Companies have a multitude of big data sources including internal databases, activity on their web and social media pages, mobile apps, and third-party sources. The growing collection of sensors coming online from the Internet of Things may soon overwhelm other sources.
Most enterprises are only now realizing how deep analysis of these data can benefit their business strategies. They may have developed a few business cases, but do not yet understand the full potential of a big data strategy for their enterprise.
The big data leaders have run pilot programs that taught them how to effectively acquire and analyze big data. These organizations are now reaping the benefits of improved decision-making, greater productivity, cost reductions and deeper comprehension of their markets and customers.
Big Data Strategic Value
Identification of high-value customers is essential for fine-tuning a company’s sales and marketing efforts. Data from any site where customers research, buy and return products or request support are invaluable in discerning patterns of behavior, preferences and satisfaction.
Contextual information such as location, network connectivity, weather, time of day and other transactions before or after a sale provide valuable correlations. Real-time analytics of specific ad campaigns, monetary or non-monetary incentives can be tested with same-day feedback.
Big data analysis is employed to test longer-term tactics and strategies too. Hypotheses can be proposed, experiments put in place and the results analyzed from multiple data pools to verify results. Depending on the degree of granularity, correlations can feed causal analysis to support current decisions or company forecasts. These experiments are also beneficial in judging the usefulness of data from various sources.
Product and Service Development
Big data is being utilized by product development teams for targeting new products that promote deeper customer engagement or meet consumer needs that were not readily apparent. Big data helps better understand the how, why, what and where of customer interactions with the products and the brand.
Such insights can transform a company’s product strategy from a reactive one in which improvements are made to existing products based on customer feedback or competitor’s enhancements to a proactive mindset from which entirely new features or product lines are created.
Additional big data insights can suggest and test the most successful distribution timing, locations and optimizations to the distribution chain in order to reduce the risks of product launch.
Big Data Use Cases
Large healthcare providers are augmenting knowledge from limited pharmaceutical clinical trials with actual usage data from the field that provide further indications of a drug’s benefits and risks. The same principle is being applied to the outcomes of interactions with caregivers such as doctors, physician’s assistants and nurses.
The big data streams from which these insights are gleaned include clinical activity, insurance claims, medical product research and records of patient behavior. These types of analyses generate tremendous benefits for drug makers, medical personnel as well as patients.
Data-centric retailers are tapping into oceans of data looking for clues to customer preferences and behavior. They analyze how customers research products, which products they buy, how transactions are completed, product returns, responses to marketing campaigns and to which sales channels they are paying the most attention. Nearly 60 percent of retailers reported to the Aberdeen Group that their number one priority is improving customer insights via improved data analytics.
Process-based manufacturers employ advanced analytics to improve productivity while cutting costs. They do so by extracting, correlating and visualizing data from their operational systems, production floors and warehouses. This assists in identifying the most significant determinants of process efficiency, which provides a basis for adjustments. Additionally, they are using external data streams to improve product demand forecasting and to evaluate supplier quality and performance.
Big data analytical power is growing exponentially in terms of raw power and sophistication. The largest information-driven enterprises are mining only the tips of the information available to them presently. They are concentrating first to those data that pertain to businesses processes, then to customer and market analysis. However, new uses for the growing volumes of data are being invented continuously.
It is no longer too early to tell what the impact of big data will be on companies’ efforts to streamline operations, improve customer assessments and optimize decision-making. The results coming in from enterprises big and small are positive and sure to improve as big data capabilities expand.