Data and analytics are at the core of the modern organization, but this central role means that your data and analytics strategy must respond to evolving organizational priorities, growing demand for insight from across the organization, changing business ecosystems and emerging technologies. This continual change requires resilience, and the ability to absorb these pressures and emerge stronger. But in an unpredictable and continually changing world, resilience demands creativity and the ability to craft new approaches to both new and old challenges. However, creativity requires the right environment to flourish, and that environment is determined by your culture and created by your leadership.

To achieve broad business impact, data and analytics leaders must extend these investments beyond individual departments and projects to empower everyone in the enterprise and beyond. Only then can you optimize every business moment, action and outcome.

Data and analytics leaders continue to face unprecedented uncertainty and disruption both internal (fear, AI, resistance to change) and external (economic, regulation, industry, technology). The potential for recession looms.

Leading and building resilient and adaptable and creative organizations that can respond to this change and capture opportunity from chaos will be critical to survival. Focused, business-driven leadership cultivating a data-driven culture to find the right organizational model will play pivotal roles. It means tighter collaboration than ever with teams of people across the organization and beyond your borders.

Growing the size and the reach of your data and analytics teams, bringing more of the right skills into those teams, and engaging more roles in a distributed way across the business to be a virtualized part of those teams will be a necessary condition of success. 

Specific investments must be made to create new roles and responsibilities, such as the chief data officer (CDO) to link data and analytics investments with strategic business outcomes and value. Building new skills competencies in data science and machine learning (ML) and AI, data engineering and, importantly, in data literacy for everyone in the organization are new cultural imperatives and organizational success factors in the digital era.

It will be necessary to establish new ways of working and new data-driven approaches that exploit complex and diverse data assets, as well as diverse thinking and teams to spark creativity and innovation.

Investing in data and analytics as a strategic priority aligned to mission-critical organizational and line-of-business priorities and outcomes, and pushed out to all corners of the business, is an imperative. Delivering on most digital business goals and objectives will depend on it.

The size, complexity, the distributed nature of data, the speed of action and the continuous intelligence required by digital business means that rigid and centralized architectures and tools break down. It is pushing the limits of manual approaches to data management, analytics, data science and machine learning and AI.   

Virtually every aspect of data management and analytics content and application development and sharing of insights is incorporating ML and AI techniques to automate or augment manual tasks, analytic processes and human insight to action.

The imperative of building an agile, data-centric architecture that augments every aspect of the business, and responds to constant change, has never been more critical to business survival. 

Intelligent data and analytics capabilities — enabled by more aggressive transition to the cloud —   make emergent and agile data fabrics and explainable, transparent insights possible at scale. This is necessary to meet the new demands and expand adoption.

The path to success requires making the right choices in the face of unprecedented business demands and rapidly changing and complex technology options. We know that a lot of organizations working on data and analytics struggle with balancing between investments that drive innovation and renovating their technology core. 

The very challenges created by digital disruption — too much data — have also created an unprecedented opportunity. Leveraging these new vast amounts of data, when coupled with increasingly powerful processing capabilities enabled by the cloud (for both data management and data science), makes it now possible to train and execute the algorithms at the very large scale necessary to realize the full potential of AI.

We are seeing a growth in the use and application of data science, machine learning and AI across all industries, particularly as AI matures and the democratization of AI accelerates. AI will no longer be for the privileged few. AI-enabled analytics and data management tools (what Gartner calls Augmented Analytics and Augmented Data Management) will empower the many to process the vast amounts of data needed for advanced analytics at scale. This will make it possible for a broader range of skills, such as those of the citizen data scientist and developer data scientists, to create AI-driven insights and embed them in applications used by everyone across the organization. 

AI as a driver of how employees, customers and partners interact with all business applications will feature prominently in this transformation where natural language and conversational interfaces will also open up insights to more people in and beyond the organization.

Augmented and mixed reality, still in its infancy in the enterprise, will also start to play a role in the enterprise, beyond gaming and entertainment, including in data and analytics.

With so much data and insights from diverse sources, data storytelling — part art, part technology-enabled — will be a critical personal and organizational competency necessary to drive optimized actions across the enterprise.

Moreover, the broad distribution of analytics capabilities to everyone in the enterprise is giving less-technical individuals across the business the ability to generate value from data assets and analytics. To enable this growth, we see considerable adoption of capabilities such as agile data preparation, data cataloguing and metadata management technologies, and new adaptive data-governance-related processes. These are enabling content authors using any tool anywhere in the business to do more in creating and using trusted, high-value data. As more and more types of people have access to analytics, a strong data foundation is imperative now more than ever.

Organizations must consider how graph technologies enhance the accuracy of data science and machine learning and AI, and form the foundation of capabilities such as natural language processing as well as complex data modelling enabled by knowledge graphs. This will enable the organization to capture all of the knowledge about what data you have, where it resides, how it’s all related, who uses it, why, when and how — and then using that insight to provide more personalized, automated and properly governed solutions to the business.

New roles such as the data engineer and processes supporting DataOps and user communities are key enablers of this more distributed yet governed approach. 

As machine learning and AI move beyond prototyping and early departmental deployments, success will be measured by the impact of models actually deployed in production — not just by the number of models created. It is the models that are operationalized that are driving measurable business impact.

MLOps and new AI governance processes, which include model and AI operationalization, management and explanability will be critical capabilities to understand and scale the value of these investments and their contribution to business transformation and success. They will also be critical to building the trust necessary to expand adoption and to protecting the organization from regulatory and unintended negative consequences from these emerging approaches.

Data and analytics is no longer a back room IT function focused solely on reporting. It is a core enabler of the digital strategy and transformation of every organization - commercial non-profit and in the public sector.

Along with the rise of digital business, the latest CDO survey shows we’re continuing to see the growth and expansion of the chief data officer role as more and more organizations view their data and analytics initiatives as strategic to the success of their transformation journeys. 

The CDO role is not only focused on data and governance, it’s also about owning analytics and driving how data and analytics can be instrumental in achieving organizations’ vision, goals and aspirations. The CDO office should have responsibility for data and analytics strategy, data, analytics and AI governance, data literacy among the workforce, and establishing a data-driven culture.

Striking a balance between building human centric and AI-augmented organizations and their impact on society is now under the purview of the CDO.

One of the findings from the Gartner CDO survey reveals that two-thirds of CDOs now have responsibility for both data and analytics.

The chief data officer (CDO) is the best role to maximize the data and analytics value in the organization. Not every company needs to adopt the CDO title, but every company does need someone to adopt the tasks of prioritizing or leading its data and analytics strategies.

CDOs most likely to succeed view themselves as champions of change. They’re leading the development of their enterprises’ data and analytics capabilities to innovate and create new data commercialization and monetization opportunities. Those focused solely on the internal, operational benefits of data and analytics tend to be less successful than those who diversify their strategy and also drive top-line and transformational benefits that align with mission critical business priorities.

Successful CDOs tend to be those that have the full support, buy-in and backing of their leadership teams, even to the degree of reporting directly to the CEO. They’re supported with the budget, the people and the resources needed to succeed. This places them in a far stronger position to not only scale data and analytics throughout the enterprise but also to have the headroom and resources needed to innovate and identify new opportunities to commercialize and monetize their data assets.

CDOs and other data and analytics leaders tell us that their biggest challenges relate to culture: Leadership, managing politics, bridging the IT business divide, developing skills, promoting organizational openness to new ways of leveraging data and analytics to support new business and operating models, building shared values and fostering creativity. D&A Leaders need to lead and empower. A focus on culture as it is the biggest challenge and opportunity for success.

AI now holds the promise of being the technology that will transform business, every aspect of our personal lives and the broader society in an even more consequential way than the internet did now decades ago. 

Application-specific AI has now reached a state where you can get real business value from it. It has reached a level of maturity where it’s no longer theoretical, it’s no longer something only for academia or for rocket scientists or even specialist data scientists. Given the advances, most CEOs are desperate not to fall behind.

Leading companies have had early successes at embedding AI in customer-facing and back-office processes and some in making it central to their products and business models. Enterprise application vendors are embedding AI into core processes used by people across most industries every day. The addition of natural language processing technologies as an interface for interacting with all enterprise data and processes will continue to expand the reach and impact of AI.

Given the limited supply of advanced analytics skills, vendors in the data and analytics market are injecting machine learning and AI into their platforms and technologies to reduce data and analytic complexity and expand insights to more people across the enterprise. AI is being leveraged in tools for building analytics content (including analytics and BI platforms and data science and machine learning itself) and in platforms for managing, integrating, cataloguing and storing data.  

These augmented analytics and augmented data management capabilities are transforming how data is integrated and managed, how analytics content is authored and how insights are generated, shared and consumed. AI-enabled innovations are disrupting all of data and analytics. They are leveraging AI to automate tasks once reserved for people with specialized skills, reduce time to insight and bias and improve accuracy over manual, human-only driven approaches. This trend is a key enabler of the democratization of AI.

Establishing a focus on AI with specific leadership and skills and investing to operationalize and scale AI deployments will be key to broader business impact — as will an even greater focus on the data that feeds and trains algorithms. Data and analytics leaders will play a central role in all aspects (technology, culture, people, process, leadership) of AI success.

Success with data and analytics will depend on building an analytics culture and foundation of trust, accountability, governance and security that respects privacy and promotes digital ethics.

D&A leaders are faced with the amplification of multiple truths. This is caused by a lack of a common fact base — this is beyond fake news. This erodes trust and increases uncertainty.

Getting value out of data and treating data responsibly. Personalization and respecting privacy must not be an “either/or” choice. Organizations can and must do both to achieve success. Organizations must achieve more relevant hyperpersonalization that relies more and more data without being creepy. Organizations must leverage advanced analytics techniques and augmented analytics to expand the use of advanced analytics across the enterprise while reducing bias in models and insights and making the results transparent and easy to explain.

How can organizations collect and use as much data as possible while also being able to curate it to the point where we can verify our data in time to better inform the business decisions we make? How can make sure that the algorithms we build or auto generate are ethical and unbiased?

The answer is governance: Resolving these ambiguities requires multiple levels of governance processes. We must be able to trust our data, trust our analytics and trust our algorithms. Trust is always having the ability to provide scrutiny through verification and transparency to better inform the business decisions we make. Trust is about making sure the algorithms we are run our business on are based on diverse data and free from bias. Capabilities such as metadata management, knowledge graphs, and data catalogs, augmented data management, blockchain and explainable AI will form the foundation of an adaptive governance strategy.