Maybe you are new to AI and analytics. Or maybe you've been engaged in data and analysis for decades, even before we call it data science or decision science.

As the industry expands from statistics and analytics to big data and artificial intelligence, certain things remain the same.

We call these basic facts analytical principles. They provide the basis for our data and analytical methods and are reflected in our products and services.

We hope that sharing them here can also serve as a reference for your data and analytics and help guide your digital transformation and decision-making process.

The four principles of analysis are:

• Analytics tracks data everywhere.
• Analysis is more than algorithms.
• Democratization of analysis; analysis of all.
• The analysis is different.

Principle 1: Analysis follows data, analysis is everywhere

Data is a resource. If it is not analyzed, it is an unused resource. we often say, "Data without analysis is the value that has not yet been realized."

Whether your data is on-premises, in the public or private cloud, or at the edge of the network, you need to analyze it.

So naturally, whatever data you have, you need to analyze it.

The right analytical techniques

But what does it mean when we produce more data and more diverse data than ever before? All these data streams can be moved on many different networks.

The first principle of analysis is to bring the right analysis techniques to the right place at the right time. Whether your data is on-premises, in the public or private cloud, or at the edge of the network, you need to analyze it.

If the data moves to the cloud, the analysis moves to the cloud. If the data comes from the edge, the analysis is there as well.

The first principle is reflected in:

• Analytical technology is actively promoting the advantages of equipment, network routers, machines, medical equipment, automobiles, telephones, and other fields.
• Analytics is integrated with cloud storage and cloud computing.
• Software that supports cloud-native and local environments.
• Emphasizes data integration, data quality, data privacy, and data security.

Principle 2: Analysis is more than algorithms

You should pay special attention to the quality, robustness, and performance of the algorithm. But the value of analysis is no longer the characteristics and functions of the algorithm, but its value. The value lies in solving data-driven business problems.

An analytics platform is a commodity, and everyone has an algorithm. However, analysis is not a commodity. Everyone faces the challenge of turning analytics into reality. When analytics is deployed into production, it drives value and decision making.

Commodity analytics platform

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 algorithm results faster and more strategically than other algorithms, you have an advantage.

How do you get this advantage? Develop scalable, flexible, integrated, controllable and operational enterprise-level analysis processes. These characteristics are just as important as the algorithm itself.

The second principle is reflected in:

• Consider the deployment scenario when creating the model.
• Collaboration between data scientists and IT departments to speed deployment.
• Integrate analysis products with an intuitive, user-friendly tool suite.
• Integrate and support model governance for open source programming languages ​​and analytics assets.

Principle 3: Analyze Democratization; Analyze All

Digital transformation is an ongoing challenge facing almost all organizations. Data and analytics now play a strategic role in digital transformation. However, unless you extend your data and analytics beyond the data science team, you will not benefit from its impact.

You need to enable analytical skills at all levels of your organization, especially those with more domain knowledge available for analysis.

Making data and analysis available to everyone is critical to successful analysis. I call it "democratization of analysis," and it manifests itself in a number of ways:

• Visual tools for low code and codeless programming.
• Enhanced analytics support users through natural language processing and automation.
• Data management and machine learning automation.
• Analytics and artificial intelligence as supporting technologies.
• Open source integration.
• Expand educational programs for analytical skills.

Principle 4: Analyze differences

In a world where everyone has data, what matters is how to handle it.

How to distinguish from the analysis? You can use analytics to determine which data has the most value. You build better models than your competitors. You can deploy these models faster. You can then use advanced analytics in areas that best make your company stand out, such as AI, optimization, and forecasting.

Most importantly, you must keep asking yourself: in what ways can we improve our analysis? What markets can we destroy? Where can we automate and support performance breakthroughs?

Profit from the IoT

Where can you bring analytics to connected devices or machines to profit from the IoT?

If you want to build a customer intelligence model, how can you improve digital marketing through analysis and optimization?

In retail, how to optimize price, price reduction, classification, fulfillment, and revenue through analysis?

The fourth principle is manifested in many ways:

• This analysis applies to the most influential business areas.
• Data and analytics strategies extend the successful use of analytics projects across the organization.
• A culture dedicated to digital transformation and analytical thinking.
• New business opportunities are created by monetizing data and disrupting existing systems through analysis.

In Conclusion

Why are these principles important to you? Because these principles remain the same even as analytical techniques evolve and your industry changes. They provide an internal compass that can provide you with a foundation for your data and analysis methods and help you successfully perform digital transformations.