Key Takeaways

• “The last year has been incredibly challenging but our organizations have recognized that when so many things are changing so rapidly, they need to invest in people and systems that will help make sense of that change, and respond to it.”

• “We need to do the things the CEO cares about, like helping enable a transformation of our business model, identify the changes sweeping our industry before anyone else, optimize the way we manage our resources, and figure out new ways to connect employees, customers, and other stakeholders.”

• “Real change means changing people and processes, not just technology.”

• “Take the time to identify the leaders most willing to change. You can inspire them to move from the old ways of doing things and toward a future where data and analytics enable new success.”

• “We live in a world of necessary and continuous innovation, so we need technology and processes that are designed to help us keep up with change.”

• “If we want our colleagues to use data and analysis, then we need to focus on the point where they do so: the decision. Data without a decision is a distraction.”

• “We have a profound opportunity to extend the influence of data and analytics by embedding analytics in every decision.”

Learn more in the Gartner press release, “Gartner Highlights Three Actions for Data & Analytics Leaders to Succeed in a Changing World.”

Key Takeaways

• “The pandemic accelerated digital agendas and advanced widespread adoption of data and analytics. This enabled organizations to make data-driven decisions which became a vital capability for survival.”

• “Adaptation and agility emerged as major traits for dealing with the uncertainty.”

• “Advanced data and analytics technologies, such digital twins, graph analytics, augmented analytics, and smart AI, became more widely used for the first time for some organizations.”

• “The availability of the right data at the right time at the right quality suddenly became critical.”

• “As digital transformations continue, consider how your organization is to evolve and be proactive at the table as a leader.”

• “Managing risks also means taking risks and re-engineering how decisions are made with data.”

Learn how to use data & analytics to re-engineer decision making in the free Gartner e-book “The Future of Decisions.”

Key Takeaways

• “At its core, data science is a model creation cycle. Data science operations are designed to build and deploy models.”

• “Everything starts with the business. Business processes generate data, that data is collected and stored in some place where it can be explored, adjusted and brought into an environment for analysis and model creations.”

• “Once data is explored, projects are proposed, prototypes are built, ML algorithms are brought into play, models are then trained and tested into a working prototype that is consumed. That consumption should influence action, which should influence business results that then create new data.”

• Four main roles participate in the data science cycle:

  • Domain Expert: They spend most of their time on building models, training and adjusting models that are in production based on feedback.

  • Data Engineer: The role is an essential part of a model data science team. “For every expert data scientist, D&A leaders want to have one to three data engineers, depending on their data engineering needs.”

  • Data Scientist: This role should focus on data exploration, problem prioritization and prototypes. “They should also heavily collaborate with domain experts such as business analysts and mid-level executives.”

  • Developer/ML Specialist: “They focus on delivery. They don’t necessarily build models but rather manage them as assets. They focus on ModelOps and DevOps.” This role is as essential as data scientists especially if custom-trained models and building your own solutions is not a priority at the beginning.