Organizations are building in-house data science or Artificial Intelligence teams to use emerging technology and techniques to harness the power of their data. With the growth of these internal data science teams, companies want greater control of all aspects their data programs so they’re more nimble and effective. If done correctly, this provides more opportunities for creativity and experimentation with internal and external data. “Science teams” might conjure images of NASA space camp t-shirts, quoting dystopian novels and late-night sci-fi board game parties. However, science is inherently cool. And it’s especially cool when applied to speech analytics. However, organizations should draw the line between data science and software development, and be cautious when taking on data science projects that are not core to their business.
Scientists, by their nature, don’t inherently trust results or outcomes unless they are logically transparent or tested. The problem comes from organizations deploying in-house data scientists to projects outside of their core competence. There is an allure here, because building new metadata internally on a company’s highly valuable data assets can provide new intellectual property and possibly a competitive advantage. Speech recognition systems and machine learning algorthms are readily available through various cloud computing platforms. With all this upside what’s the risk?
When it comes to complicated software like a speech analytics, many organizations don’t fully realize the complexity associated with building their own solution until they are highly invested, and often are left footing a large bill with a suboptimal outcome. Here are some thoughts on the benefits of DIY speech analytics software development and some pitfalls: