The question, what are the biggest challenges faced as a data analyst, is a big step to take when making a solution-driven inquiry into the business of data analysis. In years before even up to this moment, analysts are constantly faced with quite a lot of challenges and that alone has presented data analysis in a new face to the world outside it.
Data analysis is the technical process of critically inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making in any organization in useful ways.
According to Coursera, one of the world’s biggest online institution for IT skills, data analysis is the practice of working with data to glean useful information, which can then be used to make informed decisions. Companies are wisening up to the benefits of leveraging data. Data analysis can help a bank to personalize customer interactions, a health care system to predict future health needs, or an entertainment company to create the next big streaming hit.
Also, in terms of the relevance carried around by this professional situation, the World Economic Forum Future of Jobs Report 2023 listed data analysts and scientists as one of the most in-demand jobs, alongside AI and machine learning specialists and big data specialists.
In data analysis, there are important steps involved in the process of carrying its assignments out. As economically and socially relevant the subject is, it is not some skill that is earned without due training and professional experience as the processing of it requires quite a lot of procedural steps outline below:
- Identify the business question you’d like to answer. What problem is the company trying to solve? What do you need to measure, and how will you measure it?
- Collect the raw data sets you’ll need to help you answer the identified question. Data collection might come from internal sources, like a company’s client relationship management (CRM) software, or from secondary sources, like government records or social media application programming interfaces (APIs).
- Clean the data to prepare it for analysis. This often involves purging duplicate and anomalous data, reconciling inconsistencies, standardizing data structure and format, and dealing with white spaces and other syntax errors.
- Analyze the data. By manipulating the data using various data analysis techniques and tools, you can begin to find trends, correlations, outliers, and variations that tell a story. During this stage, you might use data mining to discover patterns within databases or data visualization software to help transform data into an easy-to-understand graphical format.
- Interpret the results of your analysis to see how well the data answered your original question. What recommendations can you make based on the data? What are the limitations to your conclusions?
Possible Challenges or Problems to be Confronted by Data Analysts
To answer the what are the biggest challenges faced as a data analyst question, below are the kinds of challenges often faced by data analysts both in the past-present and the future:
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Data Quality
One of the biggest challenges in data analysis is ensuring the quality of the data you are working with. Data quality refers to the accuracy, completeness, consistency, and relevance of the data for your specific purpose. Poor data quality can lead to inaccurate results, misleading insights, and wasted resources. To avoid this, you need to implement data quality checks and procedures throughout the data lifecycle, from collection to cleaning to analysis. You also need to use reliable sources, validate your assumptions, and document your methods and findings.
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Data Visualization
Second challenge in data analysis is presenting and communicating your data effectively. Data visualization is the art and science of creating visual representations of data, such as charts, graphs, maps, and dashboards. Data visualization can help you explore, understand, and communicate your data in a clear and engaging way. However, data visualization can also be challenging, especially when the data is complex, multidimensional, or dynamic. To overcome this, you need to use data visualization tools and principles, such as choosing the right type of chart, using colors and labels wisely, and telling a story with your data.
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Data Skills
The third challenge in data analysis is keeping up with the evolving data skills and competencies. Data skills are the abilities and knowledge that enable you to perform data-related tasks, such as data collection, manipulation, analysis, and visualization. Data skills are in high demand and constantly changing, as new technologies, tools, and methods emerge. To overcome this, you need to update and expand your data skills regularly, by learning from online courses, books, blogs, podcasts, and mentors. You also need to practice your data skills on real-world projects, challenges, and datasets.
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Data Integration
Another challenge in data analysis is integrating data from different sources, formats, and systems. Data integration is the process of combining data from various sources into a unified view, so that you can perform analysis across them. However, data integration can be difficult and time-consuming, especially when the data is heterogeneous, incomplete, or incompatible. To overcome this, you need to use data integration tools and techniques, such as ETL (extract, transform, load), data warehousing, data mapping, and data lineage. You also need to establish data standards, protocols, and governance to ensure consistency and quality.
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Pressure From the Top
As risk management becomes more popular in organizations, CFOs and other executives demand more results from risk managers. They expect higher returns and a large number of reports on all kinds of data. With a comprehensive analysis system, risk managers can go above and beyond expectations and easily deliver any desired analysis. They’ll also have more time to act on insights and further the value of the department to the organization.
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Data Mindset
Another kind of challenge in data analysis is developing and maintaining a data mindset. A data mindset is the attitude and approach that enable you to use data effectively and efficiently. A data mindset involves being curious, analytical, critical, and creative with data. It also involves being open-minded, flexible, and adaptable to changing data situations and needs. To overcome this, you need to cultivate a data mindset by asking questions, seeking feedback, testing hypotheses, and exploring possibilities with data. You also need to embrace failures, uncertainties, and opportunities with data.
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Data Ethics
Dealing with the ethical implications of your data is a challenge on its own. Data ethics is the branch of ethics that deals with the moral issues and dilemmas related to data collection, processing, analysis, and use. Data ethics can affect the privacy, security, consent, ownership, and fairness of the data and the people involved. To overcome this, you need to follow data ethics principles and guidelines, such as respecting the rights and interests of the data subjects, ensuring transparency and accountability of your data practices, and minimizing the potential harms and risks of your data.
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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.
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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.
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Support Challenge
Data analytics can’t be effective without organizational support, both from the top and lower-level employees. Risk managers will be powerless in many pursuits if executives don’t give them the ability to act. Other employees play a key role as well: if they do not submit data for analysis or their systems are inaccessible to the risk manager, it will be hard to create any actionable information.
Emphasize the value of risk management and analysis to all aspects of the organization to get past this challenge. Once other members of the team understand the benefits, they’re more likely to cooperate. Implementing change can be difficult, but using a centralized data analysis system allows risk managers to easily communicate results and effectively achieve buy-in from multiple stakeholders.
Types of Data Analysis
To identify the best way to analyze your data, it can help to familiarize yourself with the four types of data analysis commonly used in the field.
Descriptive analysis
Descriptive analysis tells us what happened. This type of analysis helps describe or summarize quantitative data by presenting statistics. For example, descriptive statistical analysis could show the distribution of sales across a group of employees and the average sales figure per employee.
Descriptive analysis answers the question, “what happened?”
Diagnostic analysis
If the descriptive analysis determines the “what,” diagnostic analysis determines the “why.” Let’s say a descriptive analysis shows an unusual influx of patients in a hospital. Drilling into the data further might reveal that many of these patients shared symptoms of a particular virus. This diagnostic analysis can help you determine that an infectious agent—the “why”—led to the influx of patients.
Diagnostic analysis answers the question, “why did it happen?”
Predictive analysis
So far, we’ve looked at types of analysis that examine and draw conclusions about the past. Predictive analytics uses data to form projections about the future. Using predictive analysis, you might notice that a given product has had its best sales during the months of September and October each year, leading you to predict a similar high point during the upcoming year.
Predictive analysis answers the question, “what might happen in the future?”
Prescriptive analysis
Prescriptive analysis takes all the insights gathered from the first three types of analysis and uses them to form recommendations for how a company should act. Using our previous example, this type of analysis might suggest a market plan to build on the success of the high sales months and harness new growth opportunities in the slower months.
Prescriptive analysis answers the question, “what should we do about it?”
