Nowadays, it is difficult to argue that business intelligence is beneficial to any organization, regardless of industry. Data optimization and governance have been shown to result in better long-term decision-making.
That is not to say that data implementations have been flawless. Some businesses have failed to become data-driven on a much larger scale than one might expect. Others, on the other hand, are moving quickly and have begun to rely heavily on external data sources.
Big data has proven to be extremely beneficial to those who have successfully managed their internal sources. Strategic use of big data allows organizations to gain a better understanding of their customers, create more appealing marketing campaigns, and forecast demand more accurately.
Big data and external sources
Volume and velocity are two of the key determinants in our case, according to the five Vs model. Big data derived from external sources differs from internal data in that it has no bounds.
Internal sources will always be constrained by the size of the company. In a poetic sense, the company is at the mercy of its customers to obtain such information. There won’t be much data produced if the organization is small in terms of both operations and revenue. Attempting to derive large-scale insights from small datasets is frequently doomed to failure.
External sources, on the other hand, are constrained by the rate at which data is generated on the internet. In practice, the velocity and volume of data are virtually limitless, limited only by technical capabilities. There is so much information produced daily that even after all considerations and source trimming, there is something to be found and analyzed.
As a result, the volume and velocity of big data, primarily from external sources, are orders of magnitude greater than what internal resources could handle. Furthermore, there is a significant qualitative difference in the data.
External sources provide us with data that has been left behind by a wide range of different sources. Most of it has no direct relationship to the business that will use the information, making it far more unbiased than anything produced by an internal source.
In the end, combining both sources yield big data. External ones, on the other hand, have a much larger volume and velocity. It’s important to note that these two sources complement each other. While some of the insights they provide may be redundant (for example, customer habits), they can also provide unique signals that can help improve overall business strategy.
Hidden BI gems in big data
External sources may not always produce unique signals that cause us to change strategy, but they do strengthen our current methods. Furthermore, they may provide information that would otherwise be unavailable.
Take, for instance, the use of CRMs. Almost all digital businesses rely on these systems daily. Customer profiles, on the other hand, have expanded in a variety of directions. There are now potentially useful data on businesses and individuals available all over the internet.
A good example is social media. Because the majority of their customers will have some form of social presence, many businesses can choose to pull publicly available data from social sources. These enhancements would be especially beneficial to those working in business-to-business.
A combination of internal and external sources, on the other hand, can result in better planning and budgeting options for all businesses. External data allows organizations to forecast and predict demand, whereas internal sources can more accurately represent available resources to meet those needs.
It is particularly beneficial in industries like e-commerce. External data provides organizations with a better understanding of the overall market, its trends, and opportunities. Businesses have used a variety of methods to successfully collect and access massive amounts of external data.
Obtaining big data
Most digital businesses successfully collect a large amount of data from internal sources, so acquiring it is rarely a problem. However, the other counterpart, external data, is more complicated.
It can be divided into two categories: traditional and advanced. Traditional external data (e.g., government reports, statistical databases, etc.) has traditionally been used primarily by financial firms and large e-commerce companies. These are typically massive datasets that provide insight into broad overviews of markets and economies.
Advanced external data, on the other hand, is a relative newcomer, but it has already produced excellent results. Such data includes any publicly available online data, such as reviews, pricing information, and so on.
Big data emerges when internal sources of information are combined with advanced external data. Bringing these two together isn’t as difficult as it once was. There are numerous third-party web scraping solution providers and even DaaS businesses that can provide data on demand.
It is no longer necessary to develop scraping solutions or similar infrastructure in-house. The majority of it can be outsourced at reasonable prices, making data governance easier. All that is required is a data warehouse to house the prepackaged information obtained from a third party.
There are two approaches to analysis. The simplest approach is to treat external data as if it were its complete dataset and to seek insights directly from it without interacting with internal data. Treating it as a separate entity is often simpler, and there is less room for error.
However, sources can be combined if proper labeling is done and data is carefully selected. CRMs, as I previously stated, are an excellent example of a candidate for combination. The more comprehensive the data set, the more insightful it becomes.
Conclusion
For most businesses, adopting big data entails interacting with external sources of information. Even if we assume, they are completely self-contained, these have enormous potential. However, when combined with internal sources, they can significantly improve daily decision-making and business operations.
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