The growing importance in strategic significance of data and analytics is creating new challenges for organizations and their data and analytics leaders. Some traditional IT rolls are being disrupted by what are called Citizen roles, performed by line function business users. Other new hybrid roles are emerging that span functions and departments and blend IT and business roles to become almost the norm. In a recent survey among data and analytics leaders, 45 percent of the respondents mentioned that they consider their role to be a blend of IT and business skills. Several key factors are contributing to the emergence of these kind of citizen and hybrid roles. They include the increased strategic importance of data and analytics, driven by digital transformation. Also, the increased business and domain-led analytics has led to many part-time and hybrid roles across departments and lines of business, including IT thereby increasing business complexity. Algorithmic business also is creating new responsibilities and roles for those managing data and analytics, and asks for different complex skills in areas such as Artificial Intelligence. Much needed fast-paced business requires streaming data and continuous analytics. In other words, real-time analytics on constantly changing data, requires different skills and different mindsets. Traditional data management roles are impacted by the emergence of new user profiles demanding more autonomy in Data Management activities. Data Management roles also need to evolve to meet new and increasing demand for accessing data. New citizen roles such as the citizen data scientists are complementing traditional roles and require fresh approaches to responsibility, and accountability for Data Management activities. The need to right size the work of data and analytics governance is leading to smarter, more adaptive governance. This in turn leads to a need for changes in organization roles related to data and analytics. Finally, core architect roles remain crucial for guiding management activities, but more as an embedded capability in business domains as opposed to a focus on distinctly independent or centralized functions. Data and analytics is used across the entire organization. No longer can either multiple disparate teams or a single centralized team with a single organizational structure be identified. The reality is a hybrid and distributed organizational model spanning all data and analytics use cases throughout the organization. Data and analytics leaders have to optimize their domain-specific analytics competencies for success in digital business and to sufficiently support multiple use cases. Next, let's highlight the following important roles that data and analytics leaders need to consider today including analysts, business process analysts, data engineers, even data ethicists, and information architects. There's no single type of analyst, rather a spectrum of analysts. Their roles depend on the analytics use case. They vary by interaction and introduce different responsibilities and skill requirements. The consumers of analytics business people, prefer their interaction in the form of consuming prebuilt reports, and dashboards for the most part. They expect interaction such as filtering or drill-down, but don't actively develop analytics output for the most part. Business analysts have key responsibilities to develop reports, dashboards, and interactive visualizations, and to work with data warehouses, data integration, and data modeling. Citizen data scientists are those who aren't formally trained or designated as data scientists. But they can still execute a variety of data science tasks, supported by the technologies such as smart data discovery tools. Citizen data scientists possess the ability to extend their analytics expertise and use their business acumen to derive advanced insights, moving closer to what a data scientists can offer. Statisticians are advanced versions of business analysts with a key responsibility for analytic models and programming. Then of course, there's data scientists who are critical staff members that can extract various types of knowledge from data, and who have an overview of the end-to-end process and can solve data science problems using a variety of data, and a variety of analytic techniques. Business process analysts or BPAs take a critical view point on managing a business process from end to end in the context of a business outcome, or performance rather than managing application performance which addresses merely a technical view. The primary role expansion for BPAs includes the following worker tasks. First, to identify the most critical data needed by each application, and to separate that data which is the most important from that which is the least important. Second, to identify which business processes weren't centralized or global governance, versus those that require regional or local governance. Third is to determine the information governance policy needed and the goals or targets for each data element. Fourth, the application of continuous intelligence requires additional tasks such as process redesign, optimization, and change management. Data engineers make the appropriate data accessible to data scientists or data analysts. This leads to potentially big productivity gains, as much of the time spent experimenting with data's time spent combining the data. Data engineers are the mission-critical support staff, in particular for large data science teams. In contrast to data stewards, their core task will be much more tactical and might include the following. They need to collaborate with the business users and data scientists, become data gurus who know how and where to start with data, and which pipelines are business-centric. They educate the organization on data engineering, ensuring users can do it on their own to help them become citizen data engineers, and they help data scientists to prepare data in or develop end-to-end data pipelines. They also take responsibility for streamlining data pipelines across the enterprise, ensuring that they're production-ready. They assist with the initial data exploration steps, and they assist with programming, modelling, and data integration. They also catalog existing data sources and enable access to resident and external data sources, and they support data stewards to establish and enforce guidelines for data collection, integration, and processes. The information architect or data architect, strengthens the impact of improvised recommendations on business information. This is information that will be needed to be available, and shared consistently across the company through the identification, definition, and analysis of how information assets drive business outcomes. The information architect is responsible for discovering the data and analytics requirements for all users. This includes partnering with business leadership, to provide strategic information based recommendations, to maximize the value of information assets, and protect the organization from disruptions while also embracing innovation. They're involved in assessing the benefits and risks of information by using tools such as business capability models, to create an information-centric view to quickly visualize what information matters most to the organization based on the defined business strategy. They also conduct information modelling, or data modeling, creating and managing business information models in all their forms including conceptual models, relational database designs, and messaging models. They ensure that the architecture is used to identify, prioritize, and execute the data and analytics initiative with a clear line of sight to enterprise strategies of business outcomes. A technologists looks at what we can do with the data. A compliance officer knows what we must do with the data, taking into consideration regulations like GDPR. But with technology innovation stretching the borders of what can be done to the limits of our imagination, and regulations often trailing the pace of innovation, a third question emerges. What should we do with that data? Just because we can do it, doesn't mean that we should. This is the domain of the data or digital ethicist. The data or digital ethicists thinks through the unintended consequences of the use of data, and determines the risks and opportunities. What value can be generated from new uses of data, and does it match the organizations values? As not all unintended consequences can be predicted, the data ethicist also monitors for unforeseen consequences. Finally, the data ethicist is responsible for making all stake holders ethically aware. As the transformation toward the digital and algorithmic business continues, new roles will be emerging, that often introduce a blend of IT and business roles. In fact, the lines are blur and some no longer exist. Get ready today to develop new roles such as the following. Continuous intelligence roles. These roles are involved in designing and building continuous intelligence into business processes. Business operations require that business analysts, analytic professionals, and software developers acquire new skills and perform new functions. Continuous analytics, spans analytics, business applications, business process optimization, and design automation. Another new role is an algorithmic business domain expert. Business acumen, deep industry knowledge, and consumer behavior expertise characterize these individuals. They operate at the intersection of deep business and deep algorithmic skills, a rare blend. Think people who build algorithmic trading models for the stock market, or seismic engineers discovering oil through analytics. With their business domain expertise, they know when and how to apply what modelers and data scientists build. Then there are algorithmic business trailblazers or innovators. They know how algorithms lead to a competitive advantage. They have a broad and detailed view of an industry, to the point that they know which algorithms can markedly differentiate an enterprise from its peers, and when and how to apply them. Furthermore, they have the political and business clout to persuade others, to take the risk that algorithmic business requires.
