4 major Data Analytics Challenges And Their Solutions

Marketing Team
Published on April 13, 2021

Challenge #1: Insufficient Knowledge About Data Analytics

More often than not, companies fail to realize the rudimentary significance of data analytics which makes it all the more difficult to reap benefits from it.

In order to steer away from the risks that come along with data analytics, companies must have a clear understanding of the same. Investing in the best business intelligence and analytics tools doesn’t guarantee success – a clear, laser-focused strategy for using it does. The fact the company culture will undergo a lot of changes after data analytics is introduced isn’t surprising. In order to recognize the capabilities of data analytics and utilize them effectively, organizations should conduct training sessions and workshops for their employees. However, with self-service data analytics tools such as Microsoft Power BI, the extensive process of training is cut down short as anyone can make their way with it, despite their technological skills. Furthermore, to ensure that the analytics solution is proving fruitful, constant monitoring is crucial.

Challenge #2: Improving Data Quality

Data integration can seem overwhelming at some point as the information that needs to be analyzed is captured from multiple online sources with different levels of credibility.

When a standalone data-gathering tool is used to collect information from various online sources, chances are, it will pull up heaps of structured and unstructured information, regardless of its quality. Working with inferior quality data could produce inaccurate insights thereby, crumbling the efficiency of business operations that demand precision. Even though there are several options for cleaning data, the best solution would be to let a self-service business intelligence tool handle the entire course of analytics starting from collecting and sorting data to generating actionable insights. Your company’s data analytics model should essentially focus on two things – a) compare and validate the authenticity of the sources of data and b) match new data with existing records to segregate them under prevailing categories.

Challenge #3: Storing Large Volumes Of Data

Thanks to technology, the amount of data that companies have access to for running analytics, is increasing exponentially. Hence, businesses must procure data analytics tools that can match up with the pace of their rapidly expanding databases.

There are two ways of dealing with the increasing volumes of data. Organizations can both employ the modern techniques of compressing and de-duplication of data that ensure large sets of information are compacted into small, useful bits. Or, they can turn to a robust business intelligence tool that uses ML and AI capabilities to filter high-quality data, store them in tiers and expand its capacity as per the needs of the business without any additional hassle. As the second possibility hints at cloud-based, scalable BI platforms, users can rest assured that they will always have access to the latest version of data, presented in real-time at their fingertips.

Challenge #4: Translating Data Into Actionable Insights

Skimming through large volumes of historical data doesn’t guarantee accurate insights which may, in turn, lead to loss of revenue and loyal customers.

Market conditions and customer preferences are constantly changing. A product or service that was in high demand yesterday can be replaced by an item completely different today. Basing important decisions only on a few acumens from the past could prove costly. This is why, apart from tracking the latest shreds of information in real-time, it is also quite crucial to assign KPIs to a system of different data sources and factors. Analyzing the data from these networks will render some level of certainty about generating precise actionable insights necessary for decision-making.

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