Big data is changing the way that businesses make decisions. Government agencies, enterprises, and centers of education are gathering data from many sources to help the organizations operate efficiently and boost sales. Let’s look at how they do it and how to collect reliable data you can use – no matter what the size of your business. From accounting to forecasting and KPI-tracking, any business relies on data. Ensuring that your data is reliable is the first step to setting realistic goals.
On this page
What Is Business Analytics?
Fundamental principles and some background
Business data tracking and business analytics focus on the macro business implications of the data that is being collected. It helps decision-makers determine what actions they should take based on the information gathered.
Business data analytics combines tools, applications, and skills that allow businesses to gauge and improve how effective their core business functions are. These include IT, marketing, sales, or customer service.
Business data tracking and business analytics can be divided into predictive, prescriptive, and descriptive categories. Descriptive analytics help a business understand what has happened. Predictive analytics and machine learning allow companies to anticipate how likely it is that a future event will occur. Prescriptive analytics explore possible actions a business can take based on results from their descriptive analysis and predictive analysis.
Business data tracking requires volumes of high-quality data. Two of the challenges businesses face with business data tracking are how to collect reliable data and how to keep data secure in a business.
How to Collect Reliable Data
Big data is difficult to interpret in its raw form. However, when using the right software, data visualization lets you see and interact with your data in an easily digestible dashboard format. It takes tabular data with x-rows and n-columns and transforms it into heat maps, pie charts, bar diagrams, and graphs.
You can manipulate critical data and see how it impacts the rest of the data. Visualization gives your business the opportunity to discover solutions quicker, improve decision-making, and explore potential patterns.
Collecting data for data visualization is a seven-step process:
1. Develop your research question. You need to understand the goal of your research. This will tell you the type of data analytics that are necessary.
2. Collect or create data.
3. Clean your data. Clean data is anomaly-free. Starting with the cleanest data set possible allows you to focus on creating compelling visualizations instead of trying to fix issues while creating visualizations. The tasks for cleaning your data will vary based on the data set you are using. It will include getting rid of unnecessary variables, dealing with missing values, addressing invalid data, and deleting duplicate data.
4. Choose a chart type. Find the chart that best communicates the message you are looking to transmit. Do you want a chart that shows a comparison between variables? Are you looking to establish the relationship between variables or patterns in data?
5. Choose the software you will use. Some platforms are available for free on the web. There are also licensed tools.
6. Prepare your data. How you prepare your data will change based on the visualization or chart you want. You will need to convert values into units appropriate for your chart, create aggregate values for groups, and extract values from complex columns.
7. Create a chart. The process used will vary based on the software you choose. You can refine your chart based on design principles, increasing its effectiveness.
Let's help you make sense of business data traking
This blog article is not intended to be the rendering of legal, accounting, tax advice or other professional services. Articles are based on current or proposed tax rules at the time they are written and older posts are not updated for tax rule changes. We expressly disclaim all liability in regard to actions taken or not taken based on the contents of this blog as well as the use or interpretation of this information. Information provided on this website is not all-inclusive and such information should not be relied upon as being all-inclusive.