Whatever a company does, how it uses data is a key differentiator in its success or failure. Whether that data is generated internally or gathered from an external application used by customers, organizations now use on-demand cloud computing resources to make sense of the data, discover trends, and make intelligent forecasts. And while organizations are trying to bridge the skills gap by hiring data scientists, data analysts, and data engineers, some are giving these highly technical individuals a seat in the C-suite in the form of the chief data officer (CDO). 

The role is a relative newcomer to the boardroom. In fact, in a 2019 edition of Industrial Management & Data Systems, a research team led by Yu Nie noted that prior to the year 2000, there were only six chief data officers in the world. But that number rose sharply afterwards, with the team noting there were over 1,000 people in this role by 2015. Clearly, data is becoming more important to organizations. In this article, we explore the role and responsibilities of the chief data officer and the challenges they are facing.


The role of the chief data officer

Not all organizations are at the same point in their data journey. While some companies have sophisticated data science teams using high-level programming languages and advanced analytics to derive intelligence, others are just beginning their journey and are studying how to best structure, sanitize, and organize their data. Because the data maturity of an organization varies widely, so too do the CDO’s responsibilities.

To help provide guidance for what role a chief data officer should play at a particular organization, Yang Lee and a research team introduced their cubic framework for the chief data officer in their seminal 2014 paper for MIS Quarterly Executive. In it, the team argues that there are three dimensions of the chief data officer role:

  1. Collaboration direction dimension: Inward vs. outward. This dimension shows where the main focus of the chief data officer lies. An inward focus means the CDO is more interested in improving internal processes and identifying opportunities for those inside the operation. CDOs who have an outward focus for collaboration are more concerned about aligning data strategy with external vendors, suppliers, and customers.
  2. Data space dimension: Traditional data vs. big data. This dimension focuses on what type of data the CDO has to wrangle. Traditional datasets are often relational data found at the core of transactional services and operations: Think of an accounting system or point-of-sale system that spans multiple locations. The authors use the term “big data” here to denote the schema-less data systems that are typically powering more of the modern, web-based applications. Often, this kind of big data is not connected to the more established applications of an organization.
  3. Value impact dimension: Service vs. strategy. This is possibly the most exciting dimension Lee and his team identify. Is the chief data officer more interested in extracting intelligence from data to improve the overall functioning of the organization, such as decreasing churn and improving operational efficiency? Or is the CDO more focused on using data to drive strategic decisions and opportunities for the organization to improve its position in the market overall? This can include driving revenue with data in a variety of ways, such as infusing intelligence into customer-facing apps, products, and experiences.

Lee’s cubic framework for the CDO is an excellent tool for organizations to determine the why behind their need for a chief data officer. Understanding what the organization requires from this role can help define the authority and budget that a CDO has to do their job. But regardless of the company’s needs, a platform like Sisense can aid the chief data officer’s success along all dimensions.

Choosing a flexible solution for data experts and business users alike

Challenge: CDOs are looking for an analytics solution that is flexible enough for the highly technical modeling that data analysts and engineers need while also being accessible for the business analyst. 

Analytics platforms tend to cater to either sophisticated data scientists or less-technical users. So determining the overall analytics solution used by an organization can be a tall order for a chief data officer.

Sophisticated technical talent who are querying data and building models with languages like SQL, R, and Python need a solution that will empower them to dive deep. Platforms like Sisense enable these teams to quickly explore data through code, visualize the results, or convert them to models written back to AWS Redshift or Snowflake. Data teams are able to deliver visualizations via powerful charting libraries and via intelligence infused into workflows that empower the nontechnical user to use this analyzed data to drive better decisions.

Business analysts, who may not have the coding skills needed to derive value from the data, need a suite of self-service features that are easy to use without assistance from the data team. Drag-and-drop analysis and point-and-click drilldown are great starts, and by leveraging artificial intelligence and machine learning for augmented analytics, robust platforms like Sisense empower the growing ranks of citizen data scientists. Elements like natural language processing can even interpret their queries written in everyday language, further increasing the range of people who can derive intelligence from data without technical skills. With Sisense, CDOs can empower customer-facing employees to directly help clients innovate their products and processes and in return leverage those innovations, creating validated requirements for their product engineering resources.

When the CDO provides this level of flexibility and empowers a wide array of both highly technical and everyday users alike, employees can begin to look at how data can be used to improve internal and external collaboration and overall company performance. These empowered employees can also look at opening new revenue streams and enhancing apps, products, and experiences for end-user customers.

Bringing disparate datasets into a single view

Challenge: CDOs need to provide a 360-degree view of data across multiple data silos and types of data sitting on the cloud, on-premises, or even in local drives.

Many large organizations either have a central data warehouse or are in the process of creating one. This is one approach to solving the challenge of data silos. However, it takes time to build and can be hard to manage, not to mention forcing every business unit to use it! Invariably, some teams find a niche application or use case and begin storing data outside of the centralized location. This could be because that department is testing out an idea or may just have a specific niche use case for its area. This situation can quickly pose a dilemma for the chief data officer.

Do they stifle innovation because a group or application doesn’t fit into their overarching data model? Or do they encourage novel ideas at the risk of having unconnected data? Platforms like Sisense enable both by connecting directly to central data warehouses while also providing a caching playground for rapid prototyping. If an experiment proves valuable, that data can then be funneled back to the central data warehouse to be used in future analysis. This flexibility helps the chief data officer solve problems, whether they’re focused on traditional datasets or data generated outside those confines.

Positively impacting the top and bottom lines

Challenge: Chief data officers have to simultaneously balance the use of analytics to improve internal processes that affect the bottom line while focusing on strategic projects to bring in new revenue.

The right analytics tool will empower the chief data officer to play defense by improving metrics that affect the bottom line and go on offense by generating revenue by monetizing analytics.

For CDOs looking to optimize service, analytics platforms can be rolled out internally to help business intelligence analysts identify opportunities to boost profits with analytics that can improve operational efficiencies, reduce customer churn, or increase employee retention. This can help a chief data officer not only empower a number of front-line employees to back up their intuition with metrics, but it also can reduce the number of basic queries that encumber a lean data science team. Vitally, whatever role analytics will fulfill within the organization, the CDO must behave as a change agent across the executive leadership group to influence them and build analytics into the DNA of the company.

Additionally, the ability to infuse insights from data directly into products, services, and experiences allows product teams to differentiate their offering by giving their end users powerful data points right where they’re most useful and can have the greatest impact on stickiness and user adoption. This is one way that the chief data officer can turn data into products, helping to bring a higher level of strategy to the boardroom and unlocking new opportunities for revenue.

Building Apps that Last

Former Sisenser Charles Holive helps chief data officers and CXOs across industries accelerate digital transformation by driving business innovation toward new revenue streams.

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