At CIO Survey 2019, Gartner found that Japanese companies give lower priority to invest in data analytics as "game change" technology. This session will explain what is a barrier and a solution for this in Japanese companies.
Many Japanese user companies are suffering from where to start artificial intelligence. In this session, we will present the next actions to enterprises before the undertaking regarding how to proceed with the initial practice, or companies already undertaking initiatives against the current problem.
This session discusses how to create or modify the digital strategy in a Japanese enterprise.
Explore with Gartner what is data literacy. Also explore techniques to advance data literacy inside your organization.
CIOs and IT leaders need to push senior managements to initiate and proceed with digital transformation. This session will provide guidance on how to drive changes in senior managements and introduce several methods to deal with company politics.
This session introduces the seven essential leadership skills needed to successfully drive digital transformation and innovation in Japan. It will provide practical examples on what has worked and lessons learned on what has not worked so well in risk averse, hierarchical and consensus-oriented Japanese enterprises.
Fresh hot roles for the information-savvy organizations are emerging to help organizations become more data-driven. New skills and competencies are required for existing roles to adapt to the changing role of data and analytics. This session will highlight the key roles and responsibilities in data and analytics to be ready for the digital business and the impact on the organization model.
What's happening in data and analytics forcing change?
What's the impact on the organization?
What's the impact on roles and skills?
Japan is lagging the world in digital transformation. Main barriers are lack of talent/resources, culture (e.g., fear of failure) and unclear business scope.
This session shows how CIOs can overcome these barriers and drive digital transformation, working within the culture setting of Japanese enterprises.
Recently, many business executives who hear or read about big data, IoT and AI, or watch about latest case of these believe they also have to do something about data.
On the other hand, many enterprises has been using data but struggling to gain better business outcome.
To gain positive outcome from data, appropriate support organization and leadership is essential.
Many organizations are dispensing with the notion of classic enterprise reporting and BI competency centers (BICC) within IT organizations. In their place, self-service analytics and data science organizations have emerged with nascent analytics centers of excellence (ACE) to govern, guide and support them. Yet many such functions tend to lack a significant number of key capabilities.
The theme of the fourth CDO survey was business impact of the CDO. The following key challenges before CDOs were explored: Ability to quantify the value of organizational information (infonomics), level of embracing emergent and disruptive technologies (such as AI and Blockchain) and operational model (services only or combination of services and enablement). Learn how successful CDOs have addressed each of these challenges in the delivery of positive business impact.
Predictions by data from inside and outside of organization embedded to the packaged applications and SaaS applications. And it will be embedded in the custom-built applications in the company also. On the other hand, it will appear negative impact that will shadow AI, lack of ethics, skills, resources and etc. This session will be talking to data and analytics leaders about what should they know and how to prepare for those issues.
Introducing the term 'information as a second language' (ISL/I2L) as an umbrella for driving data literacy as a core, strategic business discipline.
● What is data literacy and why does it matter?
● What is information as a second language (ISL)?
● How can you improve your organization's data literacy?
IT strategy is now part of business strategy for growth. In this session, you will learn how to start your initiative to define, team, develop, present and measure your IT strategy.
AI is not the only thing driving rapid change in data and analytics. From the next generation of augmented analytics tools to the use of continuous intelligence to interpret streams of data from IoT or the potential of conversational interfaces, there is rapid evolution in how and where analysis can be deployed. Understanding the business impact of these changes will allow organizations to prioritize the innovations that will drive digital business.
Data and analytics leaders are responsible for both information governance and data monetization. But as a relatively new function in many organizations, data and analytics teams (led by the CDO) are under the spotlight to produce high-quality results quickly and efficiently. This foundational session will cover:
What new skills and roles are needed for data and analytics?
How to organize for success?
And how to avoid potential pitfalls?
Best practice roadmaps through Gartner’s implementation styles can create early high-value deliverables to any MDM program. We'll cover typical roadmaps for both customer and product master data and their ability to deliver early business value. How do you choose MDM implementations styles? What are the best practices for sequencing styles? How should you manage the risks of this approach?
That is why IT leaders are thinking about what data is valuable for the data-driven management. Some IT leaders are asking to the CxOs what they want to know from the data. However, most cases could not have had the exact answer that IT leaders imagined. In this session, discuss the gap and how to avoid it based on the knowledge from interaction with the Gartner customers and interview sessions.
In the age of digitalization, "data" is considered as the source of business value. In Japan, many organizations are launching data-focused businesses but on the other hand, privacy risk is also increasing like never before. In this session, we describe the key points organizations must consider in such situations surrounding the enterprise.
(Although this session may include a discussion of related legal issues, Gartner does not provide legal advice or services, and its research or guidance should not be construed or used as such. The client is strongly advised to consult with their own legal counsel in considering and applying the advice and recommendations contained in this session.)
Organizations increasingly need to connect things that share data — disparate data and analytics programs, MDM and master data stores, applications, processes, teams, and external partners. But without a well-planned strategy based on requirements for mediation and governance, it's hard to enjoy a smooth flow of trusted data.
● What are data hubs and how do they support data sharing and governance?
● What are the most effective starting points for a data hub strategy?
● What are the best approaches to architect and deploy data hubs?
This session will describe the foundational concepts for the discipline of master data management and the technology solutions that support it. What are the current states of the MDM discipline and its associated software market? What are the latest implementation trends and market developments? How can you prepare to effectively take advantage of these developments?
"What should my Data and Analytics strategy look like" is one of the most common inquiries Gartner's D&A research community gets. This is because the role of D&A is changing. From being a discipline of itself, to becoming a set of capabilities in support of the wider digital strategy or transformation. This presentation will share the best practices that have emerged from hundreds of document reviews and thousands of inquiries.
This session will provide a high-level introduction to data science and machine learning and their proper function in a data-driven organization. Content will include hype vs. reality, key trends, proven use cases and an overview of leading technologies. How do data science and machine learning fit within both the organization’s analytics and AI strategies? What are the early steps data and analytics leaders should take to invest in data science and machine learning? What do the first two years of a data science and machine learning initiative look like?
Modern analytics and BI tools are deployed on many organizations but agile, governed and business-impactful self-service analytics is still a mirage for most users. From analytics decentralization that remains centralized, to self-service anarchy, failure has many shapes and forms. Learn about best practices to start, evolve and expand self-service analytics for pervasive and timely access to information across the organization.
Data and analytics leaders including CDOs often lack the internal resources or skills needed to speed up and scale D&A and drive digital business value and they look for external support. Although the D&A services market is mature it is also facing disruption, changing the behavior of system integrators and consultancies. So what do DA leaders need to do, to find and choose from hundreds of possible service providers?
When to select an external service provider?
How to select an external service provider?
How to manage the relationship?
Augmented analytics is a disruptive trend that leverages machine automation and AI to transform how data is prepared, how insights are generated and shared and how data science and ML models are created and operationalized. This presentation provides an overview of the key trend and how you need to fully leverage it in your organization.
Almost every large enterprise has implemented BI tools and they have gotten reasonable outcome. On the other hand, the needs for data is changing day by day, so many users feel frustration against current BI. To get better experience, new visual rich tools and cloud base offerings are purchased by business users. This presentation will explain BI trend and the hint to improve your BI environment.
Analytics should be a key enabler of organizations' goals but that is seldom the case. Most initiatives tend to focus on tools deployment and user support with little connection to business objectives. In this session, we describe a step-by-step methodology to design a business-outcomes-driven analytics evolution roadmap with a strong commitment to users and a shift from technology to business impact.
For many I&O leaders, AI ecosystems for machine learning and deep learning can be complex and capex intensive. We share the market landscape of cloud solutions for AI, based on our exhaustive market surveys and the current state of the art technologies. We also present best practices in devising hybrid and public cloud strategies.
Data science techniques harness predictive insights to drive prescriptive action. This presentation will provide an approach for extending from predictive analytics to prescriptive approaches. Questions addressed include: How do predictive and prescriptive analytics complement each other? What are the most common techniques for both? What are best practices and recommendations for combining them as demonstrated through case studies?
A new era is about to begin. There is a need for infrastructure strategy for the new world which is not an extension so far. It is assumed to be digital, Mode 2, cloud. In this session, we describe the next generation infrastructure strategy, IT organization and people’s way of thinking to accelerate preparation for disruption.
This session will cover trends and best practices around hiring and training not only data scientists, but the entire skill mix necessary to build successful data science teams: Data engineers, developers, machine learning specialists and domain experts. Training and upskilling have also become vital components of data science initiatives, as citizen data scientists become the leading source of new models and even expert data scientists struggle to keep up with the latest techniques and innovations.
How many traditional data scientists do I need and what does their supporting cast look like?
What are the best options for training and education in data science and machine learning?
What are the organizational principles for placing data science teams?
The democratization of ML and the popularity of open-sources languages are facilitating the proliferation of ML models. However, as analytical assets are being deployed across organizations, the scalability of their performance and integrity (and their economic value) is in question. Organizations need to establish data science operational strategies to scale and systematically monetize their data science efforts.
As the usage of public clouds has expanded, data analysis and machine learning are increasingly being deployed on the cloud. That is not only for cost reduction, but also for new use cases enabled by advanced services. This session will explain how the cloud is transforming data analysis/machine learning.
This session showcases top disruptive innovations with live demonstrations from representative vendors leading in these areas. Key issues addressed include: How can you use the Hype Cycle to track emerging trends? What are 4 innovations that will transform your business and what value do they provide? How do you prioritize your technology investments? Which are the vendors to watch? What challenges should you consider?
This session will also include live demos from representative vendors in each innovation area.