When business leaders hear the term big data, they most naturally think of the daunting volumes of new data available today. This data is created from e-commerce and omnichannel marketing, or from connected devices on IoT, or from business applications that generate ever more detailed information about transactions and activities.
Nevertheless, big data is not simply characterized by the sheer scale involved; the data itself is diverse and constantly changing. As a result, the term big data also encompasses new ways of storing, processing, managing and serving the information that drives business decisions. It is these new techniques, especially big data analytics, just as much as the volume of data, that enable the big data benefits that business executives and IT teams alike hope for.
Let's have a look at six ways in which big data can improve the way we do business.
1. Better customer insight
When a modern business turns to data to understand its customers -- whether individually or in categories -- it has a wide range of sources to choose from. Big data sources that shed light on customers include the following:
- traditional sources of customer insight such as purchases and support calls
- external sources such as financial transactions and credit status, if these details are available within a company's terms of service
- social media activity
- data from external surveys
- computer cookies
Clickstream analysis of e-commerce activity is especially useful in an increasingly digital marketplace, shedding light on how customers navigate through a company's various webpages and menus to find products and services. Companies can see which items customers added to their carts but perhaps removed or later abandoned without purchasing; this provides important clues as to what customers might like to buy, even if they don't make a purchase.
Not only online stores, but brick-and-mortar locations can also glean useful understanding of their customers, often by analyzing video to learn how visitors navigate through a physical store compared with their navigation of a website.
2. More insightful market intelligence
Just as big data can help us appreciate our customers' complex shopping behaviors in more detail, it can also deepen and broaden our understanding of market dynamics.
Social media is a common source of market intelligence for product categories ranging from breakfast cereal to vacation packages. For almost any commercial transaction you can imagine, there are people out there sharing their preferences, their experiences, their recommendations ... and their selfies! Yes, even of their breakfast fare. These shared opinions are invaluable for marketers.
In addition to competitive analysis, big data can also help in product development: prioritizing different customer preferences, for example.
In fact, big data does not just assist with modern market intelligence; in almost any e-commerce or online market, almost all market intelligence is driven by diverse, ever-changing data.
3. Agile supply chain management
Whether it is pandemic-driven toilet paper shortages, the trade disruption of Brexit or a ship stuck in the Suez Canal, you should be aware by now that modern supply chains are surprisingly fragile.
Surprising, because, mostly, we don't notice our supply chains until there is a truly major disruption. Big data, including predictive analytics, often in near real time, helps to keep our global network of demand, production and distribution working well for the most part.
This is possible, because big data analytics can integrate customer trends from e-commerce sites and retail applications with supplier data, real-time pricing, and even shipping and weather information to give a level of business intelligence not seen before.
It's not just large enterprises that benefit from these insights. Even modestly sized e-commerce businesses can use customer intelligence and real-time pricing to optimize business decisions such as stock levels and risk reduction, or temporary or seasonal staffing.
4. Smarter recommendations and targeting
In our lives as consumers, we are now so familiar with recommendation engines that we might not be aware of how much they have evolved since the advent of big data. At one time, the predictive analysis for recommendation engines was quite simple: association rules which found those common items in market baskets. You can still expect to find this as a feature on e-commerce websites telling us that customers who bought widgets also bought fidgets.
Newer recommendation systems are much smarter than that, building on the sophisticated customer insights we have already discussed, with the result that they can be more sensitive to demographics and customer behavior. These systems aren't limited to e-commerce, either. A friendly waiter's recommendations may well be data-driven -- decisions prompted by a point-of-sale system that evaluates stock levels in the pantry, popular combos, high-profit items and even social media trends. When you share a picture of your meal, you are providing yet more input for the big data engines to digest.
Streaming content providers use even more sophisticated techniques. They may not even ask customers what they want to see next: even before the current movie, program or song finishes, the next selection fades in, keeping viewers binge-watching by utilizing their own preferences combined with a great deal of big data analysis gleaned from other users and social media.
5. Data-driven innovation
Innovation is not just a matter of inspiration. There's a great deal of hard work in identifying subject areas that are promising for new efforts and experiments.
Big data tools can enhance R&D, often leading to the development of novel products and services. Sometimes, the data -- cleansed, prepared and governed for sharing -- becomes a product in itself. The London Stock Exchange, for example, now makes more money from selling data and analysis than it does from securities trading.
Data by itself, even with the best big data tools, will not produce new insights. We still need the human element: the understanding and imagination of data scientists and business intelligence analysts. However, the breadth and scope of big data, especially when stored on a single platform such as Hadoop or a cloud data warehouse, can lead teams to a new understanding of trends, which would be difficult to glean in a less integrated environment.
6. Improved operations
Business activity of all kinds can be improved by the use of big data, but one of the most interesting and rewarding has been the use of big data analytics to improve physical operations.
For example, the use of big data and data science to inform predictive maintenance schedules can reduce costly repairs and downtime for critical systems. You can start by analyzing the age, condition, location, warranty and service details. However, some of these systems, such as security and HVAC in facilities, are notably affected by other business activities such as staffing and production schedules, which may, in turn, be influenced by sales cycles and, therefore, by customer behavior. Well-integrated big data analytics can pull all this together to help you maintain the right equipment at the optimal time.
Big data is now the lifeblood of businesses
As you can see from these six scenarios, the potential for using big data is very exciting. It is also fair to say that you must be increasingly conscious of the regulatory environment -- compliance with privacy, security and governance regulations is essential. Nevertheless, the advantages and benefits of big data outlined here are surely worth that effort. Big data is the lifeblood of modern business and one of your greatest resources for driving smart, sustainable change.