Big Data Choices and Tradeoffs

Big Data Choices and Tradeoffs

This is the final post in a series of three addressing strategic considerations for how to approach building your organization’s Big Data infrastructure to create business value.

The previous post described the technology infrastructure required for Big Data analytics to help manufacturers derive value across the enterprise.

The path to competitive advantage ultimately rests on strategy, and in a smart, connected world companies face new strategic choices related to data and the required technology infrastructure. Each choice involves tradeoffs, and each must reflect a company’s unique circumstances.

A key question companies must address is what data must they capture, secure, and analyze to maximize the value of their offerings?

To determine which types of data provide sufficient value relative to cost, the firm must consider questions such as: How does each type of data create tangible value for functionality or efficiency in the value chain? How often does the data need to be collected to optimize its usefulness across business functions, and how long should it be retained?

Since data is increasingly fundamental to value creation and competitive advantage, some manufacturers choose to capture everything in a more-is-more strategy. Companies, however, must consider some key costs and risks for each type of data collected, analyzed, and stored beyond the infrastructure already discussed in this article.

First, the more data, the more complex and uncertain this process. Big Data analytics is highly process- and device-dependent and there is no “one-size-fits-all” solution. Second, there are hard costs from the additional embedded sensors, processors, and data transmission fees, and also significant security and privacy risks from amassing product data as product data clouds become new targets for hackers.

Third, many companies find the ultimate limiting factor is not technology, but the cultural and organizational change required to transform business processes based on new data insights.

Instead, a company should identify the data that reinforces its competitive positioning and creates unique value for its customers. A strategy focused on delivering optimized product or service performance must capture and analyze “immediate value” data in real time to reduce product downtime. This is especially important for complex, expensive products for which downtime is costly, such as mining or medical equipment, wind turbines, or jet engines. A strategy focused on establishing a product system or enabling a system of systems must capture extensive data across the ecosystem and multiple products.

Another key question companies face is whether they should develop a full set of smart, connected product capabilities and infrastructure internally or outsource to vendors and partners.

Developing and maintaining the new global technology infrastructure and capabilities requires significant investments that have not been typically present in manufacturing companies. Many of the capabilities required are scarce and in high demand.

A company must choose which layers of technology to develop and maintain in-house and which to outsource to suppliers and partners. In utilizing outside partners, it must decide whether to pursue custom development of tailored solutions or license off-the-shelf, best-of-breed solutions at each level. Our research suggests that the most successful companies choose a judicious combination of both.

As we have seen in previous waves of technology, early in the market we see vertically integrated solutions, where a single vendor is providing the entire technology stack. Over time we see specialization, just as Intel has specialized in microprocessors and Oracle in databases. New firms that specialize in components of the infrastructure are already emerging, and their technology investments are amortized over many thousands of customers.

A simple change in an analytics approach or business use case can result in a multifold increase in costs and time. Early movers that choose in-house development can overestimate their ability to stay ahead, potentially resulting in a slowing down of their development timeline. A better strategy is to focus on areas where, because of knowledge of the customer, product or processes, an advantage can be created over specialized technology players.

Conclusion

The technology and capabilities enabling Big Data analytics and machine learning are amongst the hottest growing technology areas fueling new business opportunities and innovation.

By capturing and analyzing data during each and every stage of the product and service lifecycle, manufacturers can access the information needed to create competitive advantage, but this will require new skills, infrastructure, and cultural norms. The winners in this smart, connected world will be those who understand how to capture, analyze, and capitalize on these new streams of data. Those who don’t risk placing their competitive advantage at risk.

This is an excerpt from an article first published in Frost & Sullivan’s Manufacturing Leadership Council’s thought-leading Manufacturing Leadership Journal.

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Image by Perspecsys Photos on Flickr (CC BY 2.0)

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