In this article...
Key takeaways
Data analysis helps businesses make better decisions using mathematical techniques and Artificial Intelligence (AI) tools to extract and transform data into actionable information that improves overall business operations. Data analysis is a critical component of business intelligence and plays a crucial role in making data-driven decisions.
What is data analysis?
Data Analysis involves collecting raw data that is cleaned, transformed, and analyzed to find patterns or other insights to draw accurate conclusions and improve decision-making. Data analysis tools and techniques are used to find answers in raw data not readily apparent without going through the data analysis process.
What is the data analysis process?
Before actionable information is derived from raw data, the raw data must go through a multi-step process to become meaningful. The multi-step process to convert raw data is as follows:
1. Identify the issue or situation and ask questions
The first step is to ask questions about a specific objective and what the business wants to accomplish. This question is crucial because it can skew the remaining steps in the data analysis process if it doesn’t address the particular objective. Therefore, individuals involved must clearly understand the problem or situation to formulate a question.
2. Prepare and collect data
The data that is collected and prepared is relevant to the objective identified. Collection methods are surveys, interviews, observations, or extracting data from a data source such as a database, spreadsheet, or text file. The data collected can also be qualitative (non-numerical) or quantitative (numerical). Hence, the data collection method chosen needs to be able to address the objective you are trying to accomplish.
Once the data is collected, it may need to be prepared, if required. Data can be normalized, consolidated into raw data sets, or receive new attributes or dimensions, which are a few examples of data preparation.
3. Clean and process the data
Data cleansing is an essential step in the data analysis process. In this step, you’ll check the data for inconsistencies and errors that must be removed or corrected. The cleansing of the data validates the quality and reliability of the data. This step ensures the data will generate meaningful results during the analysis.
4. Analyze the data
After cleansing the data, you’ll use AI tools and mathematical or statistical techniques to find insightful information, such as trends, patterns, and relationships. Programming software like R and Python are used in data analysis. R is a statistical programming language that can help with data cleaning, analysis, and visualization. Python is a general-purpose language used for various tasks, such as data manipulation and machine learning.
In addition to using mathematical techniques, you can also use Machine Learning (ML) algorithms, Natural Language Processing, or Deep Learning, which are sub-categories under the domain of AI that can help find insightful information.
5. Share the results
The analyzed data are the results or findings that need to be shared with interested stakeholders. Interested parties must easily interpret the results; therefore, displaying the results visually with a chart, graph, or other visual representations is the best way to show results. Displaying data visually helps the audience better understand complex data while providing a clear picture of the results.
Data storytelling is another way to share results. Storytelling uses a narrative form that is easy to understand and can help a non-technical audience better understand the findings of analyzed data.
6. Act or report on results
The final step is to act on the findings by making a data-driven decision. The finding can also be a report that accurately updates a specific business operation or situation and is compared against an established Key Performance Indicator (KPI).
What are the methods used to analyze data?
There are multiple data analysis methods used to help businesses. The most common are descriptive, diagnostic, predictive, and prescriptive used in data analysis. Yet, other methods are as important and beneficial as the most common data analysis methods. These data analysis methods help extract information from databases, identify trends and patterns, optimize marketing campaigns, and improve operational efficiency.
Overall, data analysis helps businesses reduce costs, understand their customers better, boost revenues, improve security, and provide better problem-solving strategies. Some of the most helpful data analysis methods are the following:
Quantitative analysis
Quantitative data focuses on numerical data and uses measurements, mathematics, and statistical modeling to derive a numeric value based on the inputs. This type of analysis can be used for risk management, credit analysis, inventory, and financial decisions. Quantitative analysis is objective and uses concrete numbers that remove variability, making the results accurate and reliable.
Qualitative analysis
Qualitative analysis is subjected to interpretation. Qualitative analysis uses interviews, observation, surveys, case studies, and focus groups that can be ambiguous information that is difficult to measure. This type of analysis is ideal for getting input from groups of people that can help businesses understand their perspective. For example, a retail business can use qualitative research to understand a target audience’s purchasing preferences.
Predictive analysis
Predictive analysis attempts to predict future outcomes, using historical data to make projections about the future. Predictive analytics uses artificial intelligence, machine learning, and mathematical and statistical methods to predict the value of something or the outcome of future events, such as projected sales revenue, detecting illness, or weather forecasts.
Descriptive analysis
The descriptive analysis describes what happened while trying to answer the macro-level questions like Who, What, Where, and When. Descriptive analysis will use historical data to review and understand what has occurred in the past. For research, descriptive analysis uses statistical techniques like data dispersion, measures of central tendency to identify patterns, trends, summarizing data points, and relationships in data.
Diagnostic analysis
Diagnostic is used to find out why something happened or the root cause of an event. This analysis will use probability theory, regression analysis, clustering analysis, filtering, data drilling, data mining, and time-series analysis to find the why of an event. For example, a business shows two consecutive months of negative revenues, so the descriptive analysis provided this information but not the why.
The diagnostic analysis will evaluate all internal and external data sources with data mining and drilling techniques. After all the relevant data is collected, additional mathematical computations will be run on the accounting information to find out what is different about the last two months to help explain why.
Prescriptive analysis
Prescriptive Analysis focuses on how to make an event happen and is the most advanced type of data analysis. Prescriptive analysis can be used with any combination of descriptive, diagnostic, or predictive analysis, including all four to predict a future event a business purposely wants to happen. Prescriptive analysis uses artificial intelligence, machine learning, and any mathematical or statistical calculations that can influence a future outcome beneficial to a business.
Inferential analysis
Inferential analysis uses a sample size of data from a larger data pool or population. The smaller sample used in inferential analysis will be used to draw conclusions or predictions about the larger population. This type of analysis is a branch of statistics used to provide information about the larger sample size or population by only using a small sample size pulled from the larger data pool.
Statistical analysis
Statistical analysis is straightforward; it collects and analyzes large volumes of data to identify patterns and trends. Statistical analysis takes raw data to find correlations between variables that interested stakeholders can use to make informed decisions. Statistical analysis is helpful in many different businesses, such as health care departments, quality control departments of a business, weather forecasting, or sales tracking for retail organizations.
Text analysis
Text analysis involves machine learning techniques using computers to read and understand human-written text. Text analysis helps extract specific text from unstructured text data. Text analysis is also called content analysis since it can classify, sort, and extract information from text documents. Businesses can use content analysis to quickly digest and summarize online documents, which can help improve data-driven decision-making.
What are the challenges of data analysis?
Data quality is always an issue. Poor data can lead to erroneous decisions or misleading insights that can lead to revenue loss, incorrect treatment strategy for a hospital, or an unpatched cyber security vulnerability. A labor force that lacks the skills to select and understand how to use the correct data analysis tool can hinder the production of the most accurate data for decision-making.
The lack of a data governance board can impact data privacy, improperly validating data before it’s saved, and the uncertainty or looseness of a data management plan makes it extremely difficult to select the correct dataset for data analysis. Therefore, forming a data governance program needs to be a priority before considering a data analysis software solution.
Also read: Top Prescriptive Analytics Tools & Software 2023
Choosing the best data analysis software solution for your business
Whatever your industry-specific business does indicates the type of data analysis software solution you need to look for. If you work in a hospital or retail, find a data analysis solution focusing on that business type. If you can’t find a solution that addresses your business needs, you can use our data analysis software guide to narrow your choices.
Your final decision will be based on the three to five must-have analysis features you know your business will use consistently. Once you have validated the must-have analysis features, involve the staff in using the analysis tools to validate their usefulness.
Featured Partners: Business Intelligence
1 Domo
Domo puts data to work for everyone so they can multiply their impact on the business. Underpinned by a secure data foundation, our cloud-native data experience platform makes data visible and actionable with user-friendly dashboards and apps. Domo helps companies optimize critical business processes at scale and in record time to spark bold curiosity that powers exponential business results.
2 Yellowfin
Yellowfin’s intuitive self-service BI options accelerate data discovery and allow anyone, from an experienced data analyst to a non-technical business user, to create reports in a governed way.
3 Wyn Enterprise
Wyn Enterprise is a scalable embedded business intelligence platform without hidden costs. It provides BI reporting, interactive dashboards, alerts and notifications, localization, multitenancy, & white-labeling in any internal or commercial app. Built for self-service BI, Wyn offers limitless visual data exploration, creating a data-driven mindset for the everyday user. Wyn's scalable, server-based licensing model allows room for your business to grow without user fees or limits on data size.
FAQs
