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Excel models won’t get you a corner office (but these new tools might)

New AI-powered techniques are pushing business intelligence to the next level, and now you don’t need to know Python to use them.

New AI-powered techniques are pushing business intelligence (BI) to the next level, and now you don’t need to know Python to use them.

According to Forbes, 90 percent of the world’s data was generated in the last two years, with 2.5 quintillion bytes of data being created each day. This growth of data means data analytics is becoming more complex than ever.

Read this report to find out how new AI techniques and tools help overcome issues with data preparation, as well as help produce deeper and more accurate analysis with no coding or data science knowledge. Specifically, the report dives into the following:

  • Six factors that hinder data preparation
  • What prevents organizations from leveraging AI techniques and how to overcome them
  • How to choose a BI tool that fits your operational needs
  • Why and how data analysts can be more strategic than tactical
  • New ways for data analytics to support strategy and operations

Data Preparation and Insight Data Science: Moving On From Outdated Methods

Excel has been around for a while, and for good reason. Since its launch in 1985, the Microsoft program has had numerous updates and versions, but the essence of Excel remains the same. It allows users to organize data and use formulas and functions to perform calculations and analyze the numbers.

However, Excel’s time has long passed for data analysis purposes. It and other outdated methods for data work put organizations at risk. Since making a simple modification like modifying an end time requires a multitude of other changes, spreadsheets are not only time-consuming. They also put the integrity of your data at risk. According to an IBM report, 88 percent of all spreadsheets contain errors.

Data inaccuracy can endanger lives as well as organizations. For instance, Public Health England (PHE) – tasked to collect and analyze swab tests of the public – chose to use Excel to manage the associated data. Some labs sent swab test results to PHE in the form of comma-separated values (CSV) files, a commonly used way to deliver data.

Unfortunately, PHE used Excel to collect data from labs, and Excel files limit the number of rows they can have. For example, the older XLS sheet format could have up to 65,536 rows. Meanwhile, the XLSX format could handle 1,048,576.

When PHE loaded the CSV files to their Excel files, rows that exceeded the capacity of their Excel files were cut off. As a result, more than 15,000 cases were missed – and more than 15,000 people went about their daily lives, unaware they had the COVID-19 virus and were potentially infecting others.

Now, closer to home, imagine relying on such an insecure, inaccurate program to guide your daily operations and business decisions. However, there is a solution. Our free report dives into the importance of data science and data analytics and illuminates the pitfalls of using Excel or Tableau for data analytics and visualization. We also detail the tools your organization should invest in that can better power your decisions and data operations, all while reducing expenses and increasing ROI.

Keep reading for a preview of what you’ll find in our report, or fill out the form on this page to get a copy sent to your email.

Data Science for All: Underpinning the Need for Democratization and Demystification of Big Data

The ability to chop data into more easily digestible insights has long belonged mostly to data scientists and others in this field. For this reason, data scientists became in huge demand in the mid-2000s, and the career path continues to be popular.

Big Data analytics has reshaped the BI landscape and will continue to do so. However, data preparation capabilities must grow and evolve to keep up. There’s also a growing need to demystify and democratize data science so that it does what businesses need it to do: guide business decisions to mitigate risks and increase profits.

At the same time, organizations also need to be mindful of costs, onboarding time and other pitfalls of introducing new tools into the mix. For this reason, some companies stick with outdated and limited programs such as Excel for data analytics and visualization. However, these tools have not evolved to handle data that is constantly in flux.

For this reason, businesses either stick to the status quo by having the same imperfect analysis and poorly supported decisions or implement data science via conventional BI tools.

However, there are many problems associated with the second path, which include:

  • Complex and confusing app integrations that are difficult to understand.
  • Too much time and resources spend on custom code.
  • Traditional BI tools require heavy human labor.
  • Lack of collaboration options.

Traditional BI tools are best for long-term or strategic planning and decision-making, as it is too slow and cumbersome for mission-critical decisions. This makes it less than ideal for operational intelligence applications, so to address these challenges, data science for all, which makes it accessible even to those with no coding experience, is the way forward.

Keep reading our report to learn how Ikigai is making this happen and what BI tools you need to incorporate into your workflows to meet your operational needs.

No-Code and Low-Code BI Solutions  

Faster data extraction and preparation enable faster decision-making. Fortunately, new AI techniques and tools have been developed to make this possible, not just for industry behemoths with gigantic budgets and a deep pool of data scientists with Insight Data Science fellowships.

One of the keys to making data science more accessible and cost-effective while ensuring clean and timely data is a no-code or low-code BI platform.

This platform allows data teams to custom-build applications that empower them to better serve the management teams.

With No-Code: Those with minimal developer skills can build a fully functional analytical application without writing a single line of code. Instead, they just drag and drop pre-made components.  

With Low-Code: More skilled team members can code directly within the application to customize workflows and add functionalities, whether it’s to gather forecast data or manage inventory.

This type of platform allows for greater collaboration with team members of varying coding experience and skill sets. For instance, a data scientist can create the framework for the app and then pass the project over to the developer for additional changes.

Due to the fact that you can build applications more quickly and further enhance them with minimal time and resources, you can make better decisions faster. What’s more, with a no-code and low-code BI platform, you can:

  • Build data-driven interactive web applications in a production environment quickly and cost-effectively. Therefore, you can shorten your time to market without compromising on quality and functionality.
  • Create apps that automate everything from gathering forecast data and creating daily purchase orders to generating easy-to-understand reports for tracking purposes.

Leverage Cutting-Edge AI Techniques With a Low-Code BI Tool  

Ikigai is a cloud-native BI platform that allows operational teams to speed up the timing and accuracy of their decisions. Ikigai helps businesses leverage data to meet their goals based on MIT research on AI and machine learning.

Learn more about leveraging data science to drive your organization forward and which tools you need to support your business strategy. Book a demo today.

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