Data Analytics

Systematically imagine plausible futures based on data and trends
Gavin Jones
July 10, 2016

Big Data is a term used to describe the collection, storing and processing of huge volumes of data. These data sources come from internal data sources such as purchasing or case management systems and external data sources from citizens, suppliers, partners and social networks to name a few. Using complex algorithms, the structured and unstructured data can be analyzed to provide details on the past, predict the future, and understand outcomes before they happen.

There are three main types of analytics that organizations are using to leverage their big data and inform decision-making:

  • Descriptive Analytics: Uses data aggregation and data mining of big data sets to provide insights into the past to help influence future outcomes. This is the most common type of analytics which uses basic algorithms to answer the question, what has happened in the past? E.g. analyze departmental spend.
  • Predictive Analytics: Uses past data to predict and model future outcomes. It provides organizations actionable insights based on past data and probabilities. This has been used to predict behaviours based on trends in activity, forecast case loads based on predicting what the services clients will require etc. Predictive analytics helps answer the question, what could happen in the future? E.g. capabilities to predict to the day when a certain country might have a significant public protest or the growth of a political movement.
  • Prescriptive Analytics: Uses data to not only predict future outcomes but predicts multiple possible futures and allows organizations to assess a number of outcomes based on different actions. This is done through business rules, machine learning and computational modeling. Prescriptive analytics helps answer the question, what should we do? E.g based on patient health data, suggest changes in lifestyle to reduce the severity of possible diseases

Advantages

  • Exploit weak signals and/or lead indicators by connecting disparate data sets that amplify, compliment or extrapolate from these early indicators
  • Nurture experimentation and generate enterprise wide insights at scale by allowing data from wide ranging sources to be analyzed and hypotheses to be tested concurrently
  • Obtain results and make decisions faster through automation and algorithmic analysis

Limitations

Although big data analytics can help improve an organization, there are some limitations:

  • Incomplete data: Incomplete data sets are created when the subject that is being analyzed does not have the complete data set regarding one or more relevant variables causing false or unreliable insights. This limitation can be mitigated through diligently assessing the data sources being used to analyze specific variables.
  • Privacy Concerns: Government, business and citizens are becoming more sensitive around the privacy of their data domestically and across borders. Data policy and privacy laws in some instances are outdated which leaves gray areas around what data can be stored and shared.
  • Skills: Organizations don’t necessarily have the skilled data scientists and analysts in-house to be able to manage and derive meaningful insights. 

Policy Opportunity

The value proposition for using big data analytics:

  • Derive data driven insights on how old policies and programs are performing and explore outcomes of new policies and programs before they are implemented
  • Bringing together Canadian tech start-ups, business leaders, policymakers and academics to understand how Internet of Things (IoT) and data analytics applications can create real economic value for Canadian businesses and citizens 

Considerations

Big data and analytics has been around for a long time and it is becoming even more important in today’s reality. New products are being introduced to the market daily that are collecting massive amounts of data and more organizations are becoming aware of the need to introduce analytics into their everyday business. Some areas to consider when applying analytics are:

  • Understand where the data sources are coming from
  • Determine what is the correct type of analytics for the data
  • Have the correct workforce working with the analytics

Government of Canada

  • National Research Council Canada: hosts a data analytics centre that offers data cleaning, analysis, modeling and prediction
  • Health Canada: exploring the use of big data to enhance its surveillance of diseases and air quality
  • Justice Canada: Using predictive models to manage legal risk
  • Innovation, Science and Economic Development: Using big data analytics to improve client experience on the Canada Business Network Website
  • Employment and Social Development Canada: Assessing the EI programs labour market impacts and outcomes for various demographic groups
  • Canada Revenue Agency: Using predictive analytics to rapidly predict non-compliance
  • FINTRAC: Using analytical modernization to connect data from a variety of sources to detect money laundering and terrorist financing
  • Open.Canada Initiative: Government of Canada is working with the national and international open government community to create greater transparency, accountability and drive innovation and opportunity

Best in Class

  • Global Affairs Canada’s has made it a priority to enhance its analysis capabilities and management of procurement within the Department. GAC engaged Deloitte to analyze its departmental spend and procurement data to gain deeper insight into spending trends in order to identify potential areas for cost savings.
  • Anthem Inc has been exploring new ways of using analytics to transform how it interacts with their members and with health care providers across the country. They have been piloting several differentiating analytics capabilities applied to medical, claim, customer service, and other master data, which have already delivered insights into members’ behaviors and attitudes.
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