Business Data Analysis- Different Types and Processes to Know
The intelligent collation and implementation of well-researched business data analysis into day to day operations can set the wheels in motion for data-driven decision making.
By collaborating historical sales and customer data across all facets of business operations, management can gain actionable insights that lead to greater profits, efficiency, and capacity for growth.
Understanding Data Analysis
Data analysis essentially sums up the collation of useful information to a business, which is then organized, processed, and ultimately analyzed through various methods to provide vital insights. In the world of business, this analysis of data is utilized to not only solve problems and increase revenue but also to better understand patterns and business predictions to ensure greater optimization.
Once implemented, the use of data analysis can have a multi-faceted impact on an organization's operations while positively impacting data-driven decision making. These effects can span from constructing targeted marketing strategies to utilizing identifiable patterns in demand. By understanding consumer behavior, businesses can build sales plans based on quantitative trends to avoid uncertainty.
4 Key Types of Business Data Analysis
Data analysis can be a convoluted and diverse process. Therefore, businesses should first look inward to identify their objectives and requirements for analyses to see which of the 4 main data analysis types will suit their situation.
1. Descriptive Analysis
This form of data analysis is all about looking at the past to better understand the future. In a nutshell, descriptive data analysis takes data from previous financial periods to draw conclusions for the current and future workings of a business. This type of analysis is used to great effect in garnering sales leads and tracking Key Performance Indicators (KPIs).
2. Diagnostic Analysis
Diagnostic data analysis is all about understanding why an event occurred, whether it was positive or negative, to understand how it came about. After pinpointing the causes and factors surrounding these scenarios, businesses can either take preventative measures or replicate the process. For example, diagnostic analysis is utilized to identify sales slumps or rises to help businesses employ the correct marketing strategies moving forward.
3. Predictive Analysis
Unlike descriptive analysis, predictive data analysis seeks to look forward, typically by using descriptive data. This enables a business to use past statistics and figures to predict what will happen in the upcoming quarter or year. The predictive analysis model can be used to forecast everything from market trends to ongoing revenue. By looking into the future, businesses can also assess potential risks in the supply chain before issues arise.
4. Prescriptive Analysis
This is oftentimes the most comprehensive of the 4 data analysis types. The prescriptive analysis method combines elements from the descriptive, diagnostic, and predictive models to enable businesses to formulate data-driven plans. During this process, businesses aim to create actionable insights that will positively impact decisions and the company's direction moving forward.
5-Step Guide To Analyzing Data
For businesses, information is power - which means the ability to efficiently and accurately process data can significantly affect an organization's ability to scale operations. By following the 5 general steps for data processing and analysis, organizations can harness and utilize the full potential of their datasets.
1. Ask the Right Questions
Before the data analysis begins, start by devising clear and measurable questions that seek to solve a specific problem. This could entail making adjustments to the number of staff, such as the feasibility of increasing the team or exploring the viability of implementing cost-cutting measures without negatively impacting quality.
2. Choose What to Measure and How to Measure It
Once the right questions have been identified, the next step in data analysis involves deciding what datasets are required to answer them. As one broad question can have multiple parts, it may be necessary to break the topic down further.
For example, when examining the feasibility of increasing staff numbers, it would be prudent to measure more than just the overall viability, but whether that increase would be maintainable during slow sales periods. It is also crucial to then decide how this will be measured, such as what unit of measurement and time frame will be used.
3. Collate the Data
Once the key questions have been set and the measurements and parameters defined, the data collection can begin.
Prior to collecting that data, however, businesses should explore what information can be gleaned from existing sources and devise a file storing system to help with task delegation and team organization. Through data and system integration, businesses can automatically collect and cleanse all information in a single database.
4. Analyze the Data
With the help of data integration software, the analysis can now begin. In this step, the data needs to be sorted and manipulated to plate up clear patterns, trends, and variations.
Using visualizations such as graphs and charts can clearly show the correlations in existing datasets. Depending on the findings from this step, businesses may need to revisit the previous steps to collect additional information such as market insights and big data.
Integration software can connect all existing systems such as the point of sale, inventory tracking, forecasting, and reporting software to create a free flow of information and automate the analysis process.
5. Interpret the Findings
Once the results are in it's time to understand precisely what they mean. During this final step, businesses should ask the following 3 key questions-
- Did the data analysis answer the initial question? If so, how?
- Did the data analysis disprove any objections? If so, how?
- What are the limitations of the study?
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