"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.
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.
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.
Fresh Hot Roles for the Information-Savvy Organization 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. Key issues: What’s happening in data and analytics forcing change? What’s the impact on the organization? What’s the impact on roles and skills?
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?
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.
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 GDPR raises the compliance bar and introduces a full set of organizational and procedural requirements before personal data can be properly handled. Sanction risks, customers in action and pressure to deliver value don't have to kill your analytics capabilities. The proper approach to personal data management not only legitimizes your actions, but delivers customer value where it's valued: With the customer.
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.
Data storytelling offers a more engaging means of communicating findings than BI reporting or data visualization alone. This trend is an extension of the now dominant self-service model of BI, combining data visualization with narrative techniques. What is a data story? When and how should data storytelling be used? What new skills and techniques are needed to create compelling data stories?
Analytic applications offer packaged analytics to solve specific industry vertical or domain problems and are important part of an overall analytics strategy. But how to pick the right one? Where to find analytic applications? What are the most important things to consider in evaluating different vendor options?
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?
As machine and deep learning models are critical parts of more and more business decisions, knowing how those models derive their insights is becoming necessary. From potentially harmful biases to legal and regulatory compliance, the anatomy of business decisions is vital to generate trust in the process before those models apply decisions at scale. A few best practices can help organizations in opening those black boxes.
Customer analytics is one of the primary drivers of analytic adoption. The sheer diversity of potential opportunities to apply analysis to deliver great customer experiences and improve customer relationships can be daunting.
- What are the key areas of customer analytics?
- What are the best practices in customer analytics?
- What are the key trends in customer analytics to plan for?
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?
This presentation highlights the foundations and key trends in analytics and BI, and is a must-attend for all data and analytics leaders and business practitioners involved in analytics who are interested in starting or expanding their subject-matter expertise.
The Future of Data Management will help plan for what’s next. It describes where the market, function or capability is going and how it’s evolving. It should help early adopters get a leg-up and more conservative companies plan and prepare for and make the changes.
Building transparency in uses of data has come to the forefront with GDPR and data governance pressure. This has created increased interest in metadata management and data catalogs. Yet Data and Analytics leaders struggle to get the momentum needed to start and develop the metadata management practice.
1. How can metadata management deliver value?
2. How to set up a metadata management practice?
3. What technologies can help support the metadata management practice?
The traditional data warehouse still serves as the basis for analytics programs and remains foundational. However, increased demand for new data types and new use cases continues to expand. Data warehouse architecture has to evolve in order to meet these demands in both distributed and centralized solutions. This often means adding new technologies like Hadoop. Advanced architectures like the logical data warehouse help make this a reality.
Everyone wants a data lake, but most attempts fail to live up to hype around this concept. This session explores the multiple reasons why data lakes fail and how you can avoid the traps. Specifically, this session covers:
What are the major scenarios for data lake failures?
How can you detect and correct these scenarios?
Data is at the core of digital transformation. Enabling the use of data across the enterprise and even outside has become an imperative. The database management market is undergoing a rapid and profound transition as the supporting technology is changing. Cloud and dbPaaS is becoming the platform of choice and pricing models are under pressure from both open-source and the cloud.
What is driving the data infrastructure transformation to new technologies, platforms and cost models?
How are existing technologies changing and what new technologies are emerging to support this transformation?
What are the vendors doing and how will the market evolve?
Effective governance is a critical success factor for data and analytics initiatives, and one of the most difficult challenges that organizations face. This session explains how to establish solid foundations needed for successful data and analytics governance today, and the future direction of governance as it evolves to address new business and technology challenges.
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?
Successful implementations of digital platforms remain elusive. Data and analytics sits at the core of the digital platform, but what strategy should you pursue? This session starts the discussion by presenting three competing and complementary options, and how they are used to supercharge your existing business or to pursue net-new products and business models. Specifically, this session will explore:
● What are the differences between hubs, lakes and warehouses?
● How do you balance the trade-offs between these options?
● What are the technology options and how are they integrated?
The Future of Data Science, Machine Learning and AI presentation is for people looking to plan for what’s next. It describes where the market, function or capability is going and how it’s evolving. It should help early adopters get a leg-up and more conservative companies plan and prepare for and make the changes.
The NLP market is vast and fragmented. But, from fulfilling customer demands to analyzing vast number of documents, conversing with employees, translating citizens’ requests in real-time to recognizing patients’ sentiments, NLP techniques have seen great advances in the last few years. Organizations should leverage those techniques around carefully targeted business problems, estimating tangible business outcomes while managing expectations.
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.
Blockchain's data provenance features and trusted interactions could change how data is controlled, shared and governed. Although big technological hurdles remain, innovative data management opportunities are emerging. It's time for data and analytics leaders to start experimenting. This session explores:
● How does blockchain compare to today's databases?
● How might blockchain disrupt your data management program?
● What is the maturity level of data management on blockchain, and where should you begin?
The growth of digital business and increased customer expectations are elevating the importance of real time, contextualized customer experiences for competitive advantage. This session will cover the emerging practice of 'always on' continuous intelligence in the customer engagement hub to prescribe the right action at the right time based on the most relevant business moment.
So you thought "big data" is large and complex and fast-paced? Consider how billions of devices, outside your line of sight and generating oceans of events, are going to put pressure on your ability to ingest, store and process data. Digital business and IoT hold massive promise for innovation, new business models and advanced analytics. How does the IoT create new data and analytics challenges? What must data and analytics leaders do to drive adaptation for the IoT? Which new capabilities will be critical to success?
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 demos from representative vendors in each innovation area.
Continuous intelligence brings dramatic, measurable benefits in revenue generation, resource allocation, customer service and other metrics. More than ever, Data and Analytics leaders use real-time analytics to improve the performance of their businesses.
Why is continuous intelligence proliferating now?
What are best practices for deploying analytics into operations?