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August 5, 2021

Gartner Data & Analytics Summit India: Day 2 Highlights

We are bringing you news and highlights from the Gartner Data & Analytics Summit, taking place this week virtually in India. Below is a collection of the key announcements, and insights coming out of the conference. You can read highlights from Day 1 here.

On Day 2 from the conference, we are sharing how to use data to boost machine learning, how to optimize data quality and we bust myths about artificial intelligence. 

How to Optimize Data and Analytics Value: The New Strategic Imperative

Presented by Rita Sallam, Distinguished VP Analyst, Gartner

Data and analytics is a strategic business function that fuels digital acceleration. Yet, most organizations don’t have a systematic way to assess, optimize and articulate data and analytics value. In this session, Rita Sallam, Distinguished VP Analyst at Gartner, highlighted how CDOs, CIOs, and data and analytics leaders across the organization can optimize business impact and align to key business initiatives.

Key Takeaways

  • “We know from our chief data officer research and surveys that data and analytics leaders that are involved in strategy development are able to deliver far more business value than those who aren’t.”

  • “Data and analytics leaders struggle to optimize business value, and there’s often a lack of alignment to mission-critical priorities.”

  • “You as data and analytics leaders need to function as your organization’s ‘chief value officers.’”

  • “The ROAR (Risk, Opportunity, Appetite, and Return) Model gives you a systematic way to score both benefits and risks for each initiative and then think about the combination of those initiatives in terms of an optimal portfolio.”

  • “While we have models that can help you think through and prioritize, it’s what goes behind those models that’s important. Thinking trumps doing.”

  • “Optimizing value from data and analytics is a continuous process. It’s not just a once a year activity, it’s not just a quarterly activity — you need to think about it as central to everything else that you manage.”

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

Boosting Machine Learning With Better Training Data

Presented by Farhan Choudhary, Principal Analyst, Gartner

The lack of training data is one of the biggest stumbling blocks for machine learning projects. In this session, Farhan Choudhary, Principal Analyst at Gartner, discussed techniques to facilitate the boosting of the quantity and quality of available training data.

Key Takeaways

  • “Poor training data is more common than anticipated. Good quality data is a game changer for machine learning applications.” 
  • “Many projects fail to make it to production due to insufficient or conflicted real-world training data. Organizations can use a variety of techniques to tackle the challenge of low availability of training data.” 

  • Technique #1 - Expand your data collection strategy. The data which is currently being ignored or dark data can be the missing piece of what your machine learning application needs to know. 

  • Technique #2 - Spend more time on data preprocessing. For example, removing outliers, finding missing variables, oversampling, etc. Along with these, many data science platforms contain tools and features to support this preprocessing. 

  • Technique #3 - Data sharing. Create national or international alliances that collect data together and use federated learning approaches so that data can be shared amongst the enterprises without compromising the data privacy. 

  • Technique #4 - Crowdsourcing. Cautiously crowdsource data not from professional data providers, but from a pool of internet users. 

  • Technique #5 - Pool data from external data sets. While the availability of data may be easier, D&A leaders need to ensure that data is validated, as external data can be full of bias.

Myths and Pitfalls of Artificial Intelligence and How to Navigate Them

Presented by Alexander Linden, VP Analyst, Gartner

Even as enterprise artificial intelligence (AI) maturity grows, many myths persist about this technology. In this session, Alexander Linden, VP Analyst at Gartner, discussed the most common myths and pitfalls facing AI and machine learning experts.

Key Takeaways

  • Myth #1: AI capabilities surpass human capabilities. “The deception is that they allowed the computer top five accuracy metrics. This means that the computer was allowed to make five guesses, and only one of those guesses has to be correct for the system to score ‘correct.’”

  • Myth #2: AI is disrupting industries. “When you apply AI it has so many use cases, which all will result in better cost savings, customer satisfaction, downtime reduction, risk reduction and more, but all these things that we’re going to see be better don’t necessarily translate into huge disruption.”

  • Myth #3: AI is about intelligence. “The systems that we create don’t understand much, they only react.”

  • Myth #4: AI can do anything. “The spectrum of problems that AI can tackle is surprising, but we have to say that AI is a point solution. A solution for fraud detection is not going to drive cars.”

  • Myth #5: AI will replace human intelligence. “With our flexibility to learn things, to understand things, and to be super fast and adaptive, it will take a long while before AI will replace human intelligence.”

  • Myth #6: AI can learn on its own. “If you look at the whole lifecycle of AI, only the advanced analytics is fully automated.”

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