How Chief Data Officers Can Accelerate Success
If your title starts with the words "chief data," here are four areas where you can make a big impact in your job.
- By Hannah Smalltree
- June 28, 2021
Enterprises are increasingly putting data leadership in the C-suite, tasking them with transforming data asset potential into data-driven action. Whether the role is labeled chief data officer (CDO), chief data and analytics officer (CDAO), or even head of analytics, the goals are similar. Placing the same leader at the helm of data pipelines to analytics and AI/ML creates a through line from raw data to business value. The strategy can yield a rinse-and-repeat cycle of data utilization -- at least for those data leaders who come into the role with the right plan.
In a new CDO or CDAO role -- and odds are you are the first-ever at your organization -- you arrive with expectations. The criteria for success includes implementing holistic data pipelines, enabling data sharing and analytics modernization that shows a clear impact. That's a lot.
Achieving quick wins is possible, and these successes begin with a smart modernization strategy that leverages the power of the cloud. As the newest change agent within your organization, here are four areas where you can make a rapid impact.
1. Embrace the cloud to shave months off modernization
New CDAOs and CDOs chasing data and analytics goals through traditional infrastructure strategies face headwinds. Gartner estimated the failure rate for big data projects at approximately 80 percent. Cobbling together DIY cloud-based data technologies for analytics can require six months to a year to get off the ground, and then seven-figure annual budgets to maintain. DevOps teams wielding both cloud and data skills, if you can find them, are expensive. Plan to spend about five times the cost on personnel than you do on the tech stack itself if you choose this approach. These numbers place hard limits on scalability, and make it clear why so many projects fail before yielding useful results.
As an incoming C-suite data leader with a mandate for change, introducing a modern cloud stack shifts the odds to your favor -- quickly. Cumbersome legacy platforms and processes can be ripped out and replaced with flexible, cloud-first tools designed to support the variety of ongoing use cases, data sets, and AI/ML applications you require. For example, cloud data lakes for analytics can be turnkey and production-ready without requiring internal DevOps, SecOps, or CloudOps personnel or overhead. Such strategies set a CDAO's foundation for project acceleration out of the gate. Cloud spending, per AWS, is still only 4 percent of the overall IT market. The opportunity for an incoming CDAO to be the data and analytics modernizer will be available for the taking for most.
2. Get the right people
Led by a strategic CDAO or CDO, a small team is all that's needed for data and analytics success. You must have the right team, and the team needs to include one or more talented data scientists. However, your team doesn't need to be bloated with expensive DevOps staff. To move fast, it's essential to carefully consider your organization's needs, your team, and what technologies will make them most efficient. Assemble team members with the skills and mindset to utilize modern analytics and machine learning techniques to deliver results. Then, make sure they have what they need to collaborate and succeed.
3. Introduce self-service analytics and data democratization
The central purpose of a C-suite data leader's data modernization imperative is empowering data scientists and other business users to efficiently access data and derive insights. In legacy systems, data access processes are often complex and cumbersome; IT teams need to take action to complete any data request. The process requires securely transferring data from source systems, preparing data for analysis, and landing data on the proper platform for user access. It all makes for a slow-flowing data-to-insights pipeline at organizations saddled with these limits.
Make sure your modernization efforts focus on eliminating all barriers to data access and on accommodating your business users' needs. Data scientists shouldn't require any operational, DevOps, or data engineering skills. They should have simple and direct access to analytics-ready data they can easily put to use in their models. Data scientists must also be able to utilize whatever tools and methods they prefer to efficiently do their best work. None should be held back technically, such as lacking analytics and AI/ML capabilities. At the same time, your self-service data and analytics pipeline must still meet all security, compliance, and governance requirements.
4. Deliver more use cases, faster
Making an impact depends on delivering results across the organization. The more projects or use cases you enable, the more potential impact. Use cases can be as simple as self-service access to a certain data set, or larger – such as revamping risk modeling algorithms or customer-360 projects.
Woe to the CDO who approaches this linearly. CDOs must add systems and processes that allow for quickly scoping and delivering use cases across the organization -- potentially in parallel. Some data sets will enable multiple use cases. Some use cases will have more visible and strategic impact although others might be more subtle, delivering longer-term results.
One thing is clear: Most CDOs won't have years to figure this out. Savvy CDOs must deliver quick wins and a variety of use cases. Cloud modernization and a repeatable process for scoping and delivery are critical tools in the CDOs arsenal.
Demonstrate Value Quickly
No matter what your exact "chief data" title, your ability to quickly assemble a powerful modern data and analytics machine and deliver valuable insights for your organization hinges on modernization through the cloud. With the right team and technology outlook, you'll accelerate analytics benefits, outpace competitors, and clearly show -- in short order -- the value your role brings to the table.
Hannah Smalltree is a VP at Cazena, which provides instant cloud data lakes. Much of Hannah’s career has been in data and analytics, including at Treasure Data, Pivotal, and ParAccel. Hannah was also a reporter and editorial director at TechTarget, where she focused on data management, BI, and analytics.