DataOps, a practice aimed at improving the quality and reducing the cycle time of data analytics, is becoming increasingly crucial as businesses strive to leverage data for competitive advantage. According to BMC’s "Putting Ops Into DataOps" report, businesses have adopted several best practices to streamline their DataOps processes.

In this article, we delve into seven key practices highlighted in the report, providing insights into how enterprises can optimize their data operations for better outcomes.

7 DataOps Best Practices According to BMCs Putting Ops Into DataOps Report

Here are seven data ops best practices that businesses have adopted according to BMC’s putting Ops into DataOps report.

1. Data Ops Are More Likely To Adopt a Do It Yourself Methodology

One of the standout findings from BMC’s report is that DataOps teams often adopt a do-it-yourself (DIY) methodology. This approach allows teams to tailor solutions that precisely fit their unique data requirements and operational contexts. By leveraging open-source tools, in-house developed software, and customizable platforms, organizations can create bespoke data workflows and pipelines that are more flexible and scalable just like when you buy VPS hosting.

The do it yourself approach empowers teams to rapidly iterate and innovate, fostering a culture of continuous improvement. It also encourages a deep understanding of the underlying data processes, which is critical for troubleshooting and optimization. However, this methodology requires a skilled team with a strong grasp of both data engineering and domain-specific knowledge to implement and maintain these custom solutions effectively.

2. Large Scale Enterprises Have Efficient Data Management Processes To Support Technology Adoption

Large-scale enterprises typically have more mature data management processes in place, which are essential for supporting the adoption of new technologies. Efficient data management ensures that data is accurate, consistent and readily available for analysis. This involves robust data governance practices such as data cataloging, lineage tracking and compliance with data privacy regulations.

With efficient data management, enterprises can seamlessly integrate advanced analytics tools, machine learning models and artificial intelligence applications into their existing workflows. This integration enables them to extract more value from their data and make more informed decisions. Additionally, having well-defined processes reduces the risk of data silos and ensures that insights derived from data are reliable and actionable.

3. Distributed Data Ops Responsibilities

The report highlights the importance of distributing DataOps responsibilities across the organization. This practice involves decentralizing data operations tasks such as data ingestion, transformation and analysis to various teams rather than centralizing them within a single data team. Distributed responsibilities ensure that data operations are more responsive and aligned with the specific needs of different business units.

This approach promotes collaboration between data engineers, data scientists and business analysts, leading to more holistic and contextually relevant data solutions. It also helps in scaling DataOps practices across the organization by leveraging the expertise of different teams and reducing bottlenecks that can occur when responsibilities are centralized. Effective communication and coordination mechanisms are essential to ensure that distributed teams can work together seamlessly.

4. Poor Data Management Prevents Organizations From Using Data To Extract Useful Insights

Poor data management is a significant barrier to extracting valuable insights from data. Issues such as data quality problems, inconsistent data formats and lack of data integration can hinder the ability of organizations to perform meaningful analysis. BMC’s report emphasizes the need for organizations to invest in robust data management practices to overcome these challenges.

Improving data quality involves implementing data cleansing processes, standardizing data formats, and ensuring data is regularly updated and validated. Effective data integration requires the use of ETL (extract, transform, load) tools and practices that consolidate data from disparate sources into a unified view. By addressing these data management issues, organizations can unlock the full potential of their data and derive actionable insights that drive business growth.

5. Delivering On Data Quality and Quantity Is Quite Challenging

Delivering on both data quality and quantity is a complex challenge that many organizations face. High-quality data is essential for accurate analysis, while sufficient data quantity is necessary for comprehensive insights and robust machine learning models. Balancing these two aspects requires a strategic approach to data collection, storage and processing.

Organizations need to establish clear data quality metrics and continuously monitor their data against these standards. This involves setting up automated data quality checks and validation processes to identify and address issues promptly. Additionally, scalable data infrastructure such as Dedicated Gaming Servers is required to handle large volumes of data efficiently, ensuring that data quantity does not come at the expense of quality. By prioritizing both quality and quantity, organizations can enhance the reliability and depth of their data-driven insights.

6. Lack of Automation Hinders Organization Capability to Deliver User Data

Automation is a critical component of modern DataOps practices, enabling organizations to streamline data workflows, reduce manual intervention and accelerate data delivery. BMC’s report highlights that a lack of automation can significantly hinder an organization’s ability to deliver user data efficiently and accurately.

Automating repetitive tasks such as data ingestion, transformation and validation allows data teams to focus on more strategic activities such as data analysis and model development. Automation also ensures consistency and reduces the risk of human error, leading to more reliable data operations. Investing in automation tools and technologies is essential for organizations looking to enhance their DataOps capabilities and deliver high-quality data to users promptly.

7. Prescriptive and Predictive Analytics Lead The Enterprise Data Consumption

The adoption of prescriptive and predictive analytics is leading enterprise data consumption, according to BMC’s report. Predictive analytics involves using historical data and machine learning algorithms to forecast future trends and outcomes. Prescriptive analytics goes a step further by recommending actions based on these predictions to achieve desired business goals.

These advanced analytics techniques enable organizations to move from reactive decision-making to proactive and strategic planning. By leveraging predictive and prescriptive analytics, enterprises can optimize operations, improve customer experiences and drive innovation.

The successful implementation of these analytics methods requires a strong foundation in data management and DataOps practices, ensuring that the underlying data is accurate, timely, and relevant.

Conclusion

BMC’s "Putting Ops Into DataOps" report provides valuable insights into the best practices that organizations can adopt to enhance their DataOps capabilities. From adopting DIY methodologies and efficient data management processes to distributing responsibilities and leveraging advanced analytics, these practices are essential for organizations looking to maximize the value of their data. By addressing challenges related to data quality, automation, and management, businesses can unlock the full potential of their data and drive strategic growth.