Data Analytics Challenges & How To Overcome Them

There is no doubt that data analytics is changing how businesses grow and succeed, and it has impacted nearly every field. By collecting and analyzing huge volumes of data, companies are able to unlock unprecedented potential, from predicting new market patterns to optimizing security to reaching new demographics.

Collecting huge volumes of data and then embarking in the data reporting process can seem daunting, but it doesn’t have to be a tedious task. With the help of embedded analytics solutions, you can serve thousands of users with snappy and attractive analytics. These solutions can help your business thrive, since companies that successfully monetize their data can see up to 20% of total revenue generated by analytics-based offerings.

However, data analytics doesn’t come without a number of challenges. If a company doesn’t analyze big data well, it may end up losing money that it has invested in big data and data analytics.  The process of collecting and analyzing big data is complex and expensive, hence, failure to use big data effectively is like burying your money in the ground.

If companies don’t use the right approach when analyzing data, they will not get helpful insights that may help them make sound decisions. For data integration, ETL tools are the best in business.

In this article, we will be discussing the challenges of data analytics and how to solve them.

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Part 1: What Is Data Analytics?

Data analytics refers to the process of examining data sets to find trends and draw conclusions from the information they contain. Data analytics is normally done using specialized tools and software that combine machine learning algorithms, automation, and other features.

Data analytics tools and techniques are mostly used in commercial organizations to aid the decision making process. Scientists and researchers use data analytics to verify or disprove scientific theories, models, and hypotheses.

Data analytics has helped many companies to understand their customers better, design and develop better products, personalize their content, improve their advertising campaigns, and improve their bottom line.

With data analytics, you can take raw data and uncover insights to gain valuable insights from it. The information to be used for data analytics can be historical data or new data that is collected for a particular initiative.

The business can also collect data first-hand from its customers or visitors or purchase it from another organization.

Part 2: Data Analytics Challenges

The following are some of the data analytics challenges and how to solve them:

1. The Volume Of Data Being Collected

Today, most organizations are data-driven, and organization employees are normally overwhelmed by the amount of data that is being collected. An organization may be receiving data from each incident and everyday interaction, which leaves the data analysts with volumes of interlocking data to analyze.

To solve this problem, organizations should have data systems that automatically collect and organize information. Performing such operations manually is time-consuming and unnecessary in today’s world.

2. Lack Of Data Science Skills

Data analytics is a relatively recent field, hence, not many people have ventured into it. Due to this, there is the shortage of data science skills in most companies.

It is not simply about training people on how to use big data analytics solutions. Due to the interplay of factors, this can be a gap that can take years to fill.

To solve the lack of data science skills, most organizations turn into the use of automated machine learning to automate repetitive tasks.

With automated machine learning, data scientists can focus on business problems. However, automated machine learning is not the ultimate solution to lack of data science skills, but it can help teams to accomplish tasks when they lack adequate data science skills.

3. Collecting Real-Time Data

Due to the huge volumes of data generated by companies, it’s difficult to dig deep and extract insights that are needed most. When company employees get exhausted, they are most likely to focus on measures that are easy to collect rather than on measures that add value.

Additionally, if the employees are required to manually dig deep into the data, it may be difficult for them to extract real-time insights into what is currently happening. The use of outdated data can have a negative impact on the decision-making process of the company.

To solve this issue, companies should implement systems that automatically collect, organize, and automatically alert users of trends. Employees will then find it easy to generate reports and get answers to their problems.

4. Data Visualization

Data visualization makes data easy to understand. It is normally done using graphs and charts. Despite the usefulness of these tools, it’s hard for you to implement one from scratch.

Collecting information from multiple sources and putting it into a reporting tool can be frustrating and time-consuming. However, you can use strong data systems that generate reports at the click of a button. This will give employees the information that they need in an educational and appealing format.

5. Collecting Data From Multiple Sources

It’s always difficult to analyze data stored in multiple, disjointed sources. Data pieces are always housed in different systems. If employees don’t realize this, this will lead to inaccurate and incomplete analysis.

Combining data from multiple sources manually is time-consuming and it can limit insights into what can be viewed easily. With a centralized and comprehensive system, employees can be in a position to access all the data they need from a single location.

This can save the time spent accessing data from multiple sources, allow for cross-comparisons, and ensure that data is complete.

6. Sharing And Collaboration

Data analytics teams usually face the challenge of sharing and collaborating on a data analytics project due to security, inaccessibility, data transfer, and transparency issues. The problem becomes harder when there are remote teams that need to collaborate over distances, which may lead to data quality issues.

The solution to this problem is having a secure, centralized, cloud-based analytics portal that puts all analytics assets in one portal. This way, the teams can prevent data from being altered while in transit, or large pools of data from going offline.

Part 3: Use Hevo Data

Hevo Data provides its users with a simpler platform for integrating data for analysis. It is a no-code data pipeline that can help you combine data from multiple sources.

You can use it to transfer data from multiple sources into your company data warehouse for analytics. It provides you with a consistent and reliable solution to managing data in real-time, ensuring that you always have analysis-ready data in your desired destination.

Your job will be to focus on key business needs and perform insightful analysis using BI tools.

Conclusion

This is what you’ve learnt in this article:

• You’ve learnt more about data analytics.
• You’ve learnt the challenges that individuals and companies face when doing data analytics.

You’ve learnt how to solve the challenges facing the process of data analytics in organizations.

If you are interested in even more technology-related articles and information from us here at Bit Rebels, then we have a lot to choose from.

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