Data is the lifeblood of every business. It is a valuable asset that can provide important insight into how to drive better business outcomes, uncover revenue opportunities and even improve customer relationships.
The healthcare industry is a key example of that. Anne Arundel Medical Center, a medical centre based in the United States, relies heavily on data to identify risks and make the correct decisions to meet clinical goals in a financially responsible manner.
Rethinking your data capabilities
But building a data-driven culture, such as that of Anne Arundel Medical Center, can be challenging.
Organisations typically have various teams or business units and within each of them, there are smaller groups that focus on specific data-driven activities. The challenge with this type of structure is the lack of interaction between teams – especially those working on data analytics. As a result, there is an overlap in work and a creation of data silos. This inefficiency can also result in different groups analysing the same data and ending up with different results.
Unsurprisingly, such traditional approaches are increasingly being left behind in favour of more agile and real-time integrations of teams and data. Modern data integration approaches like DataOps help eliminate information silos and increase data accessibility to employees, providing a consistent view to business users. This way, businesses can build an agile and future-ready enterprise.
DataOps is something you do, not something you buy
DataOps enables a collaborative process for delivering data in a timely, efficient manner. In addition to streamlining processes and training people, DataOps leverages key technologies such as real-time data integration and automation to accelerate data delivery for analytics.
A core component of a DataOps framework is the creation of an enterprise data catalogue – an internal marketplace that lists what data is available for analytics. Another important aspect is that organisations should be working towards automating as much of it as possible of the data integration process, so that they can free up more time and resources for analytics and deriving insights.
Shire, a global research pharmaceutical company, successfully integrated data from various information pipelines into a single connected data source, allowing the business to aggregate big data sets into useable and manageable information. This empowers teams at all levels to make trusted decisions across the business, ranging from ensuring efficient manufacturing processes to improving a healthcare patient’s journey.
Data management platforms such as cloud data lakes and warehouses require continuous data integration to meet the needs of real-time analytics. Change data capture (CDC) technology provides a non-invasive method to catch data and metadata changes from core application systems and databases and stream them in real-time to the data management and analytics platforms. The beauty of CDC is that it works in conjunction with data lakes and warehouse automaton to provide near real-time, analytics-ready data wherever it is required.
Five key requirements to ensure DataOps success
When building modern data architecture and implementing a DataOps approach, organisations should consider five key requirements:
1. Continuous integration – Continuous integration is foundational to these modern data platforms and to meet the needs of real-time analytics. It requires individuals to think differently about integration, which can no longer be a batch process that only happens at certain points in time and impacts transactional systems.
2. Universal applicability – Seek solutions that support a broad variety of data sources and targets and span multiple integration use cases. For example, you should only capture changed data once per transaction system and route that data where and when it’s needed: to a data lake; or a data warehouse; or replicate it to another database. This provides greater efficiency and fewer moving parts.
3. Automation – It’s always a challenge to find the right people and skill sets to meet the everchanging technology landscape. Thus, automation becomes essential to the success of a DataOps initiative, from the generation of change data streams to delivery, refinement and finally the creation of analytics-ready data sets. This becomes particularly important for data lakes, where heterogeneous data is coming in a variety of formats and is continuously updated with new change files.
4. Agility – Being able to quickly embrace new customer requirements is vital due to today’s rapidly changing marketplace. An enterprise must be able to quickly pivot, or it can be left behind the competition. Flexible integration solutions allow for IT to simply change a data source or target without disrupting the entire infrastructure – providing an agile, modern infrastructure that future proofs an enterprise.
5. Trust – Organisations must provide data that is not only trusted, but also deliver that data in a timely fashion. This ensures that business users are privy to where data came from, and how it was transformed and manipulated. The architecture requires a data catalogue to easily find the information; data lineage to showcase where the information came from and how it has been transformed, and validation to ensure that any data movement was successful.
Once the DataOps methodology has been ingrained into an organisation’s culture, companies will be empowered to support real-time data analytics and collaborative data management approaches, while easing the many frustrations associated with access to analytics-ready data. Collaborative DataOps strategies using modern data cataloguing and data integration technologies will lead to increased data trust and quality, enabling employees to be confident in their business making decisions, thereby placing businesses in a strong competitive position.