By Gary Alleman, Managing Director at Master Data Management
The desire for insights to inform decision making remains a primary focus for executives.
Over time, the analytics budget and focus may have shifted – from business intelligence to big data, to data science and, most recently, to artificial intelligence and machine learning. Why? Because the business’ need for information is not met timeously.
As one executive said to me, “I am running my business blind. I only get my forecasts in the third week of the month.”
It is easy to understand why each new analytics capability or technology appears attractive, it is positioned as the ‘silver bullet’ that will miraculously deliver insights when needed.
Yet, without investments in the underlying data management foundation, each new technology and approach runs into the same hurdles as before.
A recent Coronium Intelligence survey of over 300 C-level executives across EMEA, the Americas, and Asia-Pacific, showed that:
- Almost 9 in 10 CDOs say they are challenged by a lack of staff with the right skills.
- Data teams spend too much time on average (40 percent) cleaning and prepping data for analysis, with some reporting as high as 80 percent.
- Nearly two-thirds of respondents (65 percent) said staff will only trust insights from data if it confirms their gut feel.
- Data quality concerns are hampering data integration programs for 82 percent of CDOs.
- 4 in 5 enterprises struggle to enrich their existing data at scale to uncover new insights and hidden patterns.
What is DataOps?
DataOps is a collaborative practise that brings together diverse teams to streamline and automate the delivery of data across an enterprise.
It leverages data management technology and practises to ensure:
- Data Integration: simplifying the process of connecting to and consolidating disparate data sources.
- Data Integrity: testing and improving data to ensure that business decisions are supported by accurate, complete data.
- Metadata Management: maintaining and communicating a clear picture of the data estate, origin, dependencies, and changes over time.
This sound foundation engages both business and IT stakeholders in an agile way to deliver high quality, well-understood data to support analytics on demand.
DataOps unlocks data for employees across the company – from executives and middle managers to business analysts and operational staff. Each knowledge worker is empowered by access to trusted data and insights, enhancing productivity and competitiveness at every level.
What is the difference between DevOps and DataOps?
DevOps is a methodology to optimise software development by enhancing collaboration between the developers and operational IT staff. This brings the development, testing and deployment of new software together into a single automated process, reducing software delivery timelines through enhanced communication.
DataOps, by contrast, brings together business and IT stakeholders to optimise the delivery of data pipelines. Rather than focusing on technical outcomes, the focus is on data, and its ability to support the delivery of business goals and objectives. This means that collaboration plays a much bigger role in DataOps than it does in DevOps as the priorities and expectation of business stakeholders must be clearly defined and prioritised.
Data integrity is another key focus. The DataOps team must continuously monitor the data pipeline to ensure that previously trusted data has not become tainted.
The technology at play also differs. Where DevOps is focussed on agile software development – using tools such as project management, source control, automated testing, and the like, DataOps is focussed on enabling data management capabilities including data catalogues, data lineage and data quality, along with data preparation or transformation capabilities.
Faster speed to insight
The agile framework of DataOps focuses on continuous delivery, supporting targeted pipeline deployments and changes to deliver against changing business priorities. Collaboration tools build feedback from data consumers into the pipeline development process, allowing tweaks to be made early in the process and delivering customised insights.
The ability to understand the impact of changes and eliminate manual, cumbersome processes increases the productivity of the data engineers and gives business stakeholders and decision-makers quicker access to the data they need to do their jobs.
This is the end-goal – delivering faster time to insight for key stakeholders through a sound, automated data management foundation, enhanced collaboration, and agile delivery.