Why let go of data munging? (Challenges and issues)

Around 20 years ago, when companies started seeing the value of data for business operations, data munging was developed and implemented in order to help the organizations make use of their data resources. 

This process consists of manual data management, including tasks such as extracting, collecting, integrating, consolidating, organizing, cleansing, transforming, processing, mapping, and storing data for the purpose of it to become available for future use in analytics, machine learning, and acquiring valuable business insights. Usually, this process is done with the usage of spreadsheets or other non-automated frameworks or tools. 

In theory, data munging is a very important process, which transforms data resources into impeccable and trustworthy tools, ready to be directly used for business purposes, such as planning and assessment of areas of improvement. 

However, in reality, that is not completely true. The data scientists see major issues with this flawed and already old data-management model. The challenges considered, are mainly linked to the fact that the munging process is entirely manual, and thus, impractical, unnecessarily complex, and time-consuming. 

• Lack of real-time data 

Let’s start with “time-consuming.” 

Huge loads of data are generated on a daily basis in every business entity, especially in industries such as Warehousing, Hospitality, Industry 4.0, and Retail. For those, the need for accurate and trustworthy analytics in real-time is crucial, in order for them to immediately cope with sudden problems, and occurred issues. 

In that sense, real-time data enables those companies to achieve better control over internal processes, assets, and operational efficacy, prevent any operational delays and faulty business operations, and as a result, minimize losses and optimize revenue streams. 

But when data management is done manually, all the volumes of data take lots of time to be extracted, cleansed and prepared for analytics.

As a consequence, organizations that have deployed data munging always have obsolete data resources on hand, missing the advantages of immediate analytics, which enable them to effectively track stock availability levels, supply chain processes, and many other essential metrics.  

Mistakes and quality issues

According to Harvard Business Review, “Bad Data Costs the U.S. $3 Trillion Per Year”. Globally, those numbers drastically increase, generating significant losses from wrong decision-making, planning, and risk evaluation processes motivated by low-quality data analytics. 

Manual data management is related to such quality issues, as well as to operational bias, which is usual for the work of every human being. This type of bias has the potential to result in crucial data errors, mistakes, outliers, inconsistencies, or even missing resources, which could have been effectively used. 

The risk associated with the usage of erroneous data is linked to low-quality analytics, untrustworthy sets, and misleading “data-driven” insights for business decision-making. 

And with the millions of automated tools for data collection, processing, and integration that exist nowadays, it is extremely easy to prevent the usage of inaccurate data. Taking that into account, the obsolete munging process becomes not only unnecessary but also irrelevant for the following decades.

Employee time misallocation 

Data munging is a task for data scientists and Chief Data Officers. And according to an article in Forbes, those experts tend to spend around 80% of their working time on munging activities, such as collection, cleansing, optimization of data, etc. 

Only the other 20% of the time is used for evaluating data analytics and communicating results and visualizations of data-based insights. Exactly this is one of the main challenges that CDOs face on a daily basis. 

With such time allocation, data specialists are stuck with daily tasks, which can be instantly conducted by automatic management tools. Data integration software, consolidation platform, and even ETL tools are outstandingly suitable to reallocate the tasks’ time of data experts, enabling them to focus on converting data into information and deriving insights from it, for the purpose of achieving business objectives. Besides, such a change would have a beneficial impact on the level of employees’ motivation, as they won’t be conducting tasks, which can be done automatically.

One step behind the modern competition

In 2020, dealing with data manually is directly linked to a lack of competitive advantage, especially in the industries where the majority of companies have adopted digital culture and use automatic data management tools. 

If we use data munging, lack real-time, high-quality data, and at the same time, our employee focus is on preparing sets for analytics, and not on the analytics and their business value itself, we cannot fully benefit from the potential that our data resources offer us. 

Such a lag, and inability to become competitive to the cutting-edge companies in our sector, eventually leads to productivity and performance issues, a smaller client base, lower customer satisfaction, and financial losses. 

Conclusion 

Data munging should stay in the past. And even though this practice has been useful at the beginning of the emerging data trend (long years ago), now it is only a trammel for effective performance and business development.