Businesses use big data analytics and data science to make better decisions about customers, prices, demand, operations, risk and products. The practical starting point is not a model or a large dataset: it is a decision someone needs to make. Analytics creates value when useful evidence reaches the people or systems responsible for acting on it, and the effect can be measured.
What can businesses use data science for?
Common use cases fall into four groups: growing revenue and improving customer experience, making operations and supply chains more efficient, managing risk and financial decisions, and creating data-enabled products or services. The same analytical method can serve different goals; its value depends on the decision, the data available and what the business does with the result.
| Business goal | Decision to improve | Data and analysis | Action and possible measure |
|---|---|---|---|
| Customer growth | Which customers to reach, what to offer, or which product to recommend | Transactions, behavior, customer attributes and preferences; segmentation, recommendation or response analysis | Tailor communications, offers or recommendations; measure response, conversion, retention or satisfaction |
| Operations and supply chain | How much to stock, where bottlenecks are, or when to service equipment | Orders, inventory, shipment, production and equipment-condition data; forecasting, optimization or anomaly analysis | Adjust purchasing, schedules, routes or maintenance; measure availability, downtime, throughput, defects or cost |
| Risk and finance | Which activity warrants review, how credit risk should be assessed, or how cash needs may change | Transactions, repayment and relevant financial records; anomaly detection, risk analysis or forecasting | Investigate flagged activity, inform a risk decision or revise plans; measure losses, review quality, forecast accuracy or cash performance |
| Data-enabled offerings | Whether data or analysis can improve a product or support a new service | Product, customer and operational data; diagnostic analysis or analytics services | Improve an existing offering or develop a data-related one; measure customer value, adoption and sustainable business results |
How can analytics grow revenue and improve customer experience?
Segment customers and tailor marketing
Businesses can combine purchase history, behavior, geography and other customer attributes to identify groups with different needs or likely responses. Teams can use those segments to tailor communications instead of sending the same message to everyone. The decision is which audience to contact and with what offer; a useful evaluation compares response and customer outcomes with an appropriate baseline, rather than treating the existence of segments as a result.
IBM Think’s article, published November 6, 2025, describes European fuel retailer MOL using loyalty transactions to create product-purchase microsegments. IBM reports that personalized communications produced returns three times higher than general communications and customer-satisfaction levels 20% higher than competitors. These are reported results for that case, not expected returns for another company; IBM does not date the implementation in the article.
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Set prices, promotions and retention actions
Pricing analysis can use demand, competitor prices and customer preferences to inform price changes. Promotion optimization, cross-selling, upselling and churn prevention are related applications: each supports a different decision, such as which promotion to run or which customer may need a retention offer. There is no universal pricing formula established by the cited sources. Businesses need to apply their own commercial rules and customer context, then assess outcomes such as margin, sales, retention and customer response.
Recommend products and inform product development
Recommendation systems use past behavior to help select what a customer may want next. IBM describes Netflix using viewing habits to personalize recommendations. For physical products, customer feedback, diagnostics and usage or telematics data can help product teams identify design or performance improvements; IBM cites Honda’s use of vehicle and driver data in engineering. These examples illustrate possible applications, but the cited case descriptions do not independently establish the full business effect of either program.
How can analytics improve business operations?
Forecast demand and manage inventory
Demand forecasts help organizations decide what to order, where to position inventory and how to prepare capacity. Gartner describes forecasting incoming product orders together with optimization so organizations can respond proactively to changing supply-chain demand, including when historical records are incomplete or dirty. A forecast is useful only if it arrives in time for a purchasing, allocation or production decision; its quality should be assessed alongside the operational consequences of over- and under-estimating demand.
Schedule predictive maintenance
Predictive maintenance uses equipment condition and operating data to estimate failure risk. Teams can then prioritize inspection or maintenance before a likely breakdown rather than relying only on fixed schedules or waiting for failure. OECD reports, attributing the figures to Dilda et al. (2017), that predictive maintenance typically reduces machine downtime by 30%–50% and increases machine life by 20%–40%. These are reported general estimates, not a guaranteed outcome for a particular asset or company; results depend on equipment, data and implementation.
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Predictive analysis or computer vision can flag defects and inefficiencies earlier in a production process, giving quality or operations teams a chance to intervene. IBM Think reports that Frito-Lay used computer vision to assess potatoes and achieved savings of more than USD 300,000. IBM’s article does not state when the implementation occurred, so the figure should be read as a company result reported by IBM, not as a current or typical saving.
Optimize warehouse and logistics work
Inventory, shipment and route data can help identify delays, bottlenecks or avoidable movement in distribution operations. IBM Think describes truck-parts distributor FleetPride using data mining and predictive analytics in warehouse and shipping operations; IBM reports doubled productivity and reduced shipping costs. The article does not give a percentage reduction in shipping costs, so none can be inferred from the case.
How can analytics help detect fraud and manage risk?
Prioritize suspicious transactions for review
Anomaly detection and other pattern analysis can surface transactions that differ from expected activity, helping teams decide what to investigate or interrupt. A flag is a reason to assess an event, not proof that it is fraud. Useful measurement includes how effectively the process finds actionable cases, how quickly teams respond and the burden of false alarms; the appropriate balance depends on the cost of missed and incorrectly flagged activity.
Assess credit and business risk carefully
Credit analysis may combine traditional repayment records with other relevant information, such as income, rent, utilities or account-transaction histories. IBM describes this broader-data approach as one possible way to assess creditworthiness. Broader coverage does not automatically make a decision fairer or more accurate: data availability, privacy, fairness and applicable law all matter. The sources cited here do not provide jurisdiction-specific legal advice, so organizations need to assess relevant obligations for the places where they operate.
Support finance and workforce planning
Analytics can improve demand forecasts, payables performance and cash forecasts, as well as inform workforce decisions such as performance management and retention. McKinsey describes those as priorities in an example involving a global agrochemical company. They are the priorities of that reported organization, not a universal ranking for finance or human-resources teams.
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Can data analytics create new products or business models?
Some businesses use data to improve products and internal processes; others develop data-related products, sell or license data, or provide insights and analytics as a service. McKinsey distinguishes these new business models from top-line customer use cases and bottom-line internal process improvements. OECD also discusses selling or licensing data, developing data-related products and using data to improve products and production.
These paths require a customer need and a viable way to deliver value, as well as attention to data rights and quality. Data is not automatically monetizable simply because it has been collected. A business should establish what customers would value and whether the organization can provide it responsibly before treating a new data offering as a commercial opportunity.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should a business choose which use case to pursue?
Compare candidate projects against the decisions they improve, the effort needed to make them operational and the risks of acting on their outputs. McKinsey frames prioritization around strategic questions, expected impact and barriers such as poor data, dependencies and privacy. Use the following checks to distinguish a promising decision problem from an analytics project with no clear route to action:
- Strategic relevance: Which business goal or recurring decision does the use case support?
- Expected impact: What outcome could change, and how will the organization measure it against a baseline?
- Data readiness: Are the needed data sufficiently available, accurate, fresh and integrated? What work is required to address gaps?
- Timing: How quickly must an insight arrive for the responsible team or system to act?
- Error cost and risk: What happens if a forecast, score or alert is wrong, and who checks consequential decisions?
- Privacy and governance: Are the data use, access, retention and intended decisions appropriate under applicable rules and internal policy?
- Operational ownership: Is there a team with authority and capacity to take the recommended action?
- Implementation and measurement: What dependencies, skills and process changes are needed, and how will performance be monitored after launch?
What is the difference between reporting, prediction and prescriptive analytics?
Descriptive reporting summarizes what has happened or what is happening. Predictive analysis estimates what may happen, such as future demand or equipment failure risk. Prescriptive analysis goes further by comparing possible actions or optimizing a decision. The categories are useful to distinguish because a prediction does not itself make or carry out the business decision: a team, system or defined operating process still needs to decide what to do.
Gartner describes the role of data and analytics as equipping businesses, employees and leaders to make better decisions and improve decision outcomes. Its examples pair forecasting or simulation with defined actions or optimization. That is a practical standard for evaluating an analytics proposal: identify the decision owner and response process, not only the model output.
What does “big data” add to a business analytics project?
“Big data” does not mean every analytics project. IBM describes its characteristics through volume, velocity, variety, veracity and value: the scale of data, the speed at which it arrives, the kinds and formats involved, its reliability, and the usefulness an organization can derive from it. Which characteristics matter depends on the use case. A project may need fast event data, diverse records or simply dependable information; scale alone does not establish business value.
Data quality, freshness, integration, governance, privacy, skills and adoption are part of implementation, not finishing touches. A technically capable model cannot compensate for data that does not represent the decision, a process that cannot use the output or a team that has no clear responsibility to act.
How should businesses interpret published results?
Published figures can help explain what an application looks like, but they are not promises of return. OECD cites Müller, Fay and vom Brocke (2018) for an association between adoption of big-data-related assets and an average 3%–7% improvement in firm productivity. An association does not establish that an analytics project caused the improvement. The predictive-maintenance estimates cited above are attributed by OECD to Dilda et al. (2017), while MOL, Frito-Lay and FleetPride results are company examples reported by IBM Think in its November 6, 2025 article, which does not date those implementations.
These results come from different kinds of evidence and do not share a single measurement method. Do not add them together or treat them as a comparable ROI forecast. For a company’s own use case, define the outcome and baseline before deployment, then assess results in the operating context where the analysis is used.
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