The art of choosing Excel, SQL or Python for Better CX

The art of choosing Excel, SQL or Python for Better CX

Take a look at how marketers can unlock the power of data analytics using traditional spreadsheet software and computing languages.

Marketers want to unlock the power of data – but with so many platforms to choose from, where do you start? If you’re in that boat, you might be asking yourself which data format will best help you analyze and visualize data related to customer experiences.

Let’s take a look at how you can streamline your analytics and visualization efforts and elevate your marketing strategy.

The ability to seamlessly move between data types has changed the game in recent years. With a plethora of tools, from cloud options to no-code and low-code applications, data management has become more versatile than ever.

But with so many options, it can be difficult to decide which one is right for you – Excel, SQL or Python. Each has its own strengths and limitations, but the new solutions available for intermediate data storage have created both new opportunities and confusion. To truly understand the impact of these developments, it is important to examine the traditional roles of Excel, SQL, and Python.

The traditional role of Excel

Excel, with roots back to Lotus-123 and VisiCalc, is the tool for creating data tables. The well-known table format is widely used by professionals in business, non-profit organizations and public agencies.

Even with the rise of Google Sheets, Excel is still the first choice for many. Its easy-to-use interface makes it ideal for developing basic data table structures, and acts as a whiteboard for organizing data. However, Excel’s manual workflow can be a disadvantage, slowing down decision-making and promoting silos of workflow techniques. Despite the addition of cloud integration and collaboration features, manual workflow can still be a problem.

The traditional role of SQL

SQL, like Excel, has a long history in organizations. It allows for well-defined relationships between and within tables, making it a powerful language for exploring data hosted in a backend computing environment.

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This leads to relatively effective data standardization. However, the rise of open source development has brought new accessibility to data structures, changing the way tables are accessed. With the use of apps via APIs, data requests became more frequent, leading to the emergence of new protocols such as noSQL and in-memory data storage.

However, the complexity of SQL’s table relationships can be daunting for analysts who need an easy way to curate their data. The influx of many data sources can increase the steps and complexity needed to deliver metrics for a dashboard, hindering rapid development environments that need to structure data explorations. This can lead to longer investigation times for product or service features with data than the teams have available.

Python’s traditional role

Just as open source development revolutionized SQL, it also rejuvenated programming languages ​​that work with data. Languages ​​like Python and R, although dating back to the 1990s, are relatively new to marketers. Originally designed for front-end or server-side computing environments, developers have discovered new ways to extend their capabilities by creating dependencies and introducing new data sources, for example through APIs.

This has enabled the use of statistical models and iterative analysis for predictive insights into customer behavior data, such as Markov Chain analysis to identify the likelihood of sustaining a service or trend.

This has made Python one of the most popular languages ​​in business intelligence. However, the success also has a downside for some analysts. The abundance of resources and tutorials on a wide range of applications, such as games, data visualization, and machine learning models, can be overwhelming for those without programming experience, making it difficult to navigate dependency and modeling choices. The abundance of options can make it challenging to choose the right computer product development in an extensive programming environment.

Related article: How AI-powered data improves CX

Development of data tools for CX

The roles of Excel, SQL and Python have evolved as analysts have gained access to tools that have expanded the capabilities of the platforms, allowing them to be used in new ways and in combination with each other. This has led to a cross-adoption of syntax concepts and data storage, as well as increased use of intermediate storage for data comparison and cleaning. Automation features have also been added to make it easier to access and work with data across different platforms.

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For example, tabular data is more often placed in staging for extensive comparisons, such as examining IDs for hashing rows in a data cleanroom. Features to automate cross-language access and calculations in SQL, Python, and R programming have been added. Libraries added to R programming allow SQL queries and Python syntax without changing the programming environment. Python has similar options. A flood of these dependencies facilitated repeated data access.

As a result, it has become more difficult to determine the specific skill needs of teams working with these languages. Do you really need someone dedicated only to SQL, or could the team that prefers Python get by with a few SQL skills?

What can these tools do to improve customer experience and marketing results?

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