Data intelligence, sustainability and importance of connecting supply and demand

In recent times, I’ve worked with various smart cities and smart spaces on some exciting, innovative projects. And when you get into discussing innovation and how to scale it up, projects tend to throw up some interesting ideas. I recently worked with an energy company on an innovation project that centered around how to use measurement of people movement and people tracking to generate useful data. We reasoned that if it was possible to gather people movement data, like how and when they move through cities, where they spend time and how long, we could analyze it and generate insights from it. It might then help the city better manage energy supply and distribution.

It’s similar to the example of Clear Channel’s Radar program that uses people movement data to target resources in the shape of billboards: it’s been in place for four years and uses consumer mobile device data to target ads at them. People movement gives good quality data and helps Clear Channel reach out to the right people, accurately.

What if you could do more with your data?

If your organization – it could be a smart city, a manufacturer or retailer – is generating large amounts of data from social media, mobile apps, point-of-sale (POS), e-commerce, IoT-connected sensors and other systems along customer journeys, you need to use it to its maximum. If you gather that data, store it securely, analyze it correctly and turn it into actionable insights, it can impact your business across the spectrum. From project planning to sourcing raw materials to producing finished merchandise to promotions and seasonal launches, insights generated from real-time data help you make better decisions and streamline your business.

To get to this point, you need to gather and share all that necessary data, but particularly supply and demand data, and act on it.

Matching supply to demand data

I tend to think about smart cities or smart spaces from the perspective of their control centers: city operations are typically done in silos, like street lighting, parking, sanitation or waste management. In the future, smart cities will need to employ systems of systems thinking that helps break down walls between silos and bring disparate data sources together. You do this by applying data analytics to all those data sources that are connected – energy, people flow and transportation would all interconnect around the demand for resources or services, for example.

Applying systems of systems thinking to the enterprise

If something is quantifiable, it is measurable. Basically, if you can see how much of something you are using, you can control it. This is a direction enterprises will need to head in over time. It could start with a recommendation engine: your data tools gather data, analytics tools analyze it, your AI makes a suggestion, and that action is either implemented by the automated system or a human decision maker.

You then get to match supply and demand data across your enterprise to enable you to maximize resources and reduce costs. This approach lets you make data-driven decisions based on the most accurate information possible.

What needs to be factored into this approach?

Data-driven decision-making is a desirable goal, but it needs the right thinking behind it to be effective. Historian Peter Turchin believes it is possible to predict human behavior and decision-making in the future based on analysis of thousands of years’ worth of historic data. Turchin hopes that, eventually, no government will make policy without consulting this type of data analysis, without reflecting that it might be about to engage in a mathematically preordained disaster. It’s a macro-level way of looking at data that can be helpful, and could influence how enterprises look at data and its role in decision-making.

But historical data isn’t enough on its own. You also need to include real-time data observations and change your culture, too: many enterprises still don’t have an organizational focus on data and analytics, and it costs them. Putting a dedicated executive in place with responsibility for the optimization of data and analytics in the company, and whose job it is to oversee development of related enterprise competencies is essential, too. Companies that establish a data office and appoint a chief data officer (CDO) to lead and guide this area of the business will thrive. Gartner has forecast that by 2021, CDOs will perform a vital role, at the same level as CIOs, business operations, HR and finance in 75% of large enterprises.

Further to this organizational shift, companies need to shift corporate culture and mindset. It’s one thing to have access to huge amounts of data, to see the cloud as the source of all the data you might need, but having access to it isn’t the same thing as having knowledge and insights. Research shows that only one-third of enterprises currently use information to identify new business opportunities and predict future trends and behaviors.

58% of companies say they still base at least half of their regular business decisions on gut feel or experience, rather than data and information. This seems amazing to me in this era. Enterprises that use both a macro level of historic data analysis and real-time data under the umbrella of being a collaborative, data-driven company will be those that make the most accurate, successful decisions.

Tools and techniques

Digital tools are essential in shifting to data-driven decision-making. Artificial intelligence (AI) and machine learning (ML) solutions can take workloads off human workers and make simple decisions faster than people can. An interconnected system of systems in a smart environment that leverages all the data you are generating is valuable, and automation can enable efficiencies in this model. And everything gets activated and spun up based on its demand and then wound down afterwards when demand drops.

Digital twinning is a good way to test this out. Creating a digital twin of a smart city lets you run simulations of your data-based resource allocation to see how it plays out. And it allows you to assess the impact of technologies like IoT, AI and ML on the model.

Another highly-useful technique is data visualization. Jean-Philippe Favre, Data Artist at Business & Decision, part of the Orange Group, talks about how to use visualization to make data, graphs and charts more understandable. It’s a fascinating area and one that I see becoming an increasingly important tool in data-driven decision-making. Data artists can help you bridge the gap between business and IT using data visualizations that are easily and quickly understood by business leaders.

The way forward

When your whole organization has bought into this thinking, you will reap the benefits. A combination of matching supply to demand data, systems of systems thinking in the enterprise, and a data-driven culture in your organization will deliver a massive shift in your company’s success and results.

Orange can help you drive business value in your company by unleashing the potential of innovation. Read more about how Orange can help you use collaboration and digital ecosystems to foster scalable innovation, ideas and results.