Data Science Analytics

Data Science Analytics

data-science-analyticsAt ionBuzz, we have the Data Science Analytics experience necessary for predictive analysis, scientific modeling, and optimization. We offer a variety of analytic engagements across a number of verticals. Our highly-trained data scientists bring their years of analysis and consulting expertise to a wide selection of industries and verticals. We have experience in the medical, financial services, manufacturing, telecommunications, agriculture, utilities, transportation/logistics, and retail segments of the global marketplace.

As a premier analytics consultant, we offer a variety of service engagements including exploratory assessments all the way through to large-scale implementations. Through these engagements, we can aid you in developing your data science capability and conduct an analysis of your industry’s data landscape. We can also help upper-level management in their analysis of strategic decisions about data mining and analysis. Our experienced data scientists enjoy graduate level applied-science credentials from some of the world’s most prestigious universities and have worked with some of the most influential businesses around the world.

Data Science Services

The first step in conducting a data science projects involves conducting an assessment. This assessment is aimed at determining financial as well as hard-to-quantify benefits including customer goodwill. This is not a short process; it that can take several months and the input of several skilled advisors to execute.

The Anatomy of a Data Science Project

A data science project not only constructs but also operationalizes a scientific model. That model in turn can help in optimizing a set of business decisions with the specific goal of identifying the business case realized from the assessment. A data science project is comprised of the following components:

  • data sampling
  • variable selection
  • development of the scientific model

Also, when a need exists for either the development of a decision support system (DSS) or decision automation system (DAS), we deploy the following activities as well including:

  • data architecture
  • ETL development
  • solution development
  • simulation development
  • verification and validation
  • deployment
  • enhancement

Decision Analysis

Decision analysis involves modeling a strategic or tactical decision that requires either substantial investment or has potentially far-reaching consequences. In this case, the development of a decision model can help the decision maker understand the alternatives, uncertainties, and possible outcomes, all in depth. It can be of assistance to the business owner in making rational business decisions, within certain risk perimeters. It can also be an ideal way of identifying uncertainties that a business owner should seek to reduce prior to making decisions and provides insight into doing so as well.

Benefits of Decision Analysis

Decision analysis can aid the decision maker in making more confident decisions on key matters. Not only can it provide them with the confidence that they have thoroughly explored the different facets of a particular issue, but it is a practical option for companies looking to maximize their time and money as well. This can be particularly important in the eyes of stakeholders.

The Anatomy of Decision Analysis

Data analysis can help you make many business decisions. These include:

  • whether to enter a given market
  • whether to acquire a business
  • where to locate a facility

Decision analysis can be effective as well in discovering areas of decision making that are defined by unacceptably high levels of uncertainty. In these cases, uncertainties such as this can be reduced through a value of information (VoI) analysis. VoI analysis is particularly effective at reducing uncertainty affecting a decision’s outcome and can be effective in shedding light on methods for doing so.

Decision analysis is also useful for analyzing a decision maker’s risk tolerance as well through sensitivity analysis, perceived likelihoods, and possible outcomes. Sensitivity analysis can explore whether (as well as how) optimal decision changes with variations in risk tolerance.

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