At Pivot BI Analytics, we see unstructured Data like crude oil in the way that it is buried deep inside corporations and only data analysis resulting in a structured visualization dashboard can make it insightful to businesses
What does successful use of data to drive the product development process look like? It’s much more about process and culture than it is about technology. The question is what type of culture are we talking about?
In recent years, the rapid development of Internet, Internet of Things, mobile application and Cloud Computing have led to the explosive growth of data in almost every business and industry area. Today, the big data technology and concepts have been gradually applied to the traditional industry from the Internet industry, big data has become an important driving force for economic reform and development. Market and consumer analytics is at the epicenter of a Big Data revolution.
Organizations that apply analytics and predictive tools to their product-development and project-planning processes see a dramatic reduction in schedule slippage. And because they can put the right number of the right people on their projects at the right time, they also enjoy R&D-productivity improvements of 20 to 40 percent. For companies, that means lower costs and lower risks—a powerful combination of benefits to have in a highly competitive environment.
The power of predictive analytics is multiplied when an organization takes an end-to-end process view of new product development (NPD). Idea generation and business case decision making are important. But an end-to-end view of performance in a business-process context provides additional opportunities to apply predictive analytics to improve performance in other areas, such as product development, testing, and launch.
Analytical methods apply to each of these steps. For example, in the area of product creation, it’s possible to improve performance by classifying key attributes of past success – such as early supplier involvement, broad cross-functional collaboration, use of key metrics to move from one gate to the next, etc. – and then model the relationship between those attributes and the commercial success of the offerings.
Similarly, in the area of new product testing, it’s possible to improve performance by classifying key attributes of past success – such as customer involvement, sales force collaboration, and key metrics, etc. – and then model the relationship between those attributes and the commercial success of the offerings. The same principles apply to planning and executing product launches, where a set of key attributes of past success can provide insight into what needs to be done to assure future success. The key in all of these areas is to collect data on attributes of the NPD process and then relate them to product success in the marketplace. This requires consistency, discipline, and a long-term perspective.
By taking a process-based view of the performance of NPD, organizations can improve their ability to view things from their customer’s perspective.
For example, the typical company cares a lot about the cost of new product development and the ROI on their investment, while customers care mostly about other things such as value for money, on-time product introduction (when promised), and quality (works right first time). By taking a customer focused, business-process-based view, organizations can gain new insights into why their NPD performance is below expectations. Analytics can play a big role in overcoming the major obstacles to decision making in NPD.
As the Aberdeen survey suggests, part of the problem with NPD analytics is that systems that supply data for new product development are fragmented and incomplete. Much of the data necessary for assessing customer demand and competitive responses is external to an organization.
In addition, the rise of product lifecycle management (PLM) software from vendors like PTC, Autodesk, Dassault Systems, and Siemens has made it easier to access data from internal systems. PLM data is commonly used for reporting, but it is rarely employed for predictive analytics.
We are just at the beginning of the use of analytics for NPD. Examples such as the Netflix case should spur organizations to begin using these tools to develop more successful new products. From the beginning, however, organizations should adopt an end-to-end process perspective on NPD analytics so that they don’t optimize only a single aspect of the process.
