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Big data transforms a business only when useful information improves a decision, process, customer experience, or business model—and people can act on the insight. Collecting more data or buying analytics technology is not enough. The practical question is which business outcome should change, what evidence can support that change, and how to put the resulting insight into the workflow.
What big data can change in a business
Big data generally refers to large, varied, and often fast-moving data that can be difficult to manage or analyze with traditional approaches. Its value is not the volume itself. It comes from using data to make a specific decision better, improve an operation, serve people more effectively, or create a new source of value. The World Economic Forum’s 2020 report describes understanding how to leverage data as important to business success, while emphasizing the implications of how data is used (World Economic Forum, A New Paradigm for Business of Data).
These are opportunities, not guaranteed results. In a 2019 NIST framework volume on adoption and modernization, NIST observed that organizations captured value unevenly; its account described healthcare and manufacturing as less successful than logistics and retail. That is a historical observation in the framework, not a current ranking of industries (NIST, Big Data Interoperability Framework: Volume 9, Adoption and Modernization).
Four ways data can create business value
Improve decisions and operations
Analytics can help a company identify sales prospects, set or adjust prices, target campaigns, forecast maintenance needs, plan supply chains, manage assets, or allocate staff and capital. The relevant use depends on the business decision, the available data, and whether someone can act on the result. McKinsey’s 2017 survey reported that respondents saw changes in sales and marketing, research and development, supply-chain management, capital-asset management, and workforce management associated with data and analytics. These are respondent-reported findings, not proof that analytics alone caused the changes (McKinsey, 2017 Global Survey on data and analytics).
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Improve customer and stakeholder experiences
Data may help make product recommendations more relevant, improve customer service, or identify where a channel is difficult to use. A World Economic Forum article describes a 2016 example from one Asian telecommunications company: after staff were encouraged to use analytics-based product recommendations, reported conversion rates increased about 15 percent for in-store sales and about 13 percent for inbound telesales; some outbound tests reportedly reached as high as a two-fold increase. Those figures describe one company example and are not typical results or a promise of return (World Economic Forum, 2016).
Personalization is not automatically beneficial. The same data-driven decision can affect people differently, so companies should consider privacy, fairness, and stakeholder impact alongside the intended service improvement.
Create products, services, or business models
A business may add data-enabled services to an existing offering, develop a data-based product, make a data utility available to customers, or partner with other organizations to create a shared data pool. These are strategic choices: collecting information does not automatically make it suitable or appropriate to sell. In McKinsey’s 2017 survey, 41 percent of respondents said their companies had begun monetizing data, and a majority of that group said they had begun within the preceding two years. The survey describes reported activity; it does not show that monetization caused company performance (McKinsey, 2017).
Change competitive dynamics
Analytics-enabled rivals and data-focused entrants can change how companies compete. In the same 2017 McKinsey survey, 70 percent of surveyed executives said data and analytics had caused at least moderate change in their industries’ competitive landscapes in recent years. This is executives’ reported assessment from that survey, not a current measure for every industry or evidence that analytics alone produced the changes.
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How to turn an analytics idea into an operating change
A sound implementation starts with the decision or process, not a technology purchase. The sequence below combines business-case-first guidance from the World Economic Forum with NIST’s adoption focus and McKinsey’s emphasis on integrating analytics into workflows.
- Name the decision or process. Be specific: for example, which maintenance action to schedule, which sales lead to prioritize, or where a supply-chain bottleneck needs attention.
- Define the intended result and baseline. Decide how improvement will be measured before introducing a new model or dashboard. A measure might concern service levels, conversion, downtime, cost, or another outcome relevant to the use case.
- Work backward to the needed insight and data. Identify what information could change the decision, where it comes from, who controls it, and whether it is sufficiently complete, current, and reliable.
- Check readiness and risk. Assess access, quality, ownership, maintenance, sensitive information, and the potential effect of the decision on customers, employees, or other stakeholders.
- Choose technology to fit the use case. Consider how a solution fits existing systems, its cost and upkeep, and whether it can deliver insight at the point where a decision is made.
- Bring business and technical expertise together. People who understand the workflow need to work with people who can manage data and analytics; the resulting recommendation must make sense to the staff expected to use it.
- Embed the insight and learn from use. Put recommendations into the relevant system, task, or decision process. Monitor whether people can act on them and whether the outcome changes, then adjust the approach where necessary.
Why analytics programs fail to produce value
The project is technology-led instead of business-led
Deploying a platform or building a model does not establish which decision should change. The World Economic Forum recommends starting from business needs and notes that those needs vary by company and industry. Its telecommunications examples include customer value, channel experience, network deployment, and predictive maintenance (World Economic Forum, 2016).
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Data is inaccessible, unreliable, or overwhelming
Large datasets can be difficult to handle, and more information does not necessarily produce better decisions. NIST notes that big data can overwhelm traditional technical approaches and that analytics advances may lag behind data growth (NIST, Big Data Interoperability Framework: Volume 9, Big Data and Analytics). A useful initiative therefore depends on data that is fit for the question, not simply data that is abundant.
Governance and accountability are unclear
Teams need to know who owns important data, who maintains its quality, who may access it, and how sensitive or mission-critical information is handled. McKinsey’s guidance on scaling analytics describes data strategies that use domain models, ownership, governance, and different levels of data treatment (McKinsey, scaling analytics infrastructure). These organizational practices do not replace legal advice tailored to the jurisdictions where a business operates.
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Employees cannot or do not act on the insight
An insight has little operational value if it arrives too late, is hard to interpret, or does not fit the way work gets done. The World Economic Forum recommends pilots, early wins, credible champions, and showing staff how recommendations can help their work. McKinsey likewise describes workflow integration and frontline decision-making as the analytics “last mile” where value is realized (World Economic Forum, 2016; McKinsey).
Organizational readiness can be as important as the model or platform. In McKinsey’s 2017 survey, 61 percent of respondents who recognized effects on core business practices said their companies had not responded or had responded only ad hoc rather than adopting a comprehensive, long-term analytics strategy. In that survey, respondents at analytics leaders were more likely than those at laggards to report a hybrid center-of-excellence-led model—45 percent versus 13 percent. These are dated, respondent-reported comparisons, not prescriptions or current benchmarks (McKinsey, 2017).
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to choose which use case to pursue
There is no universal ranking of big-data projects. Compare candidates using the decision they are meant to improve and the practical conditions for acting on the result.
- Business outcome: Is the desired result clear, and can the organization define a baseline and a meaningful measure?
- People and process: Which teams or stakeholders are affected, and what would need to change in their work?
- Data readiness: Is the necessary data available, sufficiently reliable, and governed by clear ownership?
- Workflow fit: Can the insight reach an employee or system in time to affect the decision?
- Cost and capability: Does the organization have the technical fit, expertise, and capacity to operate the solution?
- Stakeholder impact: Could the use case create privacy, fairness, exclusion, or other harms that must be prevented or addressed?
Business value should include the effects on people
Data can create value for a company while imposing costs on customers, workers, or communities. The World Economic Forum’s 2020 report warns that data use can contribute to exclusion and unequal concentration of power and wealth (World Economic Forum, 2020). Responsible value creation therefore means considering who benefits, who may be disadvantaged, and what safeguards or alternatives belong in the design of a data-driven product or decision.
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