The volume of data generated and stored in modern times can be calculated in zettabytes, where one zettabyte equals a staggering 1021 bytes. Organizations capable of extracting and analyzing this influx of information can uncover valuable trends and insights. From measuring performance to projecting consumer behavior to identifying patterns in marketing, operations and supply chain management, businesses can do a lot with data.
However, collecting and disseminating Big Data — large quantities of structured and unstructured data — is not easy. Significant challenges impede organizations’ efforts to leverage data strategically. Some are strictly technical. For example, how can so much data be gathered and organized efficiently? Others are more nuanced. How do organizations protect personal and company data from theft and hacking? And some are ethical in nature. What are appropriate limits on data collection and usage? And what privacy rights can data generators reasonably expect?
These questions seldom have simple answers. People generate data passively as well as actively and may not understand how much they are sharing. Data collected by household appliances, vehicles, Internet of Things (IoT) devices and wearables reveal what we’re doing and when and where we’re doing it. The growing concerns about Big Data’s capacity to compromise privacy are valid, but both individuals and organizations benefit from data analysis. Business intelligence helps companies operate more efficiently, scale more quickly and respond to consumer demand with more agility. Advanced analytics make it possible for services such as Netflix to offer customized recommendations and retailers such as Target to send timed and targeted discounts.
Using data responsibly is a complex balancing act that involves weighing stakeholder demand against consumer rights. Companies that want to collect, analyze and leverage data ethically look to skilled Big Data professionals for help. Demand for analysts and business intelligence experts who understand the nuances of responsible data use will likely grow as more organizations roll out corporate data programs. You can prepare for that eventuality now by learning about the business implications of Big Data, the technology that makes data useful and ethical considerations in a changing Big Data landscape in a program such as the online Master of Science in Business Intelligence and Analytics (MSBI&A) at Stevens Institute of Technology’s School of Business.
WHAT DO RESPONSIBLE ORGANIZATIONS DO WITH BIG DATA?
Many organizations focus on the competitive advantage of Big Data itself, overlooking the fact that ethical data use can confer an additional advantage. Companies that use data responsibly stay compliant with rapidly changing data privacy rules and regulations and maintain higher levels of customer loyalty and trust. They may also be less likely to fall victim to data risks such as breaches and hacks because they treat data with great care.
Responsibility in a fraught Big Data landscape is not about avoiding analytics. Rather, responsible organizations consider consent, ownership, transparency and privacy when they do the following:
GATHER PERFORMANCE METRICS
Monitoring performance is critical to optimizing business operations and data-driven decision-making. Collecting and analyzing this data requires skilled data analysts, like those who complete Steven’s MSBI&A program.
Companies can meet the challenges of efficient data gathering by deploying standardized processes. For example, a uniform project management or ticketing system can help companies track and quantify completed work, while cloud storage services facilitate document sharing and collaboration for faster work completion. Mobile-friendly applications that streamline workflow procedures and ease data collection support efficiency by providing workers with more flexibility. By gathering performance metrics, companies can use them to determine which units are meeting or exceeding their KPIs and KSIs and which are not. From there, they can decide how to improve processes and allocate resources efficiently.
Responsible data use involves collecting and studying performance metrics in a transparent way to improve business practices and productivity. Organizations should inform customers what data is collected and how that data will be used. For example, customer data can provide insights that become the basis of a more positive customer experience. In the best-case scenario, customers actively opt into data sharing by giving explicit consent and have the option to limit sharing if they do not want their data used in performance monitoring.
DRIVE STRATEGY WITH DATA
Big Data gives companies a window into group behavior to detect trends in real-time that represent opportunities. For example, Big Data may contain information about which product improvement will have the most significant market impact or which customers are most likely to upgrade so that marketing can be more targeted and less wasteful. Data can also drive supply chain, operational, marketing and financial strategies.
Artificial intelligence (AI) and machine learning are useful in Big Data collection and analysis because they let organizations scrutinize ever-larger datasets with critical procedure automation, speeding results and deepening insights. Big Data and AI professionals can devise multiple potential business scenarios based on past data and emerging trends. AI can even dig deep into those scenarios to propose potentially lucrative responses.
However, automation in the Big Data landscape raises questions about what decisions should be made with AI using customer data, what biases emerge when data analysis is automated, who is accountable for ensuring data accuracy in an age of automation and how to preserve privacy. Responsible organizations approach AI implementation in business intelligence thoughtfully, with an eye toward protecting customer privacy and using data wisely.
INTERACT WITH THE PUBLIC
Responsible companies practice transparency when they leverage Big Data to enhance operations, products and the customer experience. They disclose how they collect and use data in ways customers are likely to see. More importantly, these organizations do not collect and store data with no immediate use.
Given the frequency with which breaches and hacks occur, as well as the financial value of data, consumers are understandably suspicious of anything that might impinge on their privacy or put them at risk. When organizations reassure customers that their data rights are respected, they build consumer trust and can increase their market share in the process.
HOW ORGANIZATIONS CAN USE DATA MORE ETHICALLY
Organizations committed to using Big Data ethically should consider taking the following steps:
- Create a data ethics board to ensure best practices and data governance. Formulating and publishing a corporate ethics statement regarding data privacy can go a long way to easing customer concerns.
- Request consent for data collection and sharing by inviting customers to opt into data collection. As part of the consent request, organizations should include information about how they will use data and what measures they will take to protect information and privacy.
- Commit to anonymizing data by scrubbing Personally Identifiable Information (PII) from any data shared or sold.
- Commit to collecting only that data that is necessary for business purposes. Organizations should not collect or store additional PII or anonymized data unless there is an explicit intended use for that information.
- Acknowledge and obey all relevant international laws and regulations related to data collection and use. Organizations should be familiar with all applicable statutes, such as the EU-US Privacy Shield and General Data Protection Regulation (GDPR), and any industry or region-specific regulations.
LEARN TO MANAGE BIG DATA RESPONSIBLY WITH A STEVENS ONLINE MSBI&A
Students who earn an MSBI&A degree at Stevens Institute of Technology are ready to meet the needs of modern businesses by delivering essential expertise in Big Data analytics and management. Graduates work in various fields, including healthcare, retail, manufacturing, technology, government, startups and more — data science and analytics use cases are everywhere.
Stevens delivers the MSBI&A coursework via 12 classes across a 36-credit curriculum covering Big Data collection and data management, AI, deep learning, data quality, predictive analytics and business intelligence systems. The program format has flexibility built in, so you can continue working and start harnessing the power of Big Data to develop new evidence-based strategies with integrity right away. Classes are held 100% online on a part-time schedule, and most students complete the program within two years.
You will graduate able to:
- Understand the methods underlying multivariate data analysis using R.
- Use data visualization effectively to illustrate findings.
- Use mathematical models and algorithms to analyze risk phenomena and implement risk-aware solutions to improve business processes.
- Design and manage data warehouse, data lakes and business intelligence systems.
- Use supply chain analysis skills to solve real-life problems.
- Leverage data integration to identify appropriate data sources and data technologies for different use cases.
- Understand the utility of AI, machine learning models and natural language processing of business intelligence.
- Draw from resources such as the public cloud and open source code to refine your data analytics methodology.
Earning a graduate degree in business intelligence and analytics positions you to respond proactively to digital transformation and tap into the latest data science and analytics capabilities to give your organization a distinct competitive advantage. The career outlook for MSBI&A graduates is strong. Employers across industries are eager to hire Stevens alumni, as is evidenced by the fact that 93% of program graduates in the class of 2020 accepted job offers just three months after graduation. Many go on to work for notable organizations such as BMW, Exxon, IBM, Goldman Sachs and Lockheed Martin, bringing their understanding of how to responsibly manage Big Data with them.