Before pitching a high-tech proposal to your board or shareholders, look at the relevant big data analytics to see which risks need to be managed to reduce the likelihood of failure.
One of my formative C-level experiences was being the right-hand strategic person for the CEO of a semiconductor company. The semiconductor industry historically has been a feast or famine business, with the revenues either flowing in by the truckload or not flowing at all. Compared to other industry sectors that I worked in later in my career (e.g., financial services), life in semiconductors was not for the faint of heart. It seemed like there was never a time when as C-level executives that we weren't in the eyes (and sometimes the crosshairs) of our board and our shareholders.
We invested substantial R&D funds in a new piece of multimillion-dollar equipment that we thought would revolutionize work processes in semiconductor chip fabrication facilities. This was "gut feel" product prototyping, and the experiment ended by blowing up on the manufacturing floor during trials. In other cases, we developed highly successful and profitable equipment that brought in revenue and dramatically boosted stock prices.
Both the failures and the successes were ideas authorized by the CEO. The executive team had many long discussions before it was determined that we would try an idea (often only with an intuition that it would be viable), and then the product would be broadly adopted by our customers. Now, big data and analytics have the potential to "turn the page" on some of the risks that CEOs in high-technology industries have historically had to take and endure. Here's how.
- Research and development: Supercomputer-size big data repositories at universities and industry consortiums are more readily available for high-tech companies to tap into today, as are universities' big data scientists and other related resources that are available for collaboration, risk sharing, and profit sharing.
- Social media and other internet-based intelligence about specific industry sector and customer sentiment assist CEOs and other executive decision makers in the technology investments and choices that they make. In the past, this intelligence gathering was largely accomplished through word of mouth, or whatever the sales force happened to learn while out in the field.
- The ability to run complex algorithms and to develop paperless modeling of new technology concepts in a big data processing environment enables high-level decision makers to simulate a new technology or product before they make the investments that are ultimately required to move the product into a physical representation. If there is product risk, they can often uncover it in virtual modeling, before the physical resources have been spent.
This brings us to the boardroom, where the CEO must ultimately present his or her case for a new product investment. CEOs make these new product proposals to the board by explaining what the proposed product is and how it works; what its value proposition is likely to be for prospective customers; how quickly customers will commit to purchase the product once it is available; how much revenue potential is projected from sales, and when; how much internal investment is needed to bring the product to fruition; and what risks must be managed during the process.
In many small and midsize high-tech companies, the CEO presentation to the board was often a product of almost pure instinct. If a CEO succeeded in selling the idea, it was predicated on how much faith the board had in the CEO.
But with today's big data and analytics capabilities to simulate product prototype ideas and designs, to develop analytics algorithms and to run them against mountains of scientific data, and to analyze big data intelligence of the marketplace, the CEO now has analytics that he can present to his board along with his enthusiasm.
Big data and analytics don't necessarily take the risk out of high-tech ventures, but they can significantly reduce the likelihood of failure.