It would seem that artificial intelligence is the focus of businesses that are looking to future proof their organizations and stay competitive. And why not? Gartner has been encouraging organizations to use AI for their initiatives. There are multiple benefits too – from generating revenue to improving efficiency, reducing risks, and even disrupt the entire business.
But the truth is that AI is only one part of the puzzle. You will appreciate the real economic value of data science when you know the driving forces behind it. Apart from AI, you also have:
- Data and data lakes. This is what matters most. AI is mostly open-source, which means you will probably have the same tools as your competitors. The data you have will determine your edge. Data science will help turn raw data into business insights.
- Rapid testing environment. Data science needs a fast testing environment that will allow you to explore, learn, or fail fast. This will enable you to find things that will help you succeed.
- Design thinking. This is a related and sometimes considered complementary discipline of data science. It ensures that you discover, explore, and validate processes between the business and data scientists, as well as the experts when it comes to a particular area of your operations.
- Your teams. You should assemble the best data engineers, business managers, and data scientists who will work together on your data projects.
- Big Data Business Model Maturity Index. This can serve as your roadmap for using the economics of data science to improve your processes, reduce risk, and discover new revenue sources.
- Economics. Through all of these, economics should connect design thinking, data science, and other processes and technologies to create value and create new income streams.
How It All Ties Together
The platform and its technologies, such as machine learning, deep learning, artificial intelligence, and others, will help you get the most out of the data you have. That means that without data science, you will probably have wasted money, resources, and time on your big data and Internet of Things initiatives.
However, as we have pointed out, AI and other technologies are all available to your competitors — sometimes for free. This is the reason why what’s more important is the data you have and what you do with it.
Data Science Monetizes Your Data Lake
Economic potential comes from the data you have. As such, you can think of your data lake as oil, which can give an entity, country, or business immense power and high earnings.
Like petroleum, however, you will need to refine your data to extract value from it. When you have a data lake, you will need data science to capture, transform, enrich, and use the data you have.
Data Science Development Environment
Data science involves a number of steps – from defining what it is you need to test, gathering data, preparing and then visualizing the data, to building the analytic models and then evaluating the insights that your data provides.
As such, to do data science, you need a fast development environment that will allow you to go through all these processes, and even repeat some of the steps over and over.
Data Science and Design Thinking
The platform should work closely with design thinking, which involves subject matter experts from the business side of your organization. The most successful data science teams involve team leaders and managers who will help your IT to define outcomes and come up with strategic, actionable, and material plans that your business can use.
More than the business leaders and data scientist, you will also need to include data engineers. Their respective roles are as follows:
- Business leaders and experts will provide a clear view of what the data is going to be used for. They will also act as expert of their own areas: for instance, the HR manager will advise the data team on what their group needs from the data.
- Data engineers will collect, manage, and visualize the data. They make data easy and available to use.
- Data scientists analyze the available data to collect actionable insights from them.
It takes an entire community to succeed with data science.
Economics of Data Science
Economics centers on the creation of value, and it aims to discover new digital resources such as data and insights that can be used by business in creating its products and even improving its processes. As such, economics.
The Big Data Business Model Maturity Index gives you a roadmap and benchmark on how you can use the economics of data science for your business models.
You can use this index to:
- Improve your current processes
- Reduce and manage security risks
- Use insights gathered during the improvement of your processes to find new sources of revenue and value
- Give your customers and employees better experiences via smart apps that learn with every engagement
- Use products that utilize data, automation, and analytics so that it can monitor, diagnose, and repair by itself
Four Cornerstone to the rescue!
We can help you with your data campaigns. If data and data lakes bother you or if your company does not have the resources to run data science technologies, such as machine learning, deep learning, and artificial intelligence, call us now at 1 – (817) 887-3397. Four Cornerstone has a team of experts that knows the real economic value of data science.
Photo courtesy of Penn State.
