Big data means billions if not trillions of observations recorded daily. Sophisticated data infrastructures and advanced algorithms reveal new insights into our fast changing world, but only for those massive organizations with the transaction frequency and the know how to extract them, right?
The question is one of relevance. While big data benefits for large organizations are readily tangible--like in the case where retailers want to know when customers are more likely to purchase which products, or what optimal prices will move discounted products in a set time frame--smaller organizations sometimes just don't see how the data revolution is important to them. They might not know they have the data, and in some cases, start-ups for example, they might not. They are also more often focused extensively on the sales pipeline and do not prioritize data analytics. A focus on the sales pipeline is critical, but in many cases a failure to leverage the knowledge in data can render that focus futile. The irony is that both big data and smart data might be more important to the small organization than the large one.
Data science is decision science with an edge, more data. In small organizations, decision outcomes are relatively larger, and impact on both the upside and the downside is greater. Take, for example, the decision of where to locate a business unit. Different locations will have consumers with different preferences for different products. Future economic conditions will impact real-estate values, rents, and product demand. Demographic data might determine if external economies of scale can optimally support human resource requirements. A large organization might more easily commit without data analysis (not recommended), if things don't work out, they can shut down the unit and absorb the loss. In a smaller organization, the economies of scale are also smaller, and there might not be enough financial space to absorb the loss.
And small organizations do have data. On-line traffic, inventories, sales, operations, fixed and variable inputs, purchasing transactions, are all data, not necessarily the big kind, but definitely the smart kind. Production, inventories, administrative data are stored in internal organizational management systems. Financial transactions data are processed in our accounting software. Customer data is almost always collected and processed in some form, through an Internet portal or customer feedback surveys, for example. Documents, like proposals and estimations, that impact forward growth of new products, expansions, and strategies, also rely on data. If data isn't being collected, it should be.
In addition, for those start-ups that don't have the historical data to inform product launches, pricing points, and location decisions, many of the insights big data players are discovering are open source, or become so rapidly. External data sources can be mined to ensure business plans adequately minimize opportunity costs.
The fact is small organizations need data analysis too, and they need not rely on training internal staff to use clumsy and fleeting over-the-counter business solutions to mine it. A new sector of data-focused organizations, in the business of aggregating and analysing data, have begun to inform organizational decisions across disperate sectors and regions with sophisticated cutting edge data science breakthroughs. Stand-alone data science services are now available without the cost and risks found in typical big data investments.