What’s powering the digital economy? The analytics economy.

As the digital economy infiltrates almost every aspect of our lives – from banking and shopping to dating and learning – it begs the question: Is the digital economy simply becoming, well, the economy? Perhaps. Even more importantly, what makes the digital economy profitable? And how can you create ongoing value in the digital economy?

For some organizations, the digital economy brings limitless opportunities to connect with customers, innovate collaboratively and develop new experiences. For others, it’s fraught with disruption to traditional business models.

How can you take advantage of the opportunity – without becoming a casualty of disruption?

The answer is in the data, a natural resource of the digital economy. But data without analytics is value that’s not yet realized. Applying analytics to digitized products and services makes the digital economy effective, customizable, relevant and smart.

Read our questions and answers below to learn more about the analytics economy and why it matters.

What is the analytics economy?

One definition of an economy is “the careful management of available resources.” So the analytics economy is simply the careful management of your data resources. To take that a step further, consider the analytics economy as a layer of software technology that creates new and compounding value from digital data. This includes everything from traditional data management and data visualization technologies to newer machine learning and artificial intelligence methods. 

What are the challenges and opportunities in the digital economy? 

In the digital economy, organizations are either creating disruptive innovation or fending off disruptors. Disruption shouldn’t be confused with competition, however, which has always eaten away market share. Disruptive innovation is different – disruptors reinvent the way business is done. Take UberHealth in Boston. Historically, hospitals, doctors’ offices or pharmacy chains provided flu shots and other vaccinations. Now, registered nurses are providing on-demand delivery of basic preventive care. 

Data and analytics could help UberHealth expand this idea even further with additional health care delivery options for urgent care, disease screening and drug rehabilitation.

Innovation-based disruption requires integration and coordination across an organization. The best way to achieve this is by combining data, analytics and collaboration. 

The ultimate goal is help employees investigate data based on their natural curiosity, making it easy to extract new insights from that data regardless of skill sets. No matter when employees ask a data question or how they like to ask it, if you provide a common view of the data and a way to share results with others, then ongoing improvements happen.   

But finding answers and crafting innovation is just part of the analytics economy. For new ideas to work, they must be easy to deploy, otherwise they’re simply good ideas. A governed, controlled and repeatable process for analytics helps ensure that innovation is absorbed into the business, as part of ongoing operations. And a disciplined deployment approach can more easily adapt to new conditions.

The compounding value that comes from sharing data, acting on analytics insights and disclosing results for others to build upon is the ultimate benefit of the analytics economy. 

What are some examples from the analytics economy?

Duke Clinical Research Institute has made anonymized patient data on 39,000 patients available to doctors and researchers. Its cloud-based system allows different researchers to access and use the same data for research. Doctors can query the information to make informed medical decisions. Researchers can study treatment data to see what works best for different types of patients. And insights from one researcher can lead to new insights from another researcher, building upon findings and compounding the value from analytics insights.

Another example is GatherIQ™, a new, collaborative app designed to address global humanitarian issues. Available for free download, the app offers shared data and analytics to help solve problems by crowdsourcing answers that can compound the value of any one contributor. That’s the definition of an analytics economy.

Can you give us an example from the analytics economy?

McNeill: Duke Clinical Research Institute has made anonymized patient data on 39,000 patients available to doctors and researchers. Its cloud-based system allows different researchers to access and use the same data for research. Doctors can query the information to make informed medical decisions. Researchers can study treatment data to see what works best for different types of patients. And insights from one researcher can lead to new insights from another researcher, building upon findings and compounding the value from analytics insights.

Another example is GatherIQ™, a new, collaborative app designed to address global humanitarian issues. Available for free download, the app offers shared data and analytics to help solve problems by crowdsourcing answers that can compound the value of any one contributor. That’s the definition of an analytics economy.

What technology advancements are powering the analytics economy?

McNeill: In the not too distant future, the technologies of intelligence automation, networked data and ambient analytics will affect the analytics economy. Let me explain each of these.

Automation driven by analytics will extend beyond eliminating and automating human tasks. Intelligent automation embeds analytics into digital processes so that the digital processing itself self-identifies when automation makes sense, when it doesn’t and what should be automated.

For example, intelligent automation could lead to more economical cloud provider services. Analytics processing can have spikes of increased activity, perhaps when month-end reporting and the implementation of a new suite of models coincide. An automated system would forecast processing workloads and diagnose the pending need for additional compute capacity. At the same time, it could notice if your prepaid cloud is near its limit. Instead of paying a premium hourly price for the excess capacity, your intelligent automated system would monitor the cloud spot pricing rates and automatically purchase the overflow only when an economical per-hour price becomes available.

The second topic is networked data. While we’ve already begun to see new networks of data emerge in the Internet of Things, what I’m thinking about here is a bit different. With the potential to solve security and privacy concerns, a distributed data network similar to blockchain could alter the way we store, register and access data. A virtual, distributed data network that is trusted and solves current concerns may redefine how data is used and by whom.

Finally, ambient analytics refers to analytical decision points that happen around us without our knowledge or input, including spot cloud computing purchases, thermostat adjustments, traffic light changes and online advertising displays. Ambient analytics becomes possible when you bring analytics to the data by cleansing, transforming, filtering and analyzing data at its source. When data has intelligence as it’s issued, it can be directed from its source to use automatically. That’s true of ambient analytics. The data is clean, relevant and has specific merit as it’s generated. Just as data is everywhere, analytics will be everywhere that data exists.

To tie these three concepts together, when we can securely network intelligent data, analytics becomes its own network and even drives intelligent automation.

What should organizations do now to get more from the analytics economy?

Just as we’ve seen with the Duke Clinical Research Institute example earlier, the way to succeed in the analytics economy is by having a consistent approach for anyone to collaborate and take the best actions in a governed, repeatable way.

This happens when you can make decisions quickly by creating, deploying, and refining analytics applications – and then repeating the process. Your analytics platform should handle a large volume of actions and workload variations, infrastructure changes, and a variety of methods and approaches with precision, accuracy and relevance.

Measuring results and understanding quality of actions are essential, as are governance, consistency and repeatability.

The SAS vision is to provide a single platform that powers the analytics economy. This doesn’t mean you have to replace existing tools and techniques within your organization. Rather, it’s designed to bring them together into a trusted, scalable and flexible ecosystem that forms the basis for ongoing innovation and value.