Data Analytics is the process of analyzing raw data and developing insights or evidence-based conclusions based on the analyzed data. But what is raw data? Raw data is all of the data that is collected but is not yet processed or analyzed. In its raw form data can only do so much, but through the science of data analytics, raw data becomes useful for decision making in many fields, marketing in business being one of them. Marketing heavily depends on identifying target audiences and that information is gleaned from the process of data analytics. Data analytics has been largely automated and there are various software resources that do the heavy lifting in analyzing raw data. Data analysis is critical for businesses that want to effectively market their products and services. The end result of thorough data analytics is the ability to use that data to make informed decisions that will optimize many facets of successful marketing.
Importance of Data Analytics
Data analytics is a crucial process for businesses to involve in their overall business and marketing strategy. The use of data analysis gives companies the chance to leverage the received analyzed data to optimize their business practices and increase their effectiveness and efficiency. Without the use of data analytics, identifying critical factors in marketing such as target audiences would take a much longer time and may not be as accurate or effective. By using data analytics companies can cut costs, identify important aspects needed in reaching the right consumers, develop better products and services, and make more sales. Data analytics is the pathway to optimization in business and ultimately, successful day to day efficiency.
Steps in Data Analysis
Data analysis is essentially a precise science and the following steps must be taken in order to gather, process, and analyze data correctly:
1. Grouping Data: First and foremost, the desired data must be identified and divided or grouped into certain sectors such as age, income, gender, or demographics.
2. Collection: The next step is to gather the data and collect it from the source. The sources in data collection may vary from computers, personnel, pictures and videos, the internet, and more.
3. Organization: Thirdly, the collected statistical data must then be organized which can be achieved through various means such as computer software programs or something as simple as a spreadsheet.
4. Cleaning: Before sending off the data to an analyst, the data must be “cleaned” or “scrubbed”. This is the process of checking for any errors in the collection and organization of the date such as incomplete data or duplicate data.
Types
Because Data Analytics is a science it can be a complex process that is broken down into parts in order to better understand the data and what type of data is being analyzed. The four main types of data analytics are:
Descriptive Analytics: This data analysis takes data from a set time period and analyzes what has occurred and how the data has fluctuated, or not, in that time frame. It is useful in identifying trends, clicks, views, and other aspects of change or lack thereof in a certain time period.
Diagnostic Analytics: This type of data analysis attempts to answer the “why” in the results of the collected data. It aims to give insight into why certain factors occurred the way they did and how that data affected the given outcomes. Diagnostic analysis is certainly more complex in nature than simply crushing the numbers.
Predictive Analytics: As suggested, predictive analysis attempts to predict or make a highly educated prediction based on algorithms and other stats to determine what may happen in the near future.
Prescriptive Analytics: Prescriptive Analytics takes all of the information from the previous types of analytics used and aims to suggest the next step that should be taken in order to achieve a certain result. Based on all the information collected, prescriptive analytics recommends what direction or directive a company should take.
Methods of Data Analytics
There are many different techniques used in data analytics such as the Monte Carlo Simulations or Cohort Analysis, and many more but here are three of the most well known and used method in data analytics:
Regression analysis: This technique seeks to understand and analyze the relationships between a dependent and an independent variable(s). It attempts to show how the changes between the different variables affect each other.
Factor Analysis: This method consists of taking a large set of data and compressing it into a smaller set of data that is more easily analyzed. This allows for a deeper understanding of the data and picking up on anything that could have been overlooked or more challenging to identify in a massive set of data.
Time Series Analysis: Widely used in the field of finance, time series analysis follows data points over time in order to identify trends, cycles, and patterns and the driving force behind these rhythms.
Tools of Data Analysis
Data Analytics has evolved tremendously and the process has been simplified and expedited significantly by the use of technology. Traditional data analytics involves using mathematical and statistical equations that very well could be done by hand, but it is tedious and time consuming. Fortunately, now there are various software programs that can complete most of the work in a fraction of the time and with more accuracy. Using spreadsheets on Microsoft Excel is one way to store large amounts of data, but there are also many platforms that crunch numbers and perform the various techniques listed above. Some examples include Python, R, Tableau, Power Bi, Rapid Miner, Apache Spark, Talend, Splunk and many more.
Data Analytics Key Takeaway
Data Analytics is the science of processing and analyzing raw data resulting in data-backed evidence that helps businesses make more informed decisions. Data analytics is used across the board in marketing, finance, healthcare, and other fields all with the main goal of optimizing effectiveness and efficiency. Data analytics is completed through a series of steps that are crucial in gathering the right data and then analyzed through various techniques. With the results of the data analysis process, the best course of action is then decided. All of this can be done seamlessly with the help of automated data analytics technology. Data analytics is an ongoing process that aims to refine how businesses are run and specifically how marketers use data.
