Data Analytics 101
It is easy to assume that data and analytics is straight forward. This primarily is not the case. To truly understand data analytics one needs to investigate the different steps and strategies which are embodied in the entire process.
Qualitative & Quantitative
Here we look at the different kinds of data that can be collected and the strategies used to make sense of the data. Qualitative and Quantitative data separates the different types of data collected and each of them serve a particular purpose when it comes to making sense of data. This of cause depends on the nature of the expected outcome or the purpose of the analyses process.
Quantitative data is data that is expressed in numerical terms, in which the numeric values could be large or small. Numerical values may correspond to a specific category or label. Say for example you want to discover how many people in South Africa market their business on Social Media, how many of them are small medium enterprises or large corporations. That data will be displayed and expressed numerically.
“Information is the oil of the 21st century, and analytics is the combustion engine. Peter Sondergaard
According to the Migrant & Seasonal Head Start Technical Assistance Center, data that is represented either in a verbal or narrative format is qualitative data. These types of data are collected through focus groups, interviews, opened ended questionnaire items, and other less structured situations. A simple way to look at qualitative data is to think of qualitative data in the form of words. This type of data is a bit more abstract and focuses on behaviors and psychology of people who are on social media platforms. For example qualitative data will show you how these business owners interact on social media and what kind of content they put up to promote their products and services. A combination of the two sets of data is more likely to give a better understanding of the data and serve better when it comes to solving a business or marketing problem.
4 Data Strategies
We look at the four data strategies namely:
There are a variety of strategies for quantitative and qualitative analyses. Different strategies provide data analysts with an organized approach to working with data; they enable the analyst to create a “logical sequence” for the use of different procedures.
The fist strategy is visualizing the data.
This involves creating a visual display of the data which appealing and easy to understand. This of cause is the beginning stage of the analysis, by making sense of collected information it is easier to look into the necessary data to answer questions which need to be answered concerning your business or organization.
Secondly, Exploratory analysis.
Involves looking at data to identify or describe “what is going on?” This creates an initial starting point for future analysis. Exploratory analysis entails looking at data when there is a low level of knowledge about a particular indicator. The most general goal of trend analysis is to look at data over time. For example determining whether the number of businesses who are on social media has increased or decreased over the past five years and how quickly or slowly the increase or decrease has occurred.
The most general goal of trend analysis is to look at data over time.
For example determining whether the number of businesses who are on social media has increased or decreased over the past five years and how quickly or slowly the increase or decrease has occurred.
Lastly; Estimation is one of many tools used to assist planning for the future.
The data collected is used to predict what will follow or how behaviors and trends will change as time goes by. In the case of analyzing SMMEs and large corporations, you could predict the growth of SMMEs on social media platforms and how effective their impact will be. For example, results of a model to predict churn can be operationalized as part of a business process that includes the call center. The call center agent sees the results of the model and acts on it during a call—without even necessarily knowing that a predictive model was at work behind the scenes. Such capabilities have helped to drive the adoption of predictive analytics. Although it has taken some time, predictive analytics is finally becoming a mainstream technology.( Migrant & Seasonal Head Start Technical Assistance Center,2006)
In conclusion, to be successful in a competitive environment, companies must utilize data and analytics to its fullest advantage. As the amount of data continues to explode, enterprises are looking for ways to more effectively manage and analyze it for a competitive advantage.