With most businesses determined to leverage data in smarter and more profitable ways, it’s no wonder dataops is gaining momentum. The growing use of machine learning to manage tasks, from creating predictive models and deepening insights into consumer behavior to detecting and managing cyberthreats, also adds to the dataops incentive. Businesses that can move to rapid autonomous or semi-autonomous examinations of sophisticated data sets will gain a strong marketplace advantage.
As businesses consider the challenges of a more mature and robust analytics practice, some are turning to dataops-as-a-service—outsourcing the work of harnessing company data. While this approach can address some talent issues and speed up your data analytics journey, there are also risks: Without having a clear understanding of the business drivers behind data analytics, outsourcing your data needs may not deliver the data intelligence you need. And adding third and even fourth parties to the data ingestion and analysis process can increase data protection risks.
Your other option: build an internal dataops team.
This approach also has its challenges, and requires more than finding the right team members or mimicking a good devops initiative. But the payoff is worth the effort.
A dataops initiative done well will not only make a business more intelligent and competitive, it can also enhance data accuracy and reduce product defects by combining data and development input in one place.
Following are 7 key guidelines for building a successful internal dataops initiative:
Leverage devops for culture advantage
Businesses with an established devops culture and practice have an advantage when it comes to implementing dataops. They have done the hard work of bringing siloed, development and operations teams together to focus on bigger business goals. Adding data pros to this established team to create a dataops initiative will be far easier than it is for organizations without a devops background.
While the lack of a current devops program is not a reason to say no to in-house dataops, the organization needs to factor in the challenge of bringing a bigger group (data, development, and operations) together and building the operational framework from scratch.
Keep security top of mind
Expanded data access and engagement does increase security risks. Examining how data integrity is protected is an essential part of building a dataops practice and team. What processes will be used to ensure compliance is met across any tools or applications created by the dataops team? How will the organization hold the team to the best data security standards? Who will decide what customer data can or should be used?
Data breaches and loss have been devastating to businesses, and the dataops team must embody the best in data integrity.
Make business priorities the unifying force
It might sound obvious that any group within a business should make the business’ priorities its top focus. Data, however, can lead the most strategic analysts and engineers down all kinds of interesting development, operations, and data paths.
To keep dataops teams on track, continuous reminders of strategic business goals are essential. The question, “Does this data product or solution we’re considering meet an established business priority?” should be asked regularly to keep the team in sync and on track.
Take a people-first approach to managing the transition
Moving to dataops will change roles and responsibilities, which can be destabilizing for team members who are concerned that broadening project teams and responsibilities could hurt their job security. “If I don’t own my projects, how can I get credit for my work and contributions?”
Key to the successful transition to shared data tool and solution development is re-establishing performance goals and measures. Team members need to see this as an advancement in business operations and that their responsibilities will also evolve and advance accordingly.
Know that some roles will be hard to fill or automate
One compelling reason businesses are looking to outsource dataops is the talent shortage. The skills challenge is an important consideration as businesses look at whether they can staff and manage their own dataops team.
Machine learning-driven data analytics requires specialized skills. While devops and dataops initiatives often look to automate processes, there are plenty of roles in the data supply chain that cannot be automated. For example, the data scientists who interpret the intelligence and align it with business requirements, first dubbed the analytics translators by McKinsey, cannot be automated.
To address these talent challenges in house, businesses can identify highly skilled employees who work around data, such as software engineers or business analysts, and start training them up for this higher level of analytics work.
For businesses unsure of where to start with dataops and limited in resources, agile offers a good starting point: small increments. If an organization can’t build an entire team or process, it can start with one manageable data project across a cross-functional team. A small start is still an opportunity to look for automation potential, such as data ingestion or testing, and to begin building the data channels that can fuel future projects and more mature analytics down the road.
Enlist internal data consumers as early customers
Just as small, incremental projects offer a good starting point, employees—internal data consumers—also make good early customers for dataops. From executive teams looking for guidance to departments balancing heavy loads of data to manage and store, internal teams have key business strategy goals to attain. By driving the development of highly automated apps for use within the organization, dataops empowers internal teams with the intelligence needed to take their projects to the next level and grow support for future dataops projects that involve external stakeholders.