Data operations help coordinate the efforts of data developers, analysts, and scientists to get the most out of analytics. Also known as DataOps, this essential business practice is primarily about finding the best ways to manage and organize data.
DataOps work to identify exact solutions and tools that use collected information to solve problems. While companies can access data from multiple sources and have legitimate reasons for collecting that information, it can become disjointed. DataOps seeks to address that problem by fostering teamwork and additional concentration on operational practices, AI, and advanced analytics.
Data Obs Makes DataOps More Effective
Introducing intelligent, advanced analytics methodology such as data observability helps organizations evaluate and reconcile data quality once it has been collected. Data obs offers a holistic view into data operations, data management, and data pipelines. It goes beyond alerting teams to problems after the fact, and can prevent outages, identify data quality across the company, and give insight into data pipelines.
Even though DataOps is relatively new, its potential to drive growth lies behind its increased popularity. However, the “newness” of DataOps can create uncertainty about how to go about implementing it in a specific setting. Many can be daunted by a need to initiate or refine data practices to support the success of DataOps.
Considering a DataOps team? Here are four steps to getting started:
1. Build Your Team
Before you can put any DataOps initiative in place, you need to decide who’s going to lead it. Depending on your company’s hierarchy or structure, you may choose to assemble a team from different functional areas. Typically, most of the contributors will come from data analytics and software engineering roles.
Cross-functional teams have the advantage of eliminating silos and improving collaboration. By bringing employees with varied expertise together, your DataOps efforts are likely to be more holistic. The core function of DataOps is to achieve business objectives. Employees who are familiar with some (or all) of these can give your team a leg up.
However, it can still be helpful to define those objectives for the team. Let them know which goals take precedence over others. Employees already familiar with what their departments are trying to accomplish can provide additional insights. Everyone can learn where existing deficiencies in data flow and processes exist. Plus, the team may learn how functional objectives could better fit into organizational goals.
Maybe sales and marketing are looking at some of the same data. Both departments know there’s a conversion problem. Sales employees have a subset of information that shows where and why leads aren’t purchasing. Marketing doesn’t see this activity and, as a result, isn’t sure how to tweak messaging to lead to more conversions. This is one example of a data silo that a cross-functional team can work to resolve.
2. Start Slowly
Building a successful DataOps initiative won’t happen overnight. Once business objectives are in place and prioritized, it’s time to break them down. For each goal, look at the data your company is collecting. Is the collected information what you need to achieve each of your goals?
One common objective is to increase conversions. Is data coming in from various sources casting light on the behavior of sales leads? Information from surveys, sales, conversations, follow-ups, online behavioral tracking, and insights should be synced. Data from converted leads can help supplement this effort. If that’s not happening within a workflow or process, this represents a gap the team should resolve.
Involving employees from outside the DataOps team to solicit feedback about data flow is also part of the process. They can provide information and insights that those on the team may overlook or be unaware of. The employees who will use any new processes and tools can also provide feedback once they’re developed. Frequent points of contact can help the DataOps team determine whether gaps remain and if what’s being put in place is useful.
3. Classify Your Data
Classifying the data your organization works with will involve more than just defining it. Labeling subsets with categories is a start, but consider adding to this the role the data plays. Think about the data’s function, including the various systems through which it flows.
Data from different sources can end up in different places. For example, it’s common to use both a data warehouse and a data lake. It’s important to know what data is located where so that data scientists and data consumers know where the most relevant information is.
All employees can understand how data gets used across the organization by utilizing a data catalog that tags and profiles data. Multiple departments can understand how the way a contact record is put into one system impacts everyone. They can also begin to manipulate and use that information in a manner that serves the entire organization.
4. Leverage Cross-Functional Feedback
To eliminate the siloed use of data, DataOps teams can design apps and processes that include continuous feedback. It’s common for one department to identify a need for data and then figure out a way to get it independently. However, supportive apps and processes that encourage the sharing of that data may not be standard practice.
Finding ways to let various departments access and merge new and existing sources of information is a solid business practice. Your company can also develop a process for DataOps to gather feedback that predicts the future uses of data. A shift in perspective can improve how the company obtains and analyzes its information.
Collaboration and communication between functional teams, including DataOps, don’t end once new processes and apps roll out. DataOps needs to know what is working and what isn’t. Other employees need to know about potential solutions and how their insights can contribute.
Building in easy and timely ways for feedback to flow between teams is critical. In some instances, this may involve AI that compiles further information on how data is being used across the organization. It may go beyond the use of intelligent tracking and include periodic surveys and regular face-to-face meetings, too.
When setting up your DataOps team, the main objective is to stay on top of how information flows into and through your company. Be willing to adjust to the needs and goals of various departments, not just a single team.
Determine how those needs and goals fit within the bigger picture and make shared data available on demand to everyone who needs it. Business success depends on encouraging continuous collaboration that finds new ways to improve access to information.