By Bryan Kirschner, Vice President, Strategy at DataStax
One of the most painful – and pained – statements I’ve heard in the last two years was from an IT leader who said, “my team is struggling to find ways that our company’s data could be valuable to the business.”
Contrast this with what a financial services CIO told me: “Our CEO told every line of business general manager you now have a second job: you’re the general manager of the data produced in your line of business.”
The latter case is as it should be. In a pre-digital world, there would be no doubt that the people running a business function – sales, service, support, or production – should be using all the information available to them to drive better results.
But many organizations took a detour, misled by a fundamentally flawed assumption that because some data is digital in nature and technical skills are necessary to ensure it is properly stored, secured, and made available, those same technologists should be on the hook for finding new ways for business managers to leverage the data.
Wanted: Real-time data skills
Leading organizations have proven there’s a better way forward – but success can’t be taken for granted. Among all respondents surveyed for the latest State of the Data Race report, complexity, cost, and accessibility are cited as the top three challenges they face in leveraging real-time data. In contrast, the number one challenge among those most accomplished at driving value with real time data today is the availability of the necessary skills in their business units to leverage it.
It’s likely a hangover from the old way of doing things. The good news is that there’s a cure — in the form of a clear playbook for making progress toward equipping business managers
Your technology teams should indeed be accountable for understanding the capabilities of best of breed tools – and making them available widely in your organization.
But everyone — not just technologists, but also business leaders — must have both accountability and skills for using real-time data to drive the business and grow revenue.
Consider pharma giant Novartis (as detailed in this Harvard Business Review article). Over the past decade, the company invested heavily in data platforms and data integration. But it found that these investments only resulted in spotty success. Data scientists had little visibility into the business units, and, conversely, leaders from sales, supply chain, HR, finance, and marketing weren’t embracing the available data. Once data scientists were paired with business employees with insight into where efficiency and performance improvements were needed, and once frontline organization employees were trained to use data for innovation, the intensity and impact of transformation accelerated.
New ways of working
A clearer sense of a shared mission, along with a stronger common understanding of capabilities of modern technology and greater shared intimacy with business processes and customer experiences further pays off by opening the door to new ways of working.
Take banking, one industry where developers are critical to success in delivering new services for customers, and where incumbents must contend with a growing fintech ecosystem of aspiring disruptors. Goldman Sachs is embedding software developers deeper into the business where–in the words of the CIO– “we want them to answer the ‘why’ questions that get to the business purpose behind their work.”
In the State of the Data Race report, 91% of respondents from organizations with a strategic focus deploying apps that use data in real-time said that developers, business owners, and data scientists are working in cross-functional teams. Compare that to organizations who are still early on in their real-time data journey: only 67% of them claim to have this cross-functional coordination.
The other side of the coin is AI and ML, which are integrally related to activating data in real time. Some 93% of those with AI and ML in wide production are organized into cross functional teams versus 63% among those in the early days of AI/ML deployment.
Leveraging real-time data used to be a technology problem. Complex, legacy data architectures can still cause challenges, but the data technology landscape — assisted significantly by advances in the open source community — has advanced more than far enough to make real-time capabilities available to organizations of all sizes. The primary challenge real-time data leaders face is a clear indicator of this. Now, companies like Goldman Sachs and Novartis are working to ensure that the real-time data they’ve made readily available turns into real-time results.
Learn more about DataStax here.
About Bryan Kirschner:
Bryan is Vice President, Strategy at DataStax. For more than 20 years he has helped large organizations build and execute strategy when they are seeking new ways forward and a future materially different from their past. He specializes in removing fear, uncertainty, and doubt from strategic decision-making through empirical data and market sensing.
