Will your data product sparkle more if you build it or buy it?
“Should we build or buy?” is perhaps the diamond of enterprise IT: a question with at least 57 facets, each unique to a particular organization. In the past, your company may have made build-or-buy decisions when weighing options for enterprise software suites or departmental applications. Today, as more and more organizations look to data monetization to tap new revenue streams – IDC predicts that in 2020, in over half of Global 2000 firms, revenue growth from information-based products and services will be twice the growth rate of the balance of the product/service portfolio – the question emerges anew: should you build or buy your data product?
Monetizing your company’s “data exhaust” will likely be an IT initiative unlike any other, because customers will consume the fruits of your labor, suppliers and other stakeholders outside of your enterprise – not internal users. If you plan to deliver an analytic engine inside your data product or provide data analytics services, here are five key questions to consider when deciding whether to build or buy the necessary capabilities.
- Will you wow your audience? In a competitive marketplace, your data product has to sparkle – and not just on the surface. With enterprise applications now as great-looking and functional as the hottest mobile apps, you’ll need to honestly assess if you can deliver a data product that will meet user expectations, which now are higher than ever.
- Can you keep a commitment? Does your organization have the resources and budget to maintain and improve the data product after it goes live? Any organization with a software offering must continually enhance a data product, to refresh its sparkle in response to competitors’ improvements, and to offer new features and functions. A data product can’t be regarded as just a “set it and forget it” endeavor.
- Have you thought through total costs? Delivering a data product entails more costs than just numerous development resources (analytic engine, user interface, performance). You will need to calculate the resources associated with standing up new customers and to the effort required to maintain reliable data governance and cleanliness measures, as well as enterprise-class security.
- Have you considered non-monetary factors? Beyond the cost of developing, maintaining, and securing the analytic product and underlying data, there are numerous non-monetary factors to consider, too. These include the risk factors of time to market, keeping pace with competitive enhancements, and developing a roadmap of functionality. If your company’s everyday business is not in delivering commercial software, you will want to weigh the benefits of diverting resources away from your core competency.
- Do you really want to reinvent the wheel? Is it necessary to build an analytics engine for your data product? Again, if your core business is not in developing analytics software, probably not.
Assess your options
When it comes to developing a data product for the market, there is no single correct answer or approach. Consider using a tool like a risk matrix to compare the implications of building versus buying your data product, factoring in risks and costs. At the same time, look at more than just costs as you scope the long-term commitment that revenue-generating data products entail.
Birst has deep experience in working with customers in a wide range of industries, helping them to deliver successful data products. Birst analytics technology is embedded in analytics products from leading organizations, including American Express Global Business Travel, the world’s largest travel management company, and many more. We are eager to share our expertise with you.
To learn more about how Birst can help your company deliver a diamond of a data product in as little as eight weeks, download our informative white paper today: “Build vs. Buy: Making the Right Choice for Your Data Product.”