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From Data to Decision: How to Automate Operational Decisions

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How governments improve COVID decision making with AI and analytics

Government agencies across the world were embracing digital transformation even before COVID-19 disrupted the planet and forced governments to lean on analytics more than ever to carve out their competitive advantage.

But especially now, informed decision-making is being demanded by citizens across the world in crucial industries like:

  • Healthcare
  • Transportation
  • And ecological management

Access this e-book to learn how government agencies are using analytics, and taking it to new heights with AI and machine learning, in a variety of use cases to take action during COVID-19.

These are also closely related to: "From Data to Decision: How to Automate Operational Decisions"

  • Don't rely on just one model for fraud detection

    The same way technology rapidly progresses for business and recreational use, so it does for fraudsters as well.

    Account takeover fraud in banking is up nearly 300% year over year, web applications fraud is up 200% and fraud of government services and payments is up 30%.

    The only way to protect against fraud is to keep your organization up-to-date with burgeoning technologies, like machine learning, and integrate best practices into your fraud protection models.

    Read this white paper for advice on all the challenges and technologies regarding fraud detection.

  • Big Data in Big Companies



    Research shows big data paying off for big companies


    Tom Davenport of the International Institute for Analytics (IIA) and Jill Dyché of SAS, the leader in business analytics, evaluate how 20 large firms have deployed analytics to generate value from their big data assets.


    Research participants included AIG, Bank of America, Caesars Entertainment, Carolinas Health Care, CIGNA, Dell, Discover, Fidelity, GE,, Schneider National, Sears, T-Mobile, UnitedHealthcare, UPS, Verizon and Wells Fargo.


    The report found that transforming organisations through analytics requires new skills, leadership, organisational structures, technologies and architectures. Most organisations surveyed are augmenting existing analytical staff, adding data scientists with IT capabilities who can manipulate big data technologies. Solid knowledge of data architectures, data quality, and master data management hubs are just the beginning for firms pursuing big data as a long-term differentiator..


    This independent research study was conducted by Tom Davenport and Jill Dyché, and was sponsored by SAS.


    Click on the button to download this research report



    The International Institute for Analytics (IIA) was created to bring a common organizing language and ‘experienced-based’ research to the growing business analytics industry. Its mission is to meet the research and decision-support needs of business and analytics leaders and their teams as the focus on analytics as a competitive differentiator increases.

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