What is Data Analytics?
Data analytics is the process of extracting meaningful insights from disorganized information. Often compared to detective work, it enables businesses to recognize patterns, relationships, and opportunities.
Businesses looking to utilize data analytics must collect and process large sets of information. Once collected, businesses must integrate, cleanse and prepare it before creating models to predict results and effectively communicate results to stakeholders.
1. Data Collection
Data collection is an essential step, collecting accurate information from various sources in order to make informed decisions and predict probable outcomes. Doing this also saves both time and money as it prevents incorrect or inefficient choices being made.
There are various methods for collecting data, such as monitoring customer behavior, interviewing customers and conducting surveys. Selecting an effective data collection method depends on your business goals; APIs allow accessing external sources of data.
No matter your business objectives – whether that be optimizing marketing strategies, streamlining operations or developing new products – data analytics can assist with making more efficient and informed decisions. This means you’ll spend less on ineffective strategies, inefficient operations or misguided campaigns; find more potential clients more quickly to convert into customers faster; ultimately leading to higher profits and greater returns on investments.
2. Data Cleaning
Data cleaning is the process of identifying and correcting incorrect information before conducting further analysis, such as by eliminating duplicates, standardizing data for easier digestion by analysis tools, deleting missing points from analysis reports or filling gaps with unsuitable ones, filling gaps with inconsistent or inaccurate ones or reformatting inconsistent or inaccurate points into one comprehensive whole.
This step involves inspecting data to detect anomalies and inconsistencies. For instance, customers might respond differently than expected in an online survey regarding their age and marital status; or companies might have multiple locations that require different names for locations. It is crucial that this step takes place so the final analysis will provide reliable sources of information.
Data scrubbing involves the use of software that detects and eliminates errors automatically from raw data sets. This step can save both time and effort when preparing it for analytics, as well as costs associated with errors such as processing rogue records or spending extra time manually correcting them.
3. Data Analysis
Data analytics transforms raw numbers into insightful, educational insights that provide greater decision-making capability and more efficient problem-solving strategies. To do this, various processes such as data validation, profiling and cleansing must take place to ensure that the information used for analysis is reliable and ready for interpretation.
Once data has been cleansed and analyzed with tools that uncover hidden patterns and correlations, it can be compared with previous sets to determine trends and patterns that will allow companies to develop business strategies and enhance operations.
Descriptive data analysis is the most frequently employed form of analytics used in business, answering the question “what happened?” by summarizing past data in dashboards. Diagnostic analysis then investigates cause-and-effect relationships among variables to better understand why certain outcomes occurred, while predictive analytics utilize historical information to make predictions about future events or behaviors using time series analysis, statistical models, machine learning algorithms or time series prediction techniques.
4. Data Visualization
Data visualization is the process of taking analysis results and representing them visually to make them easy for anyone to comprehend, be it via table, graph or map.
Visuals help people notice patterns and trends that might otherwise go undetected when looking at rows of numbers alone. Furthermore, graphic presentations make your results much simpler to comprehend in context.
Visualizations often include graphs and charts that display data using lines, points or bars on an x- and y-axis to compare it. Some common visualizations include bar charts with rectangular bars for depicting your data, stacked column charts which divide columns by color to further breakdown the columns by data set, Gantt charts that represent timelines and tasks commonly found in project management settings, stacked column charts that divide each column further into additional sections by color as well as Gantt charts which depict timelines and tasks commonly employed in project management practices to compare data sets against one another.
Visualizing data efficiently requires striking a balance between form and function. Avoid any tricks that may mislead your audience, such as padding out certain segments with padding to make them seem larger or starting graph axes on values other than zero.
