Top 10 Pain Points for Data Analysts working in the real world

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1. The amount of data being collected

With today’s data-driven organizations and the introduction of big data, risk managers and other employees are often overwhelmed with the amount of data that is collected. An organization may receive information on every incident and interaction that takes place on a daily basis, leaving analysts with thousands of interlocking data sets.

There is a need for a data system that automatically collects and organizes information. Manually performing this process is far too time-consuming and unnecessary in today’s environment. An automated system will allow employees to use the time spent processing data to act on it instead.

2. Collecting meaningful and real-time data

With so much data available, it’s difficult to dig down and access the insights that are needed most. When employees are overwhelmed, they may not fully analyze data or only focus on the measures that are easiest to collect instead of those that truly add value. In addition, if an employee has to manually sift through data, it can be impossible to gain real-time insights on what is currently happening. Outdated data can have significant negative impacts on decision-making.

A data system that collects, organizes and automatically alerts users of trends will help solve this issue. Employees can input their goals and easily create a report that provides the answers to their most important questions. With real-time reports and alerts, decision-makers can be confident they are basing any choices on complete and accurate information.

3. Visual representation of data

To be understood and impactful, data often needs to be visually presented in graphs or charts. While these tools are incredibly useful, it’s difficult to build them manually. Taking the time to pull information from multiple areas and put it into a reporting tool is frustrating and time-consuming.

Strong data systems enable report building at the click of a button. Employees and decision-makers will have access to the real-time information they need in an appealing and educational format.

4. Data from multiple sources

The next issue is trying to analyze data across multiple, disjointed sources. Different pieces of data are often housed in different systems. Employees may not always realize this, leading to incomplete or inaccurate analysis. Manually combining data is time-consuming and can limit insights to what is easily viewed.

With a comprehensive and centralized system, employees will have access to all types of information in one location. Not only does this free up time spent accessing multiple sources, it allows cross-comparisons and ensures data is complete.

I would add:

Creation of a data warehouse fed by modern ETL tools such as Power Query or SSIS provides a coherent data source that is much easier to report from than multiple, disparate data sources.

5. Inaccessible data

Moving data into one centralized system has little impact if it is not easily accessible to the people that need it. Decision-makers and risk managers need access to all of an organization’s data for insights on what is happening at any given moment, even if they are working off-site. Accessing information should be the easiest part of data analytics.

An effective database will eliminate any accessibility issues. Authorized employees will be able to securely view or edit data from anywhere, illustrating organizational changes and enabling high-speed decision making.

6. Poor quality data

Nothing is more harmful to data analytics than inaccurate data. Without good input, output will be unreliable. A key cause of inaccurate data is manual errors made during data entry. This can lead to significant negative consequences if the analysis is used to influence decisions. Another issue is asymmetrical data: when information in one system does not reflect the changes made in another system, leaving it outdated.

A centralized system eliminates these issues. Data can be input automatically with mandatory or drop-down fields, leaving little room for human error. System integrations ensure that a change in one area is instantly reflected across the board.

Data cleansing routines can help here, but they need to be kept up-to-date as source systems change. Therefore a tool that makes it easy to maintain these routines helps a great deal. Once again, Power query is a modern tool worth considering here.

7. Confusion or anxiety

Users may feel confused or anxious about switching from traditional data analysis methods, even if they understand the benefits of automation. Nobody likes change, especially when they are comfortable and familiar with the way things are done.

To overcome this HR problem, it’s important to illustrate how changes to analytics will actually streamline the role and make it more meaningful and fulfilling. With comprehensive data analytics, employees can eliminate redundant tasks like data collection and report building and spend time acting on insights instead.

8. Achieving good performance from systems with large datasets

For this it’s important to understand architectural issues, and appropriate techniques such as indexing and crafting high-performance data models.

9. Lack of time or resources to keep abreast of technology shifts

This is a management problem, but good hands-on training course can also help educate and inspire people under pressure. The best way to consolidate knowledge after a course is to start using the technology in a project as soon as possible afterwards.

10. Lack of understanding / experience in designing BI data models

This is a crucially important skill, which takes time and experience to learn, but pays big dividends when mastered.

Collated by JBI's instructors based on course delegate feedback from the following courses:

Power BI training course

Power BI Beyond the basics training course

Python Data Analysis training course

Tableau training course