All over the world, companies of every size and structure are leveraging the power of bespoke data All over the world, companies of every size and structure are leveraging the power of bespoke data pipeline architecture. Whether you operate a grocery store loyalty program to predict supply and demand or a social media platform with reactive ad space, knowing how to smoothly integrate data management into your systems ensures you receive the insights and predictive modeling that can grow and improve your service. This is incredibly important for companies managing high volumes of data through lakes or storage facilities.
Data-driven companies are 58% more likely to beat revenue projections than those that have not experienced a digital transformation into the modern era. That is why now is the best opportunity for your enterprise to optimize or modernize its data pipeline process. To get the most out of this exciting technology, be sure your team is implementing DataOps best practices so all research, capabilities, improvements, and growth opportunities are met and aligned with your internal goals.
What are DataOps and Data Pipelines
With so many new technologies hitting the market at a breakneck pace, it only makes sense that your team may need a quick reminder of these essential areas of a modern company. DataOps is a term for the data operations of a company. This is similar to how DevOps (development operations) can combine the skills of engineers and data scientists into a single discipline.
At its core, DataOps is a method of analyzing and delivering analytics in an agile-style process. This allows a company to streamline the design, development, and maintenance of data pipelines by standardizing and governing how disparate data elements are transformed into intelligence. So, when you think of “data-driven,” you are thinking about the outcomes of an efficient DataOps, just on a much smaller level.
Data pipelines are a bit different. These are essentially hardware or digital centipedes where one input is the next connected service’s output. It is a set of processes that take in raw data from different sources and deliver them to specific storage spaces. They can then be picked apart by AI, ML, or data analytic tools to discover new insights or value points. The need to advance and improve current data pipelines is because of evolution in data speed, agility, volume, and market demand.
When you start developing and streamlining your DataOps methodology, there are a few key tricks and tips that can help you when scaling.
1 – Build Piece by Piece
DataOps best practices are based on the agile methodology of development. This philosophy breaks down a project into smaller, easier to handle, and digestible pieces, so you have the flexibility and reactive skills to respond to any changes quickly. The same principle applies to DataOps and data pipeline architecture. Even though you may want to dump all your enterprise data into a lake, focus on the specific business programs where you need insight and apply automation in manageable segments. This way you can prioritize what you need versus trying to analyze too much data at once.
2 – Collaborate Where Possible
DataOps need to be incredibly effective because you are most likely handling a growing source of data. That is why you want to incorporate the insights, experience, and expertise of various deployments strategies and the team members supporting those stages. For example, business owners are an incredibly critical part of data transformation because they have the functional domain expertise to interpret the insights gained.
The point is to have data pipeline tools that improve workflow and send your information to either the human assets or analytical tools as quickly as possible. Gather up a response and implementation team comprising different members of your company from marketing to support staff, so everyone is involved in the final process and helps create cross-functional solutions.
3 – Organize Everything
Log and store all the information, fixes, and use cases of each new implementation. You want a clear outline of status, timing, workflow, and more. This way, your team can make repairs or adjustments based on what has and has not worked in the past. You would not want a DataOps solution to be redundant or not offer the specific answers you wish because they do not integrate with what you have tried in the past. The idea is based on data harmonization and modeling that advances your team towards an end goal. Understanding the business rules to be applied to the raw data is critical to ensure the data is organized for paramount effectiveness.
4 – Automate, Automate, Automate
Since most data pipelines process critical data transfers, it is important to have a data pipeline management infrastructure to manage the lifecycle. Data Pipeline management involves Observability, Automated Alert Notifications, Problem Resolution and Change Management, core Data Operations functions that require focus and dedicated resources to avoid data downtime.
Think of this in similar ways to IT infrastructure management. You want systems in places that can observe any incidents and a change management directive with proactive actions that prevent data downtime. The reliability of your data pipeline operations results in improved quality and trust of the data being consumed for developing insights.
5 – Standardize and Catalog
DataOps management involves standardizing the information as it flows from one stage in the data pipeline architecture to the next. While some smaller apps or storage spaces may need only a few segments or touchpoints, others can have hundreds of pieces and endless branches of mini-pipelines. DataOps aims to simplify and create opportunities for increasing the interoperability of all information flowing through your pipelines. By standardizing this data, it can flow easily from stage to stage.
6 – Change the Culture
Some companies have a bit of resistance to the entire agile methodology. Many companies still operate Some companies have a bit of resistance to the entire agile methodology. Many companies still operate using waterfall methods or reactive-only philosophies. While these have some advantages, DataOps is based on flexible design, automation, orchestration, and scalability. It is an agile concept that must be adopted by all your team members just as much as the technology. Otherwise, your team will not take full advantage of all the potential benefits of speed and data-driven insights.
7 – Scale Security Measures
When you implement new data pipeline tools, you increase the volume of data being handled by your company. Whether or not this moves into a data warehouse or lake, it means you have a much higher responsibility for vetting the privacy and security capabilities of your DataOps methodology. Create a detailed plan for safely storing, analyzing, and managing this new process that ensures your team, clients, and stakeholders are all aware of the due diligence you are taking. Any business has the potential for a data breach and must undertake these measures.
8 – Allow Innovation
If you want your data management to scale and adapt to any situation, you need to allow your team to innovate with the demands and construction of each data pipeline architecture. This innovation is the key reason many companies are moving to DataOps management. As long as you have clear objectives and well-communicated standards, your team should be able to adjust the tools available to fit your needs.
DataOps methodologies are being integrated on a gradual, global scale. The more companies that experience positive returns, greater process efficiencies, and more robust agile responses to consumer demands, the more we will continue to see data pipeline tools and DataOps solutions being introduced and sold on the market. Companies that view data operations as a service driven comprehensive data pipeline management solution are more likely to achieve their business objectives faster.
Allow your team and culture to adjust. You should see greater capabilities with data management, high volume processing, and scalability as you adapt and innovate in the DataOps field. This will not only allow you to stay competitive in the marketplace, but should offer a competitive edge for future growth that your customers and stakeholders will appreciate.