Prominent Data Analytics Challenges And How To Fix Them

Are you a marketer or data analyst struggling with the data analytics challenges encountered while carrying out significant marketing and analytical operations? If yes, we have got your back. Here we have acknowledged, highlighted, and discussed some of the most prominent limitations faced by marketers and data analysts while suggesting some reliable and applicable solutions for them.

So, let us get started and learn more about data analytics challenges and find an adequate approach, tool, or method to efficiently deal with them.

Brief Introduction to Data Analytics

Before starting the main discussion let us quickly go through the basics of data analytics and get an overall understanding of it.

Data analytics is essentially the process of synthesizing raw data to extract reliable and useful information. It helps businesses utilize data to enhance the effectiveness of their marketing efforts by making targeted approaches and data-driven decisions. The various approaches and methods adopted by analysts and marketers to perform this rather intricate and complex task include descriptive analytics, predictive analytics, prescriptive analytics, etc.

The involvement of enormous data sets and the complexity faced while dealing with them and transforming them into useful and informative pieces of data imposes various challenges.

“Tim McGuire of McKinsey has subsequently defined the data analytics challenges as deciding which data to use, handling analytics, and using the insights you have gained to transform operations.
While all these challenges are very real, they do not fully describe the big data challenge, which are choosing, synthesizing, analyzing, interpreting, and acting operationally and strategically.”– nist.gov

Common Data Analytics Challenges and How to Overcome Them

Let us begin discussing some of the widely faced data analytics challenges and learn how they can be dealt with optimum ease and efficacy.

Unbound Availability and Amounts of Data

The immense availability of data might not appear as a problem initially, but as you start processing or dealing with the unbound data sets, the limitations and challenges become increasingly apparent. With the continuous flow of data coming from various channels and sources, it becomes difficult for analysts to organize or comprehend data and fully utilize it to make adequate or precise predictions.

The best way to deal with this data analytics challenge would be to take the help of a data analytics software or platform. Such platforms come in handy while collecting, organizing, and categorizing datasets without any conflict or confusion.

Identifying Meaningful Data

Another data analytics challenge that is prominently faced by marketers and data analysts is the identification or extraction of relevant and real-time information from the overwhelmingly enormous pile of data. The manual handling of this task appears quite strenuous or exhausting and requires a lot of time and effort.

A reliable and high-end data analytics software will make this task seamless and simplified while enhancing the accuracy and efficiency of the outcomes. It will also help you create real time reports that will make it accessible to make yielding data-driven decisions.

The time and energy saved by using these AI-empowered tools can be invested in performing other significant business operations.

Comprehensive Data Visualization

The next step after processing data successfully, is to present the deduced results, statistics, or outcomes in a comprehensive and cohesive manner. It is another challenging part of the data analytics process. However, it can be easily dealt with by using advanced data analytics and visualization tools.

Such tools make data visualization more systematic, organized, appealing, and easy to understand. They are capable of presenting the KPI metrics that often go unnoticed during manual handling. You can also use these software or tools to present real-time data in an orderly manner.

Acquiring Data from Multiple Sources

Marketers generally have a tough time collecting data from multiple sources to learn about the customers’ demographics and gauge the presence or performance of the business across channels. It becomes challenging for them to keep monitoring the sources continuously to get their hands on high-quality data sets.

Relying on a data analytics tool helps businesses accumulate the required information, statistics, and data from all the relevant sources with unmatched ease and efficiency.

Data Inadequacy or Inaccuracy

While dealing with huge amounts of data, one of the biggest data analytics challenges is to identify and eliminate irrelevant and inaccurate data. Inadequate or inaccurate data can jeopardize the accuracy of the outcomes and eventually compromise the efficacy of the strategies and campaigns.

Data analytics tools counter this occurrence with remarkable ease as it automatically eliminates irrelevant or inaccurate data and focuses on useful KPIs and information.

Scaling Up Data Analytics Operations

Dealing with all the above-mentioned data analytics challenges simultaneously limits the scope, utility, and accuracy of data analytics and keeps a business from optimally utilizing the analytical resources.

Taking the help of a data analytics software helps broaden the scope of utility and expansion of a business’s analytical capabilities. It rules out the obstructions and makes the process more simplified, instantaneous, and yielding.

Conclusion!!

All in all, it has become apparent how using the right tools and software can prove instrumental in solving all the data analytics challenges. Going through the challenges in a detailed manner has helped us get clarity about the way they could be managed or handled.

“Advanced data analytics software and techniques can be used to transform big data into smart data for the purposes of obtaining critical information regarding large datasets. As such, smart data provides actionable information and improves decision-making capabilities for organizations and companies.”– Hariri, R. H., Fredericks, E. M., & Bowers, K. M. (2019)