The adage “information is power” is only true for businesses with the means to retrieve it from their growing mounds of raw data. This is becoming an increasingly challenging task, given how data volumes are projected to exponentially grow over the next few years.
Businesses must improve their data processing and analytics capabilities if they are to capitalise on their data and remain competitive. Most businesses are going down the DataOps path, which brings improved agility and veracity to data analytics.
DataOps maximises the value of data insights for the business, accelerates the speed of development, and improves the potential of data-driven tech. It bridges data teams and ITOps, and it’s the next step towards building a mature data analytics program.
What measures can you apply for a robust DataOps?
Whether you have an existing DataOps practice or are looking to start one, the measures below will improve observability, quality, and effectiveness of your future DataOps efforts.
Begin With Good Data Governance
You can’t sensibly start a data-centric program like DataOps without a strong foundation in data governance—someone must be responsible for evaluating integrity, usability, and security of raw data before it can be used in data analytics. Failing to establish governance allows inconsistent data to slip through the cracks and creates the risk of security oversight.
Inconsistent data slows down data analytics projects, impacts the accuracy of insights, and exposes businesses to all manner of noncompliance. When establishing strong data governance, involve members of the executive team, not just IT and data scientists.
Designing the governance committee this way enforces accountable data stewardship and aligns data governance objectives with business goals, which is the main point of DataOps.
This also tends to reduce common friction between the data team (with their focus on the technicalities of data) and leadership, who’s more concerned on how data is being leveraged to identify risks, reveal opportunities, and provide a leg up for the business.
Follow Up With Strong Data Mapping
Chances are much of your raw data exists in the cloud, on physical servers, or anywhere in between. There’s also what’s called external third-party data, which are bits and bytes from marketing channels, business partnerships, and even purchased data sets.
The challenge for any DataOps practice is to integrate and map these diverse data points to relevant fields within data analytics programs—a Sisyphean task if data teams aren’t exactly sure what data fields to map for! Fortunately, having a foundation in data governance helps.
The combined expertise in most data governance teams allows them to guide DataOps teams when it comes to mapping data to desired business outcomes, allowing them to generate accurate and impactful business insights from analytics.
With the right mapping tools, DataOps teams can extend visibility over the entire data engineering process, build data dictionaries, and gain greater control over how data is used, collected, or processed throughout the business to benefit future projects and endeavours.
With the right parameters established, DataOps practitioners can even automate the entire end-to-end process—from data sourcing and processing to analytics and delivery—creating a continuous feedback loop resulting in more accurate, secure, and useful data insights. To enable this, however, some level of observability into DataOps is necessary.
Extend Observability and Monitoring With the Right Tools
Combining the tenets of DataOps—which prioritises data development, integration, and automation—with powerful monitoring and mapping tools gives DataOps a higher level of observability over the entire stage of the data engineering and analytics process.
In fact, every tool or solution DataOps teams use should inherently provide some measure of observability, from data collection and reporting of correlated metrics to error reporting. Data practitioners should be able to easily track and analyse the entire data pipeline.
This empowers them to identify anomalies or inefficiencies and take decisive action to fix them, further improving the efficiency of data analytics programs.
It also allows DataOps to go the way of DevOps by implementing a continuous feedback loop bringing about improvements with every cycle. Powerful observability tools provide data practitioners and engineers with the additional context and information they need to not just fix problems but understand where they can do better the next time around.
The right best practices, combined with the right tools, are essential for those looking to establish or improve their DataOps practices. And as businesses’ data mounds, DataOps will bring compounding benefits in the shape of more timely and accurate business intelligence, allowing businesses to remain competitive and adaptable to this ever-changing world.