Last week Gartner hosted its Data & Analytics Summit in Orlando, Fla. Below is a collection of insights coming out of the conference.
On the first day of the conference, the opening keynote presentation focused on innovation amidst uncertainty along with sessions on digital ethics and the top trends in data and analytics (D&A).
Opening Keynote: Unleash Innovation, Transform Uncertainty
Presented by Gareth Herschel, VP Analyst, Gartner and Debra Logan, Distinguished VP Analyst, Gartner
Data and analytics have simultaneously been a driver, enabler, and response to the uncertainty organizations have dealt with for more than two years. This keynote explored how organizations can consider new perspectives in D&A and design better decisions in a world of perpetual change.
- “As D&A professionals we should always be asking ourselves not just whether we are collecting, integrating, and storing our data in the best way possible, but more fundamentally, whether we have the right data.”
- “Having the right varieties of data is more important than volume.”
- D&A leaders can ensure they have the right data by doing four things: 1) stop collecting data just in case; 2) consider substituting big data for small data, 3) swap real data for synthetic data, and 4) ensure your active metadata tells you not only what data you have, but also what it means.
- “The most valuable data will be the data we create, not the data we collect.”
- Gartner estimates that by 2030, the majority of the data used to build models will be synthetic data.
- “Data alone is unlikely to drive decisions. Design better decisions by improving the timing of decisions, accelerating decisions and connecting decisions.”
Digital Ethics: Where Are You Now and What Will Be Next?
Presented by Frank Buytendijk, Distinguished VP Analyst, Gartner
Digital ethics has become a key component of the D&A leader’s role, as organizations move from asking what they should do about ethics to assessing their progress on digital ethics initiatives. This presentation highlighted how organizations can assess their maturity in digital ethics.
- A persistent misunderstanding is that technology is amoral. In fact, technology is intentional and its design reveals values.
- Digital ethics is essential for all technology applications, but it is particularly important when evaluating complex or emerging technologies such as artificial intelligence, blockchain, and metaverse.
- An organization's digital ethics maturity can range from unaware or purely focused on regulatory compliance; to passive, where issues are solved as they arise; to proactive, where the organization is planning the right thing to do based on multiple perspectives. At the highest level of maturity, ethics is an integral element of all operations.
- Learn to trust the process of developing a digital ethics strategy. Start with principles or values, then operationalize by identifying underlying dilemmas. Then, monitor for unintended consequences and take responsibility by having a process for escalation.
- Remember that different technologies have different moral footprints. For example, databases and development tools may have a lower moral footprint, while tools like AI or the metaverse require new thinking to consider their ethical implications.
- The bottom line is that digital ethics is now a mainstream topic. If it’s not on management’s agenda already, put it on.
Top Trends in Data and Analytics for 2022
Presented by Carlie Idoine, VP Analyst, Gartner and Ted Friedman, Distinguished VP Analyst, Gartner
Managing consequent and persistent uncertainty and volatility will be a key focus for D&A leaders in 2022. This session highlighted the top D&A trends that must be on leaders’ radar to drive new growth, efficiency, resilience, and innovation.
- Trend #1: Adaptive AI systems. As decisions become more connected, more contextual, and more continuous, D&A leaders need to reengineer decision-making. Use adaptive AI systems that can offer faster and flexible decisions by adapting more quickly to changes.
- Trend #2: Data-centric AI. Without the right data, building AI is risky and possibly dangerous. Formalizing data-centric AI as part of your data management strategy will ensure AI-specific data considerations, such as data bias, labeling, and drift, are addressed.
- Trend #3: Context-enriched analysis. It helps identify and create further context based on similarities, constraints, paths, and communities. By 2025, context-driven analytics and AI models will replace 60% of existing models built on traditional data.
- Trend #4: Skills and literacy shortfall. Through 2025, the majority of CDOs will have failed to foster the necessary data literacy within the workforce to achieve their stated strategic data-driven business goals. Organizations must foster broader data literacy and digital learning, rather than simply delivering core platforms, datasets, and tools.
- Trend #5: Connected governance. Connected governance provides a means to connect disparate governance efforts, including D&A governance, across different organizations, both physical and virtual, as well as geographies.
- Trend #6: Expansion to the edge. Data, analytics, and the technologies supporting them increasingly reside in edge computing environments, closer to assets in the physical world and outside IT’s traditional purview.
On the second day of the conference, presenters highlighted how to deliver business value using AI, best practices for trusted data sharing, and actions to improve D&A risk culture.
The Foundation of Data Science and Machine Learning: Delivering Value in the Age of AI
Presented by Peter Krensky, Director Analyst at Gartner
Catalyzed by digital transformation, the need for democratization, and the urgency of industrialization, data science and machine learning (DSML) platforms continue to evolve rapidly. This session examined the major trends, data science talent personas, and an overview of leading technologies in the DSML space.
- “The dawn of AI is apparently going to take close to a decade.”
- “You’re too late to be early in data science and machine learning and one-two years away from being late.”
- “In-house data science teams are both worth it and a significant expense/hassle.”
- “Citizen data science and expert data science are distinct but obviously related disciplines with heavy interplay.”
- “There are more personas than ever involved in data science, plus they’re evolving!”
- “There are plenty of beginner and intermediate opportunities to deploy data science and machine learning.”
- “The data science platform market bifurcated in 2021-2022, and the entire space is in transition.”
Trusted Data Sharing for Optimal Business Value — Top Best Practices to Get It Right
Presented by Lydia Clougherty Jones, Sr. Director Analyst at Gartner
Sharing data is a must for revenue growth, cost optimization, improved risk mitigation, and accelerating digital business. This session explained the business imperative of data sharing to help D&A leaders modernize and align data sharing with stakeholder priorities, enterprise goals, and organization benefit.
- “Mandated enterprise data sharing is closer than you think.”
- “Global data strategies highlight data sharing as a key priority to generating public and private value.”
- “Data sharing is a business-facing key performance indicator (KPI) of achieving effective stakeholder engagement and providing enterprise value.”
- “Embed data sharing in every relationship.”
- “Embrace the chaos within augmented data ecosystems outside of your organization’s control to find known and unknown relationships in combinations of diverse data.”
- “Organizations often unnecessarily require too much trust, or not enough, across data ecosystems, disrupting the risk/reward calculus of data sharing for business value.”
- “Trusted data sharing means the optimal, not perfect, level of trust across data sharing ecosystems. Apply “situational trust,” not perfect trust, to achieve maximum value and benefit from data sharing.”
- “While occasionally the right amount of trust could also be perfect levels of trust, business leaders must resist the emotional pull toward over-investing in perfect trust, which ironically can create enhanced risk given emerging D&A liability theories.”
- “The journey of eschewing perfect trust, and instead establishing the right trust to match the situation at hand, enables new business opportunities for data reuse and resharing, accelerating data and analytics value while mitigating risk.”
Five Actions to Improve Your D&A Risk Culture
Presented by Saul Judah, VP Analyst at Gartner
A data-driven culture is a key to the success of data and analytics teams. But if your culture is not risk-aware, your investments in data, analytics, and AI will be exposed to greater risk. This session explained why a risk-aware culture will help D&A leaders deliver better business value and five actions they can take to improve their data and analytics risk culture.
- “The 2022 Gartner Chief Data Officer (CDO) survey showed that 21% of chief data officers said they are measured on risk, but just 8% said they are involved in implementing risk culture.”
- “Good business decisions cannot be made unless you understand risk.”
- Gartner identified five actions to take to improve risk culture in organizations.
- Assess your culture with observation, metrics, interviews, and surveys.
- Analyze how culture impacts your data analytics strategy and operating model. “Even if you have the best strategy on the planet, your culture could be a limiting factor.”
- Develop risk-aware principles for data and analytics. “A principal is a clear statement. It is universal and applies as an anchor for behavior.”
- Apply “culture hacks” necessary for awareness. “Culture hacking is a method that you can use to make a series of immediate small changes in support of a larger transformation.”
- Be prepared to explain the business impact for each risk, otherwise you will fail. “If you don’t explain the business impact, then the funding you need to address the risk will be difficult to get and if you do get the funding, all the work that you do to address the risks will appear to be on the cost-side.
On the third day of the conference, presenters highlighted the cloud database management market, AI and the future of work, and top trends in data science and machine learning.
Database Management Systems - Accelerate to the Cloud
Presented by Donald Feinberg, Distinguished VP Analyst, Gartner
The database management market is undergoing a rapid transition, and cloud has become the platform of choice. This session outlined what is driving the data infrastructure transformation to new technologies and how database technologies are changing to support the shift to the cloud.
- “Digital business demands a fully configurable D&A environment. Multiple technologies are supporting this shift, including hybrid cloud, multicloud, and intercloud solutions.”
- “The database management system (DBMS) market was worth $64.8 billion in 2020. Cloud database platform as a service (dbPaaS) growth drove 93.5% of the $9.4B DBMS market growth.”
- “Cloud DBMS deployments offer numerous benefits, such as the creation of new financial models, rapid provisioning, increased elasticity and scalability, and more agile development capabilities.”
- “There are also some potential cons of cloud DBMS such as increased regulatory and governance requirements, data sovereignty issues, lack of control, or fear of vendor lock-in.”
- “While cloud DBMS is considered one market, there are seven key use-cases for this technology: traditional transactions, augmented transactions, stream/event processing, operational intelligence, data warehouse, logical data warehouse, and data lake.”
- “Gartner predicts that by 2023, 25% of organizations will embrace a data and analytics solution from a single cloud provider for reduced overall costs.”
- “Data ecosystems will be the next wave of cloud refinement.”
AI and the Future of Work
Presented by Graham Waller, Distinguished VP Analyst, Gartner
AI is already transforming tasks and changing the makeup of job roles and responsibilities. This presentation delved into the impact of AI on jobs and explored practical steps that organizations must take as AI maturity increases to ensure the enterprise has the skills it needs in the future.
- “There are both concerns and myths that proliferate about AI’s impact on the future of work. On one hand, people fear that automation is dehumanizing and AI will steal humans’ jobs. Others, however, may think that AI helps people work smarter not harder, reduces drudgery, and promotes work-life balance.”
- “Gartner believes that multiple future of work scenarios could emerge by 2035 based on how automation and peoples’ attitudes towards AI evolve.”
- “In 2022, the reality is that we have a proliferation of ‘mini-bots' that can help to augment workers. As this trend continues, the half-life of tasks and human skills will shorten, while new skills will continue to emerge.”
- “Occupations where AI-protective skills are of higher importance, such as marketing and legal operations, are less likely to be impacted by AI than roles like accountants or mechanics.”
- “Unlock AI’s potential to transform work by shifting mindsets from jobs and occupations to skills and tasks. Then, create marketplaces to match work to skills and help people grow.”
- “Activate your human-centric autonomous business flywheel to put people first while harnessing AI to transform work. Liberate employees from drudgery and continuously educate with AI augmented learning.”
The Future of Data Science and Machine Learning: Critical Trends You Can't Ignore
Presented by Svetlana Sicular, VP Analyst at Gartner
The data science and machine learning (DSML) market has a relentless pace of innovation. This session examined some of the key trends influencing the DSML landscape, including transformers, Edge AI, AI engineering, MLOps, synthetic data, and responsible AI tools.
- Set the right expectations for the near-term and long-term future of DSML.
- Have your long-term vision: enabling outcome-focused DSML. In the near-term, be intentional: aim for an MVP, skip a POC. This will get you closer to the valuable outcomes.
- Do not overcomplicate solutions to deliver value. Use the best technique for a job to achieve efficiency, speed, and simplicity. Leap to the state-of-the-art ML with pretrained models when it gives you a competitive differentiation.
- Manage risk, support accountability and increase adoption with Responsible AI tools.
- Optimize for bottleneck skills: enable professional data scientists to do the highest value work.
- Attract talent with new career opportunities: involve engineers in designing AI solutions, operational efficiencies, and implementation strategies
- Build a data foundation for AI. Approach data-centric DSML as an ongoing, high-priority investment.