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Posts Tagged: Business Intelligence
Machine Learning, Trust and Stephen Covey

Machine Learning, Trust and Stephen Covey

Trust is a big deal when it comes to machine learning. “Black box” algorithms, concerns about bias and a sense that data scientists may know everything about the data but nothing about the business all undermine trust in machine learning models. Indeed, building...

Algorithms and Regulations: Tips for Success

Algorithms and Regulations: Tips for Success

Cathy O’Neill wrote an interesting piece on regulating automated decision making recently. I am not going to argue about whether use of algorithms should or should not be regulated because I think it is inevitable that they will be. The question is how companies...

Using Technology to Reduce Operating Costs in Insurance

Using Technology to Reduce Operating Costs in Insurance

In February, we published a blog post on “Using Technology to Add Value in Insurance”. That post, referenced Matt Josefowticz’s article – Technology May be the Answer for Insurers, but What Was the Question?, in which he states that there are only three levers of...

Using Technology to Better Manage Risk in Insurance

Using Technology to Better Manage Risk in Insurance

In February, we published a blog post on “Using Technology to Add Value in Insurance”. In that post, I referenced Matt Josefowticz’s article – Technology May be the Answer for Insurers, but What Was the Question?, in which he states there are only three levers of...

Using Technology to Grow Relationship Value in Insurance

Using Technology to Grow Relationship Value in Insurance

In February, we published a blog post on “Using Technology to Add Value in Insurance.” In that post, I referenced Matt Josefowticz’s recent article – Technology May be the Answer for Insurers, but What Was the Question?, in which he argues that there are only three...

What is an Acceptable Analytic Failure?

What is an Acceptable Analytic Failure?

Many speakers on predictive analytics, machine learning (ML) and AI talk about the need to allow data science teams to fail. Without failure, without a willingness to fail sometimes, it’s very hard to build a successful data science program. This is true and often a barrier for companies that find it hard to accept that not all analytics initiatives succeed.

Harness Data-Driven Decisions with Decision Management

Get out of the reporting quagmire, be explicit about decision making with decision modeling and integrate analytics in BI and operational systems. We're Not There Yet "The activities of analytics teams and the investments made to support them aren't in sync with what...

From BI to Predictive Analytics with Decision Centric Dashboards

Many organizations are keen to improve data-driven decision making with predictive analytics but they are trapped by operational demands for traditional BI reporting and dashboards. They are asking how do they get “there” - better data-driven decisions - from “here” -...

Great Examples of Integrating BI and Data Science

We talk a lot about the power of predictive analytics*. Data-driven decision making is the goal, but to get there, organizations need to learn how to extract actionable information from their data. We also talk a lot about how this is different from traditional...

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