The world is swimming in data!
Statistica recently estimated global data storage now approaches 180 zettabytes. That’s 180 trillion gigabytes! This surge is propelled by the widespread adoption of data-driven technologies, SaaS and cloud-based applications, Internet of Things (IoT) connected devices and the proliferation of social media, leading to unprecedented explosion of corporate data. The amount of data created over the next 3 years is expected to exceed the cumulative data production over the last 30 years.
Further exacerbating the data challenge is the exponential growth of unstructured data of which only a small fraction is analyzed. Small and medium businesses can quickly become overwhelmed by this problem of ‘too much data’.
Nonetheless, with the right expertise and tools, this can be transformed into a surmountable task. In the past decade or so, major Fortune 500 corporations have been crafting comprehensive Data Strategies. They have committed investments in solutions like Data Lake and Data Lakehouse, fostered the growth of advanced Business Intelligence proficiencies, and harnessed the potential of Machine Learning and AI. These efforts have facilitated the comprehension of their extensive data repositories, enhancing their competitive advantage.
Should your organization embark on a similar journey, what unexpected gemstones of insights might you unearth from within your data troves?
Here are 5 surprising business accelerators you’ll discover…
- Hidden Customer Insights That Fuel Growth
- If your organization is collecting data on customers ranging from product preferences to purchase history and website interactions, you are already on your way toward building a data-fueled framework that facilitates growth. Analyzing this data has the potential to unveil surprising insights about customer preferences and behaviors.
- For instance, a retailer may discover that customers who bought a specific type of electronics later showed an increased interest in outdoor adventure products, leading to promotional and suggestive selling opportunities.
- Alternatively, a cosmetics enterprise that monitors social media comments might stumble upon frequent discussions about the advantages of their cruelty-free practices. Capitalizing on this, the company could integrate these aspects into their marketing endeavors, captivating a novel segment of conscious consumers and stimulating sales growth.
- Understanding such patterns can enable businesses to maximize opportunities to sell to existing customers and build brand loyalty.
- Surprise Correlations
- One of the most intriguing surprises that engaging AI capabilities can unveil is the presence of seemingly unrelated variables exhibiting strong correlations. These unexpected connections may be hard to grasp but offer the potential for significant novel insights and understanding.
- For example, a manufacturing company might observe a correlation between employee satisfaction, lighting, and varying productivity rates based on plant location. This newfound understanding could motivate targeted enhancements to the work environment, thereby increasing production rates.
- In the context of e-commerce, an enterprise may discover correlations between conversion rates and the colors of the checkout buttons on their websites. A seemingly simple change in button color could lead to significant improvements in sales.
- Uncovering these unexpected links can prompt further investigation that sheds new light on complex interrelations that remained unexplored.
- Unexpected Cost Drivers
- Analyzing data to uncover unexpected cost drivers involves examining diverse data sources and patterns to identify factors that may be influencing costs but are not immediately apparent.
- For instance, a product manufacturer may discover previously unnoticed energy inefficiencies in equipment, facilities, or production processes that lead to higher utility bills and operating costs. Correcting these inefficiencies can impact bottom line profitability immediately.
- Seasonal Patterns in Unrelated Data
- Sometimes, when analyzing seemingly unrelated data sets, you might stumble upon hidden seasonal patterns. These recurring trends may adhere to specific time-based cycles but are not necessarily explainable by normal logic or collective experience.
- For example, an analysis of customer service calls in a SaaS company might reveal an annual upsurge around the holiday season, despite the product being unrelated to anything related to the holiday itself.
- Alternatively, an insurance company may find a temporary increase in car accidents in the days immediately following the start of daylight-saving time attributed to the disruption to sleep patterns. Recognizing this trend could assist in agent scheduling during these specific periods.
- The identification of such seasonal patterns enables efficient resource allocation, ensuring preparedness for periods of heightened activity. These insights allow for strategic resource management and proactive responses to fluctuating demands.
- Emerging Trends and Predictive Insights
- Data accumulated over time contains hidden patterns and shifts, which can be extracted through the application of sophisticated data analytics and predictive modeling methodologies.
- For instance, analysis of data from sensors affixed to industrial machinery with machine learning algorithms enables detection of impending equipment malfunctions. This proactive approach empowers manufacturers to conduct timely maintenance, thus curtailing downtime, and curbing maintenance expenses.
- Furthermore, AI algorithms possess the capability to dissect historical sales records, weather fluctuations, social media trends, and other pertinent factors, facilitating the projection of consumer demand for specific products. Retailers can leverage these insights to enhance inventory management, pricing strategies, and marketing campaigns.
- Identification of these emerging trends serves as a foundation for preemptive actions, fostering enhanced readiness for the future. Such foresight is instrumental in optimizing processes, resource allocation, and strategic decision-making.
In today’s world, it is critical that firms truly become “data-enabled” in order to compete effectively. No longer can we make decisions based on experience or gut feeling alone. The stakes are high, and the winners will be those who collect and leverage data across all parts of the enterprise.
