Maybe you are new to AI and analytics. Or maybe you have been working with data and analytics for decades, even before we called this work data science or decision science. As the industry has broadened from statistics and analytics to big data and artificial intelligence, some things have remained constant.
I call these foundational truths the Principles of Analytics. They inform our approach to data and analytics, and they manifest themselves in our products and services. My hope is that sharing them here will inform your approach to data and analytics, too, and help guide your digital transformation and decision processes.
The four principles of analytics are:
1. Analytics follows the data, analytics everywhere.
2. Analytics is more than algorithms.
3. Democratization of analytics; analytics for everyone.
4. Analytics differentiates.
Principle 1: Analytics follows the data, analytics everywhere
Data are a resource. If you are not analyzing it, it is an unused resource. At SAS, we often say, “Data without analytics is value not yet realized.”
Naturally, then, wherever there is data, there needs to be analytics. But what does that mean today when we are generating more data and more diverse data than ever before? And all of that data streams or moves about many different networks.
The first principle of analytics is about bringing the right analytics technology to the right place at the right time. Whether your data are on-premises, in a public or private cloud, or at the edges of the network – analytics needs to be there with it. If data moves to the cloud, analytics moves there with it. If data streams from the edge, analytics is there too.
The first principle manifests itself in:
• Analytics pushed aggressively to the edge in devices, network routers, machines, health care equipment, cars, phones and more.
• Analytics integrated with cloud storage and cloud computing.
• Software that supports cloud-native and on-premises environments.
• An emphasis on data integration, data quality, data privacy and data security.
Principle 2: Analytics is more than algorithms
You should pay great attention to the quality, robustness and performance of your algorithms. But the value of analytics is not in the features and functions of the algorithm – not anymore. The value is in solving data-driven business problems.
The analytics platform is a commodity – everybody has algorithms. But operationalizing analytics is not a commodity. Everybody is challenged with bringing analytics to life. When you deploy analytics in production, it drives value and decisions.
The game has changed. Data science teams are no longer measured by the models they build but by the business value they generate. If you can deploy and use the results of your algorithms faster and more strategically than others, you have an advantage. How can you gain that advantage? Develop enterprise-grade analytics processes that are scalable, flexible, integrated, governed and operational. These characteristics can be just as important as the algorithms themselves.
The second principle manifests itself in:
• Creating models with a deployment scenario in mind.
• Collaboration between data scientists and IT for faster deployment.
• Integrating analytics products with a visual suite of user-friendly tools.
• Model governance that integrates and supports open source programming languages and analytic assets.
Principle 3: Democratization of analytics; analytics for everyone
Digital transformation is an ongoing challenge that almost all organizations face. Data and analytics now play a strategic role in digital transformation. But you will not benefit from its impact unless data and analytics can scale beyond the data science team.
You need to enable analytics skills at all tiers of your organization, especially in those areas that have more domain knowledge that can be applied to analytics.
Making data and analytics available to everyone is crucial for successful analytics. I refer to this as “the democratization of analytics,” and it manifests itself in many ways:
• Visualization tools for low-code and no-code programming.
• Augmented analytics supports users through natural language processing and automation.
• Automation of data management and machine learning.
• Analytics and AI as supporting technology.
• Open source integration.
• Educational programs that broaden analytic skills.
Principle 4: Analytics differentiates
In a world where everyone has data, it’s what you do with that data that matters. How can you differentiate with analytics? You use analytics to identify what data has the most value. You build better models than your competitors. You deploy those models faster. And you use advanced analytics – like AI, optimization and forecasting – in the areas that most differentiate your company.
Most importantly, you have to keep asking yourself: Where can we improve with analytics? What markets can we disrupt? Where can we automate and support performance breakthroughs? Where can you bring analytics to connected devices or machines to profit from the Internet of Things?
If you are building customer intelligence models, how can you improve digital marketing through analytics and optimization? In retail, how can you optimize prices, markdowns, assortment, fulfillment and revenue with analytics?
The fourth principle manifests itself in many ways:
• Analytics applied to areas of the business where it will have the most impact.
• Data and analytics strategies that expand the successful use of analytics projects throughout the organization.
• A culture dedicated to digital transformation and analytical thinking.
• New business opportunities from monetizing data and disrupting existing systems with analytics.
Why do these principles matter to you? Because even as analytics evolves and your industry transforms, these principles stay the same. They provide an internal compass that can inform your approach to data and analytics and fuel your successful digital transformation.
Register to learn more about the principles of analytics at SAS Analytics Forum.
Oliver Schabenberger is COO and CTO at SAS.
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