Enterprises may build their businesses on data, but they don’t necessarily manage it well and this is where Chief Data Analytics Officers can play a highly valuable role. They can help an organization value its data across the enterprise. Whether the role is called Chief Data Officer or Chief Data Analytics Officer or even head of analytics the goals of all are similar.

According to a Gartner survey, 496 data and analytics (D&A) leaders were surveyed in its 7th annual survey & the findings rendered valuable lessons for anyone accountable for building value from their enterprise’s Data & Analytics assets as well as ecosystem. CDAOs can see from the survey results where exactly to focus their time & attention in order to generate outsized impact and failing to prioritize effectively almost certainly guarantees poor team as well as the enterprise’s performance.

In a new Chief Data Analytics Officer role, you are the first ever at your organization you arrive with expectations. The criteria for success comprise the implementation of holistic data pipelines, facilitating data sharing, & analytics modernization which depicts a clear impact. Achieving quick wins is possible and these successes start with a smart modernization strategy that completely leverages the power of the cloud. Here are four areas where CDAO can make a rapid impact in the business.

Embrace the cloud

New CDAOs & CDOs chasing data & analytics goals by using traditional infrastructure strategies face headwinds. According to a report by Gartner it was estimated that the failure rate for big data projects is at 80 percent, approximately. Improvising together cloud-based data technologies for analytics that are DIY may need 6 - 12 months in order to get off the ground, and then 7-figure annual budgets for maintaining it. Finding DevOps teams wielding both cloud & data skills are expensive. Plan to spend about 5 teams the cost on personnel which you do on the tech stack itself if you choose this approach. These numbers place hard limits on scalability & make it clear why most of the projects fail even before yielding results that are useful.

Coming up with a modern cloud stack shifts the odds in your favor. Cumbersome processes & legacy platforms could be replaced with flexible, cloud-first tools that are designed for supporting the variety of ongoing use cases, data sets, & AI/ML applications that you need. For instance, cloud data lakes for analytics could be turnkey as well as production-ready without needing internal DevOps, CloudOps, SecOps personnel, or overhead. Such sort of strategies will set a CDAO’s foundation for project acceleration out of the gate.

Get the right employees

All that is required for data & analytics success is a small team that is led by a strategic CDAO. You must possess the right team. The team should comprise either one or more talented data scientists. However, it need not be bloated with expensive DevOps staff. In order to move fast, it is crucial to meticulously consider the needs of the organization, and team, & what technologies will make them the most efficient. Assemble team members with the skill & mindset for utilizing modern analytics & ML techniques to deliver results. Further, ensure they possess what they need to collaborate & succeed.

Introducing self-service analytics & data democratization

The main purpose of any C-Suite data leader’s data modernization imperative is to empower data scientists as well as other business users to efficiently access data & derive insights. The IT teams have to take action to complete any data request while in legacy systems, data access processes are most complex & cumbersome. The process needs transferring data securely from its source systems, preparing data for analysis, & landing data on the proper platform for user access.

Ensure your modernization efforts focus mainly on alleviating all barriers to data access & on accommodating the needs of the business users. Data scientists should not require either operational, DevOps, or data engineering skills. They should possess simple & direct access to analytics-ready data which can be easily put to use in their models. They (data scientists) should also have the capability to utilize whatever tools & methods they prefer for efficiently doing their best work. No one should be held back technically including lacking Analytics & AI/ML capabilities. At the same time, your self-service data & analytics pipeline should still meet all security, compliance, & governance requirements.

Deliver more use cases, faster

Creating an impact is based on delivering results across the enterprise. The more projects/use cases you enable, the more potential impact. Use cases can either be as simple as self-service access to a certain data set or larger and this could include revamping risk modeling algorithms or also customer 360 projects.

CDAOs must add both systems as well as processes which lets for quickly scoping & delivering use cases across the enterprises, potentially in parallel. A few data sets will facilitate multiple use cases, while a few will have a more visible & strategic impact and others would be more subtle, delivering longer-term results.

The way ahead

Savvy CDAOs have to deliver quick wins & a variety of use cases. Cloud modernization & a repeatable process for scoping & delivery are the critical tools in the CDAOs arsenal. Future Chief Data & Analytics Officers will possess a mix of executive experience, Artificial Intelligence & analytical skills, data management, & the ability to measure value in business terms.