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6. Using big data in business

The value of US government data to US business decisions 1

  • Federal data are comprehensive, covering the entire US, and, as a result, are useful for:
    • Benchmarking and supplementing businesses’ own data.
    • Comparison across places and over time: federal data spans over long periods and covers all regions.
  • Data generated by a firm only covers its customer base, and may not be representative of the entire US population.
  • The Value of data increases when the firm’s big data is combined with federal data generated by the US government.
  • Broad Assessments of the Value of Public Data to the Business Sector:
    • Two departments: The US Department of Commerce Reports and the National Association for Business Economics (NABE) Surveys.
    • GDIS (Government-Data-Intensive Sectors): sectors that rely heavily on government data in their production processes; and this includes:
      • Investment Analysis Firms.
      • Database Aggregator Firms.
      • Market Research Firms.
      • Benchmarking Firms.
      • And many others.
    • Concrete examples of important uses of government data included the development of models used to:
      • Project defense spending by industry, state, and occupation.
      • Infrastructure investment spending.
      • Industry footprint analysis at the state and regional level.
  • The value of government data is difficult to measure, but it is clearly a substantial strategic asset for the US business sector.
  • Such data are used by a wide range of companies from auto producers to digital platform companies, and for purposes that include production and investment decisions, marketing and inventory management, and long-range strategic planning.

Examples of the usage of government data by businesses

These industries use government data to make decisions:

  • HealthCare:
    • It uses data to decide which services to expand geographically and how many staff to hire based on the projected demand.
  • Agriculture:
    • It uses data to estimate the demand for diesel engines and the projected demand and price of crops.
  • Finance:
    • It uses data to set loan and interest rates.
    • It uses data to Keep up to date with the latest government regulations.
    • Financial services firms include commercial banks, asset management firms, equity brokerages, credit unions, and finance companies.
  • Insurance.
  • Real Estate.
  • Automotive Transportation:
    • It represents 3.7% of the US GDP.
    • It uses data about current and future economic activity, including short- and long-term behavior of GDP, inflation, interest rates, commodities, and exchange rates as their sales are directly affected by these factors.
    • It uses data to make decisions such as:
      • Investment decisions, and plant locations and expansions.
      • Supply chain decisions especially relying on government data about GPS and traffic data.
    • The transportation sector participants included auto manufacturers, auto insurance companies, public transportation organizations, and companies that provide vehicle-sharing and other innovative models.
  • Energy:
    • Firms in the energy sector include crude oil producers, refiners, oil servicing companies, electric utilities, natural gas producers, coal companies, nuclear companies, pipeline producers, and suppliers of energy-related equipment and components such as windmill turbines, solar panels, other renewable energy sources, and battery storage units.
    • The Energy Information Administration (EIA) is an independent statistics and analysis agency within the US Department of Energy, created in 1977 in the aftermath of the first OPEC oil shock.
    • Energy consulting companies depend on government data as a starting point for market analysis.

Big data and business analytics: A research agenda for realizing business value 2

  • It is not sufficient to focus on the resources that are needed to extract meaning from data, but it is necessary to adopt a viewpoint of leveraging data to outperform competition.
  • What makes big data possible and popular:
    • Storage costs have decreased.
    • Processing power has increased.
    • Sensors and other IOT devices have become more prevalent.
    • Maturity of network infrastructure.
  • Big data will provide little value if it is not accurate and people are not able to interpret the decisions.

Video resources 3 4 5

  • Big data is used to 3:
    • Better understand your customers: analyze purchase history to detect what you will likely buy in the future.
    • Improve products and services: analyze customer feedback to suggest improvements.
    • Improve the big data process itself: analyze data flow to detect bottlenecks and improve efficiency.
    • Monetize data: sell data to other companies.
  • How businesses are taking advantage of big data 4:
    • Real-time website personalization: change the look and feel of the website based on user behavior and what you know about them.
    • Detect new possible opportunities: analyze data to predict a future trend.
    • Improve online advertising and reputation management: analyze social media data to detect what people are saying about your company.
    • Bad data detection: bad and outdated data cause 46% of businesses to make bad decisions.
  • Workflows 5:
    • They are task-oriented and require more specific data than processes.
    • Each process is composed of one or more workflows each of which is composed of one or more tasks.
  • Integrating big data in workflows:
    • Identify big data sources.
    • Map big data types to workflow data types.
    • Ensure proper processing speed and storage capacity.
    • Select the best-suited data store for each data type.
    • Modify and create new workflows to accommodate big data.
  • Stages in big data analysis (the utilization part of the big data cycle):
    • Discovery stage: it is the first step. gather info about the data and the problems. generate a high-level description of the problem. It uses scorecards, dashboards, simple visualizations, and statistical analysis.
    • Exploratory stage: aka, pilot stage. It discovers patterns and relationships in the data. It uses data mining, clustering, and association rules. At this stage:
      • Perform ETL (extract, transform, transport, load) operations.
      • Perform quality checks on the data.
      • Develop a data model.
      • Develop a data visualization.
      • Validate the results and finalize the data model.
    • Codifying stage: aka, development stage. It connects the analysis process to the existing systems. At this stage:
      • Build statistical models and machine learning algorithms.
      • Iterate to improve the model.
      • Teach business rules to the model.
    • Integration and Incorporation stage: integrate the results into the business processes itself.

References

  1. Hughes-Cromwick, E., & Coronado, J. (2019). The value of US government data to US business decisions. The Journal of Economic Perspectives, 33(1), 131–146. https://www.jstor.org/stable/26566980 ↩

  2. Mikalef, P., Pappas, I., Krogstie, J., & Pavlou, P. A. (2019). Big data and business analytics: A research agenda for realizing business value. Information and Management 57(1). https://www.researchgate.net/profile/Patrick-Mikalef/publication/337543997_Big_data_and_business_analytics_A_research_agenda_for_realizing_business_value/links/5fd33aaf299bf188d40b431f/Big-data-and-business-analytics-A-research-agenda-for-realizing-business-value.pdf ↩

  3. Marr, B. (2019, March 29). How do you use big data in business​ by Bernard Marr? [Video]. YouTube. https://www.youtube.com/watch?v=-zRJZt12Ii4 ↩↩

  4. Shifu Digital. (2021, August 27). Top 10 ways businesses are taking advantage of big data [Video]. YouTube. https://www.youtube.com/watch?v=EpS3uyYg4vk ↩↩

  5. Wiley Certifications. (2017, October 25). Big Data 101: Integrating big data in organizations [Video]. YouTube. https://www.youtube.com/watch?v=mr5igqPCzkk ↩↩