7 most frustrating data analytics challenges faced by businesses
Due to digitization, a large amount of data is generated. By now, most of us have realized the importance of implementing data analytics in our businesses. However, only 12% of the data collected is analyzed. And surprisingly, 63% of the employees fail to gather insights from the data collected in the desired timeframe. Why is it so? Why aren’t organizations able to utilize data? What are the data analytics challenges that most companies face?
7 top challenges in implementing data analytics
By 2025, we will generate 463 exabytes of data. If you want to utilize it and build a data-driven culture in your organization, you’ll need to understand the challenges in data analytics. And methods to overcome these data analytics challenges.
1. Collecting meaningful data
There’s data for every aspect of business, which more than often overwhelms employees. The plethora of data from different channels makes it difficult for employees to drill down and determine the critical insights. And they end up analyzing the data that’s readily available and not the one that truly adds value to the business.
Data literacy can easily mitigate most of the data analytics challenges, starting from collecting meaningful data. Once the employees start understanding data, they will know which data is vital for your business.
You can begin arranging basic training programs for your employees and encouraging them to participate in webinars and workshops. You can also hire an experienced data analyst who has both certification and knowledge of your industry. And would precisely know the data that can add value to your business.
2. Selecting the right tool
The second most common challenge in data analytics arises with the vast number of tools available in the market.
Is MongoDB the best for storing data or Cassandra? Is RapidMiner a better option for data analytics, or should you opt for Microsoft’s Power BI?
If these questions are not answered correctly, the chances are that you will end up investing your time, money, and effort in inappropriate tools.
The best option is to seek professional help. Experts who have experience working on different tools can help you to select the right one. You can also start using the trial versions of the tools to check out the features yourself.
3. Consolidate data from multiple sources
Data comes from scattered and disjointed sources. For instance, you will need to pull data from your website, social media pages, CRM portals, financial reports, e-mails, competitors’ websites, etc. The data formats of most of these reports will obviously vary.
Combining and analyzing them in one place is one of the common challenges in data analytics. It can turn out to be more complicated if done manually. And it also increases the chances of error, making the data unreliable.
Organizations should focus on creating a centralized data hub. It will become easier for employees to access information from that location. Thus, freeing up time spent on data collection and helping you compare data from across channels.
4. Quality of data collected
Nothing can be more dangerous in data analytics than incorrect data. If the input quality of data is flawed and erroneous, the output can never be reliable. One of the primary reasons behind inaccurate data is errors made during data entry, i.e., manual errors.
Another reason for poor quality data is the disparity in data. Suppose your data operator makes changes in one system and forgets to make the exact change in others; it will create asymmetric data.
The first check that you should put is at the data collection stage. If you have the budget, automate this process. Or you can use forms with drop-down fields and data validations. Thus, not leaving much room for human errors.
The challenge of asymmetric data can be solved by system integration – your systems should talk to each other. So, when a change is made at one place, it will reflect at all the different places where the same data is used. Having a centralized system also helps in improving the quality of data.
Learn about – How to build a BI strategy & roadmap for any business
5. Building a data culture among employees
According to a study, the biggest obstacle in becoming a data-driven company lies in an organization’s culture and not technologies. Only a meager 9.1% of executives have pointed out technology as a challenge in the path of data analysis.
Many times, though top-level understand the importance of data analysis, they do not extend the desired support to their employees. Constant pressure and lack of support from the top and lower-level employees are among the most significant data analytics challenges.
As Albert Einstein has said, “The world cannot be changed without changing our thinking,” you need to create a culture that understands data and supports it. To make data analysis a success at every level, educate your employees and help them to upgrade themselves.
To know how to establish a data culture among employees, read our 4 practical ways to become a data-driven business.
6. Data security
Once businesses realize the importance of Big Data, they start focusing on storing, understanding and analyzing it. They tend to overlook the potential risks that come with the privacy and security of the enormous data sets collected.
Security of your company’s data is a necessity and one of the scariest challenges in data analytics. Unprotected data sources can become an easy entry point for hackers.
The moment you decide to use data analytics in your business, make sure to take care of the security issues. Some steps that you can implement are:
- Hire cybersecurity professionals to guard your data.
- Conduct corporate training programs on big data for your managers and business owners.
- Use big data analytics tools.
- Control access rights.
- Encrypt data with secured login credentials.
7. Data visualization
Data analytics holds no meaning for you or your stakeholders until the numbers tell a story. After all, the time, money, and effort you invest in collecting and securing the data are to help you make informed decisions and meet your ROIs. So, data visualization is very critical in data analytics and challenging too.
Use data visualization tools like Power BI, Tableau, Google Data Studio, which are easy to learn and have a wide range of features. These tools have drag-and-drop features and can also connect to various data sources. They come with intuitive graphs and charts, thus helping you to visualize your data.
Final thoughts on the business data challenges
When we are aware of the problems, it becomes easier for us to deal with them. Now that you know the data analytics issues faced by businesses and their solutions, you can start implementing them in a more structured form.
And in the end, none of the challenges in data analytics are critical enough to stop you from utilizing the benefits of big data!