Introduction 

What is your data telling you? How long does it take you to fully understand what’s in it? Do you really get deep insights from your data? Can you get full insight in one statement or do you have to take insights from different data sets?

This is what the process of analyzing data is all about. When the method of organizing data in columns and rows was computerized, it revolutionized what a computer could do with data. Data could easily be presented as charts and graphs within a few minutes and data-driven decisions could be made faster. Back then, organizations weren’t generating as much data, as fast as it being generated today. In fact, just having the opportunity to view their data in graphs that could easily be understood gave many organizations an edge over others who had not yet migrated into this new computerized data analysis.

Fast forward to today. The age of big data. Organizations have data coming from multiple platforms, multiple locations, multiple languages, multiple tax zones, etc. All this data is in different formats, different data sets, and different table formats. How challenging can this analysis be? Fortunately, this has presented an even bigger opportunity than most companies anticipated at the onset of modern-day AI and ML-driven data analysis. Our data quality platform at DQLabs not only makes analysis of such data very easy but also provides deep insights that weren’t visible before.

Beyond rows and columns, modern analysis platforms such as DQLabs allow the organization to maintain its earlier data formats where necessary and still get better and deeper insights.

Modern Data Management, Beyond Rows and Columns

Modern analytics has made the work of data analysis faster, and easier, and dramatically increased the accuracy and depth of analytical insights. A change from worksheets that are harder to keep track of workflows that are neatly displayed to ease the understanding of data sets. From the era of 10’s of manual queries just to get your data ready for analysis to the age of auto, intelligent data preparation. Modern data management covers every step of data analysis and makes it much easier for the analyst and the decision-makers. Below are reasons why you should stop the struggle with rows and columns, and move into modern analytics:

Data Cleansing

When working with spreadsheets, the job of data cleansing is repetitive and boring. Trying to keep track of where you’ve made adjustments while still mapping where you want the next task to take you can be overwhelming even to the best data analysts. The tasks of parsing, deleting rows, creating columns, and so on can make an analyst lose sight of what they started out to achieve. Not only do all these basic tasks take time from the important tasks but they also are an invitation to make the result-altering mistake. Duplications and miscalculations during the initial stages of data analysis interfere with the integrity of the data, leading to inaccurate conclusions. Data integrity is critical in any analysis exercise and should be maintained even when you change the format or even move it. With sole reliance on the traditional rows and columns spreadsheets operations, the possibility of maintaining data integrity is greatly reduced. However, with modern data management tools such as DQLabs.ai, this process is simplified to just a few clicks with a guarantee of no negative interference with data integrity.

Limited Input Options

With different departments and multiple data sets, the task of organizing data into usable sets can be daunting. Spreadsheets of rows and columns limit what you can include in your data without adding additional benefits to the data. This also limits the format of the usable data making it more difficult to analyze the data and create links between different data sets.

With modern analytics, the formats in which you can enter data are almost limitless and there are no worries of losing data while changing file formats. This ensures that no data is left behind and the integrity of the data remains protected.

Data Joins

Spreadsheets with columns and rows require altering the source material while joining data sets. This is a tricky process that will sometimes lead to damage to some data, in which case you have to restart the whole process again. This process is time-consuming; for instance, constantly calling on ‘VLOOKUP’ or ‘INDEX MATCH’ can slow down the process of joining data. Modern analytics will join data without the user having to be concerned about the format. Furthermore, the user gets to view the process and the ability to easily undo the joining process if they aren’t satisfied with the outcome.

Implementing Calculations

Worksheets with rows and columns will do their calculations from logical formulas normally employing the ‘IF’ statement which is fast. However, in times when you want to do multiple computations, the process of ‘copy-pasting’ formulas starts to feel too complicated and hard to keep track of. What if you could set up the formula once and utilizing it exactly where you need is much more effective. This is how modern analytics views the process of calculations; logic, data, and execution all exist in layers that are separate. With DQLabs you are also able to view the workflow with the option of going back and rethinking the logic.

Data Transformation

To better understand the data, you are analyzing when using spreadsheets you first decide on the parameters to use and then transform the spreadsheet to reflect your areas of interest. To achieve this, you have to manually transpose, filter, and sort your data hoping to get a clear picture. Because the process is tedious, you can easily miss some fields or some filters that would’ve played an important role. With modern analytics such as DQLabs.ai, this process is done automatically; all you have to do is query what you want to view and then get a simplified view of your area of interest.

Conclusion

In conclusion, the time to leave the struggles of rows and columns is now as modern analytics is already here and has transformed how many companies view and utilize their data. With modern analytics, the tedious tasks of data cleansing and data joining have now been transformed and the limits of formats removed. The process of joining data from different data sets is also made easy and requires just a few clicks from the user. Data integrity issues that come with spreadsheets have no space in modern analytics which accepts data in multiple formats. By migrating to modern data management, your data’s integrity is protected and the insights from your data are very accurate.