as part of the Hitachi Application Reliability Centers (HARC), enterprises can use these services to produce reliable and trustworthy data analytics and prevent wasted resources, damaged reputation, and regulatory fines.
To that end, Hitachi Vantara is redefining data reliability by bringing technology and expertise around data quality, data observability, and automation with its Data Reliability Engineering Services. The practice delivers a number of key values and benefits in three main areas:
Boosts data resiliency, scales data reliability, and proactively manages cost.
The Hitachi Data Reliability Engineering Services can:
- Increase cost eﬃciency by managing inactive or cold data and minimizing data redundancy with data tiering and data reliability engineering practices.
- Protect data from unauthorized access, use, disclosure, disruption, modiﬁcation, or destruction.
- Swiftly resolve data issues by detecting anomalies or errors in data and processes and resolving them with little or no manual intervention, thereby reducing mean time to recover and overall impact.
- Harness the synergies of data quality, data governance, data lineage, and data reliability to improve data quality and reduce risk.
Increases data trust and transparency.
The Hitachi Data Reliability Engineering Services can:
- Track data drift s by automatically monitoring data and data pipelines to check for data accuracy, completeness, and consistency regularly.
- Establish data lineage to map data movement from the source and databases through processing and transformation pipelines to its end use in AI/ML models or reports so enterprises can propagate data governance policies to protect their data.
- Foster a healthy and balanced DataOps culture with blameless postmortems so enterprises can identify an incident’s root causes and improve processes.
- Minimize data-related errors by ensuring that data or system changes are made in a controlled manner. That is accomplished using release and change management tools and processes to provide a clear audit trail for all changes and reasons for any changes.
Delivers an automated and optimized data ecosystem.
Hitachi Data Reliability Engineering Services can:
- Achieve higher return on investment (ROI) with data assets by improving data usability and accessibility with metadata engineering.
- Deliver business-ready data quickly by adopting DataOps principles to automate data orchestration throughout the enterprise, reduce data errors, and enhance data integrity by optimizing data quality.
- Implement automated monitoring and alerting systems to detect data quality, availability, and performance issues. And embrace data-driven continuous improvement to improve ML models.
- Increase productivity by providing self-service data for consumption and better decision making.
The Hitachi Data Reliability Engineering Services’ difference
Moving to the cloud or adopting a multi-cloud strategy involves managing multiple systems, applications, and data sources. And so, this increases complexity, making it challenging to deliver reliable data to essential applications, enhance reporting efforts, or maintain superior analytics workflows.
Looking at the pain points the data stewards, like the Chief Data Officers, who want to build a data culture, are facing, enterprises realize they need more than the tools they have long used for data management.
Enter data reliability engineering.
Data reliability engineering helps enterprises consistently check if something is wrong with the data while the data is flowing or data is in motion. It lets a business build self-healing capabilities and the automation of data management, integrity, and quality.
Enterprises seeking to leverage data reliability engineering methods can certainly go it alone. But many enterprises lack the skills, resources, or expertise to do so. Hitachi Vantara provides professional services, methodologies, tools, and frameworks for companies to jumpstart their data reliability engineering journey.