The volume of data you can source from different sources determines the insights you can gain about how effective your business processes are working. It can also position your team to collaborate in alignment with future trends.
Nevertheless, data collection does not produce any meaningful and actionable results without adequately analyzing it. You will only end up with numbers and figures with no basis.
However, there's no rule of thumb for analyzing data. Data analysis is based on your requirements and the form of data you wish to source. These factors will determine the methods you will adopt. That's the more you need to understand the form of data and the best outcomes.
Data Analysis is the process of fine-tuning, converting, and modeling data to generate meaningful and actionable insights that inform sound business decisions. Data Analysis aims at extracting vital information from data and implementing decisions leveraging the data analyzed.
Anytime you need to choose your life, there's the urge to examine what has happened or what will happen before concluding.
This act merely is performing an analysis of the past and future to conclude. For instance, you can recall nostalgia or memories of your past or your dreams of the future. This is simply Data Analysis. And that's the same thing you need to do in your business if you want to see growth.
In case you do not see growth, all you need to do is learn from your mistakes by first acknowledging them. You need to prioritize data analysis of your business data and processes.
We have different forms of data analysis based on technology and business.
Another name for Text Analysis is called Data Mining. This method is deployed to discover any pattern in large data sets utilizing data mining tools and databases. Text Analysis is leveraged to change raw data into business data.
You can find several business intelligence tools are available in the market, which can be used to make informed decisions. Most importantly, it provides a means of generating and examining data and discovering patterns before interpretation is made.
Statistical Analysis asks, 'What happened?' by relying on previous data in the dashboard forms. This form of Statistical Analysis incorporates data collection, analysis, interpretation, presentation, and data modeling. It analyzes data samples or a set of data. There are two forms of Statistical Analysis - Descriptive and Inferential Analysis.
This analyzes a sample of summarized numerical data sets or complete data. It depicts the mean and deviation for serial data while it shows the percentage and frequency for categorical data.
In this form of data analysis, you can cull several conclusions from the same data sets by picking various samples.
Diagnostic Analysis seeks to answer, 'Why did it occur?' by discovering the cause from insights in Statistical Analysis. Diagnostic Analysis is vital in identifying the behavioral patterns of data. If a new issue surfaces in your business operation, then you can delve into this Analysis to discover related patterns of that issue. That way, you can utilize the same prescriptions for that new issue.
This type of Analysis asks 'What's likely to occur' by relying on previous data. A good example is if last month, we spent $500 to generate 1000 likes on Facebook. If our ad spends increased to $1000, we can conclude that we should generate 2000 likes this month. However, it's not as easy as that; there's also a need to factor in other events such as Changes or Updates to Facebook Advertising or other factors.
Therefore, we can assert that Predictive Analysis forecasts future outcomes based on previous or current data. The forecast accuracy relies on how detailed the information you have sourced and how much you researched it.
This form of Analysis leverages insight from all data to decide on an action plan or resolve an issue. A lot of data-driven enterprises use Prescriptive Analysis as descriptive and predictive are not enough. It would help if you had more than Analysis to enhance data performance. Prescriptive Analysis uses current problems and events to analyze data and arrive at a decision.
Having analyzed your data, it is highly essential to represent the data in a graph, chart, and other visual formats. This is where Data Visualization comes in. It uncovers the relationships of the analyzed data using images. With Data Visualization, you can easily see trends and patterns.
So how do you hone your data analysis skills and enhance your decision making?
In your data analysis, there's a need to start with the appropriate survey questions that are measurable, clear, as well as concise. Tailor those questions so it can annul or disannul likely solutions to the specific issues or opportunity.
For instance, a PPC agency is experiencing rising costs, and it's finding it challenging to tender competitive contract proposals. One possible question to resolve this issue might incorporate: Can the agency downsize without compromising quality?
You can break this step into two sub-categories
A. Conclude what you intend to measure
B. Decide the strategy to adopt in measuring it.
Let's examine those two sub-categories
Going by the analogy of that PPC agency, you might need to examine the types of data required to answer salient questions. In this situation, you'd need to understand the number of employees and freelancers working with you. Their cost, as well as the percentage of duration they spend in the business operations.
This question alone will generate several sub-questions such as
Are we maximizing our workforce?
If not, what agile and process improvement can we leverage.
Lastly, when you are ready to measure, ensure you factor in any reasonable objections your team might have. For instance, how will the company cope with an increase in demand if the agency reduces staff?
It is highly important to ponder on how you will measure your data before the data collection phase. This is because your measuring procedure either enhances or jeopardizes your Analysis later—some salient questions.
What's the time frame - Annual or quarterly expenses?
What's the unit of measurement - USD or Euro?
What parameters should be incorporated? Annual salary or an annual salary, coupled with the cost of employee benefits?
Having defined the question and established your measurement priorities, you will need to keep these vital points in mind:
Before sourcing for data, decide on the data to be sourced from existing databases.
After you've sourced the correct data to resolve your question in the first step, the next stage is to go deeper into data analysis.
Start by converting your data in several ways, like plotting it on a graph, examining the correlations, or creating a pivot table in your Excel.
What is a pivot table?
This table enables you to sort and filter data over different variables to calculate your mean, minimum, maximum, and standard deviation of that data.
Data analysis software and tools are highly important during this stage. You can try Stata, Visio, Minitab, or Microsoft Excel.
As soon as you've analyzed your data, go ahead, and interpret the results. While interpreting your Analysis, bear in mind you can't prove the validity of a hypothesis. Instead, you can only accept it. This means that regardless of how much you source data, your results can be interfered with by unforeseen circumstances.
So you need to ask yourself as you proceed:
If the interpretation holds against all these questions and factors, you have possibly reached a productive conclusion. The only stage is to utilize the data analysis process results to make informed decisions in your business.
By adopting these 5 data analysis steps, you will make better and informed decisions for your business or agency. Your choices will be backed by data that you've carefully sourced and analyzed.
As time goes on, you will gain speed and accuracy. This means you'll be making better decisions to run your business effectively.