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Analysts preview the future of data and analytics leadership at the Gartner Data & Analytics Conference
In a press conference held on June 20, for the opening of the Gartner Data & Analytics Conference, in São Paulo, Donald Feinberg, vice president and Emeritus Analyst at Gartner, stated that business leaders should focus on building a data-centric organization, leverage key trends and emerging technologies, and design the results that lead transformational business models. "It's not hard to imagine a world in which data and analytics applications are used pervasively in every business process and every decision we make," says Donald Feinberg. "However, to make this a reality, we need to help our colleagues everywhere in our organizations become comfortable working with data in their activities." The executive also highlighted the results of Gartner's CIO survey, in which Latin American executives have placed business intelligence (BI) and analytics as the number 1 priority in their businesses, essential for achieving the other goals of organizations: business growth, financial stability and cost management. focused on the theme, which help to evangelize companies and show the paths for advances in the practice of BI.
João Tapadinhas, research director at Gartner, highlighted the importance of organizations going beyond the traditional BI phase to a stage of industrialization of the use of analytics and user empowerment, so that they themselves know how to use the tools and are no longer dependent of the IT team. “The technologies allow BI to investigate and solve more complex problems in companies from a new dynamic that offers a self-service of data to users, in real time. This change demands that more people acquire skills in analytics and the reformulation and integration of processes”, he explained. The director believes that software companies have made an effort to make their customers move from traditional BI to self-service analytics, through structured internal networks and the effective crossing of information within an organization, from the integration of systems ERP, CRM, Sales, among others.
Gartner took advantage of the event to present a three-step plan to achieve data and analytics abundance. See what they are:
Step 1: Rethink Leadership
Organizations should start by considering creating a data office and appointing a Chief Data Officer (CDO). Gartner's research on key data actors shows that the CDO's primary responsibilities across the organization are an oversight of data governance and analytics initiatives, followed by responsibilities to define the analytics strategy for the organization and ensure reliability and reliability. value of information, that is, its governance. "The increased role of the CDO reflects the growing need for open leadership of data-driven digital businesses and for championing the value of information assets," commented Feinberg. "However, the CDO's role is more influential than control. We still need to enable decentralized departments to play larger roles in organizational strategy."
Step 2: Modernize the Technology
After leadership, a big part of achieving analytical abundance is dealing with the scale and variety of available data. Traditional approaches to data management infrastructure such as data warehouses, batch data streams and relational databases are starting to break down in the face of digital business requirements. Organizations must rapidly adopt architectures and technologies, such as data virtualization, that enable real-time data integration and access needs. "By 2018, Gartner predicts that organizations with data virtualization capabilities will spend 40% less building and managing data integration processes to connect information assets," says Tapadinhas. "This is the key: getting a data management infrastructure capable of supporting digital business demands requires collection and connection. Collecting and securing data ensures the reliability of mission-critical processes while connecting to data allows you to support real-time requirements. , handle massive scale and distribution, and support rapid experimentation.”
Step 3: Maximize Your Business Contribution
One of the keys to maximizing business contribution and harnessing this abundance of computing power and analytical expertise is to turn data governance into a business enabler. This management needs to shift from centralized, top-down and dictatorial to local, collaborative, agile, flexible and business-oriented. At the same time, data and analytics leaders must still achieve the trusted, shared, and consistent views of master data management (MDM) data and data quality initiatives. "Obtaining this right – context-driven policies and distributed authority and responsibility – is critical to providing the high-value, trusted foundation of data that supports any use case an organization's leadership may have in mind," Feinberg concluded.
João Tapadinhas, research director at Gartner, highlighted the importance of organizations going beyond the traditional BI phase to a stage of industrialization of the use of analytics and user empowerment, so that they themselves know how to use the tools and are no longer dependent of the IT team. “The technologies allow BI to investigate and solve more complex problems in companies from a new dynamic that offers a self-service of data to users, in real time. This change demands that more people acquire skills in analytics and the reformulation and integration of processes”, he explained. The director believes that software companies have made an effort to make their customers move from traditional BI to self-service analytics, through structured internal networks and the effective crossing of information within an organization, from the integration of systems ERP, CRM, Sales, among others.
Gartner took advantage of the event to present a three-step plan to achieve data and analytics abundance. See what they are:
Step 1: Rethink Leadership
Organizations should start by considering creating a data office and appointing a Chief Data Officer (CDO). Gartner's research on key data actors shows that the CDO's primary responsibilities across the organization are an oversight of data governance and analytics initiatives, followed by responsibilities to define the analytics strategy for the organization and ensure reliability and reliability. value of information, that is, its governance. "The increased role of the CDO reflects the growing need for open leadership of data-driven digital businesses and for championing the value of information assets," commented Feinberg. "However, the CDO's role is more influential than control. We still need to enable decentralized departments to play larger roles in organizational strategy."
Step 2: Modernize the Technology
After leadership, a big part of achieving analytical abundance is dealing with the scale and variety of available data. Traditional approaches to data management infrastructure such as data warehouses, batch data streams and relational databases are starting to break down in the face of digital business requirements. Organizations must rapidly adopt architectures and technologies, such as data virtualization, that enable real-time data integration and access needs. "By 2018, Gartner predicts that organizations with data virtualization capabilities will spend 40% less building and managing data integration processes to connect information assets," says Tapadinhas. "This is the key: getting a data management infrastructure capable of supporting digital business demands requires collection and connection. Collecting and securing data ensures the reliability of mission-critical processes while connecting to data allows you to support real-time requirements. , handle massive scale and distribution, and support rapid experimentation.”
Step 3: Maximize Your Business Contribution
One of the keys to maximizing business contribution and harnessing this abundance of computing power and analytical expertise is to turn data governance into a business enabler. This management needs to shift from centralized, top-down and dictatorial to local, collaborative, agile, flexible and business-oriented. At the same time, data and analytics leaders must still achieve the trusted, shared, and consistent views of master data management (MDM) data and data quality initiatives. "Obtaining this right – context-driven policies and distributed authority and responsibility – is critical to providing the high-value, trusted foundation of data that supports any use case an organization's leadership may have in mind," Feinberg concluded.
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