The growing demands on data infrastructure and analysis are building at an exponential rate. As more and more businesses and agencies begin to place data tools as their primary viewpoint of operational strength, that pressure is likely to continue growing into the foreseeable future. Further complicating the matter is the call for more oversight of privacy concerns and proper data management by the general public and government officials. All of these factors contribute to a complicated new problem that can be solved with clean DataOps and data management.
The collaborative benefits of integrating automated data flow throughout an organization are limited only by our collective imagination and current technologies. As these technologies are rapidly evolving, the conceptual design or flow of a DataOps plan could be obsolete in only a few years without innovation. That means for a business or entity to fully take advantage of the exciting world of data management, they need to build reactive, flexible, and experienced transformation teams as they upgrade to new systems.
Key Members of Your Data Team
Data integration Accuracy, integrity, and redundancy are going to be your friends as you move data from an antiquated system to one built on DataOps principles. To foster this transformation, you will need some essential key players, including:
This is the role given to those who manage and curate the data by building a governance framework for all the included data systems. Their goal is to ingest, store, process, and transmit data based on this framework as well as integrate any new tools that may improve the usefulness of that data before, during, and after transformation.
Just like any builder out in the physical world, this is the person responsible for deploying and maintaining the overall data infrastructure. This could be everything from data pipelines to workflows and anything in-between. They are the quintessential human guidance system that accelerates the movement and acquisition of any data and work closely with Data Stewards and Consumers for improvements.
This role will most likely always exist in some capacity. Ever since the beginning of modern computing, there has been a need for someone to model and visualize data for data consumers. This is the primary position that interprets the information gathered from the data and then presents it to the rest of the team and stakeholders for decision making.
The goal of this position is to provide a consistent and cohesive structure that can scale with the needs of the business or organization. This person needs to have both the technical skills to fix and overcome problems as well as the innovative insights for new system integrations that allow for emergent design.
What About Supplementary Team Members?
While those primary positions may cover most of the roles that maintain, grow, and curate the data as it is moved to new systems, it still requires a lot of supplementary team members to actually integrate the new pipelines.
Think of this as a job site for a new hospital building. Yes, you have the architect, plumbers, engineers, and electricians doing the planning and proper code adherence, but you also have numerous skilled workers executing those designs along every aspect of the building plans.
The same is true for DataOps transformation. This is typically not a small move for an SME company. This tends to be a significant undertaking requiring a lot of eyeballs carefully reviewing and updating information through every stage of the process.
That is why consideration has to be taken for those roles within the IT infrastructure of your company or organizational model. Each member will play a part during this transformation. It is critical that they follow the predetermined plan to introduce an often agile experience.
The Organizational Chart Matters
Most data owners who define and manage the purpose of the information are business leaders. They have to make significant decisions about the future of a business and how to best allocate their carefully guarded resources that present a strong competitive advantage. This is done by leveraging the power of a CDO as the operational liaison between the data owners, the business stakeholders, and the data teams.
Without some kind of organizational chart or inherit understanding from everyone involved in the flow of power throughout the business, there are bound to be mistakes in leadership. It is imperative that these concerns are addressed early and transparently through the planning phase. Otherwise, you could risk the entire transformation due to a simple misunderstanding from leadership concerning the actual work of individual data teams.
This organizational management matters a great deal to data teams, especially when the question of centralized or decentralized deployment during transformation is considered. In a centralized model, the data team is led by the CDO while serving the functional leaders with help from IT. Most decision-making is a trickle-down from the CDO, who holds the final approval of different efforts and maintains a single point of contact or repository of information.
Pros of Centralized Data Management
- Speeds up communication with a centralized source
- Easier to share ideas
- Higher level of accountability through greater transparency
- Reduces conflict while improving security
- Maintains a central vision
Cons of Centralized Data Management
- Harder to implement flexibility to the needs of the business or other entities
- Individual members may feel not as essential because the decision making power is reduced
- Reduces legitimate feedback
Decentralized spreads out the decision-making capabilities where each data team is controlled and managed by the functional leader and depends on IT for infrastructure support.
Pros of Decentralized Data Management
- Far more reactive capabilities to industry trends
- Higher flexibility with greater scalable possibilities
- Allow for more feedback from everyone involved
- Higher morale due to diversified decision-making power
- Greater stability against unwanted downtime
Cons of Decentralized Data Management
- Greater strain on the IT infrastructure
- Possible higher costs for maintenance and more potential for locational issues
- Requires more points of access or physical machines
Enter the Value of DataOps
At its core, DataOps increases the automation of a data management plan by introducing greater agility of data and possible insights. This is accomplished by improving the speed, quality, and continuous improvement of data. The more you transform to a DataOps model, the greater your efficiencies in data generation and processing of processes.
The rapid reusability and consistency around the knowledge gained from a new DataOps designed system and team introduces greater support for your decision-making. It enhances the speed by which you can respond to moves made in the market, by consumers, and by the competition.
That is the driving reason behind the recent mainstream movement to adopt DataOps principles – the competitive factor. As more businesses, agencies, and organizations introduce DataOps, they become better in their respective fields. Those companies that do not move in this direction will immediately begin to see less value creation, market penetration and lower revenues.
The only way to ensure you have high data reliability during the data transformation lifecycle is to introduce data governance through the lens of bespoke DataOps.