What is data analytics?
Data analytics is the process of inspecting data to find useful information about it. It’s how a business intelligence tool turns raw data into actionable insight. Data analytics is a multi-stage, iterative process, starting with data collection and ending when all possible insight has been pulled from the data.
The process includes steps like data transformation (where data is translated from its original format into more useful ones) and data cleansing (where inaccurate or incomplete data is removed from the data set). Some BI tools focus more on the ETL and data warehousing aspects of data analytics, while others focus more on modelling and visualization, but all BI tools do data analytics in some way.
Domo, like many other BI tools, focuses on turning its analytics into visualizations.
The rise of cloud-based business intelligence solutions has made data analytics far more accessible. Now, businesses don’t need expensive on-premise servers or data scientists to leverage the data they collect. With data analytics tools, any business can process the data it collects and use it to drive insights.
The steps of data analytics
Data analytics is a multi-stage process. Some of the steps overlap with other steps, and the whole process is iterative. Insights from one step of the process might necessitate redoing an earlier step. All steps of the process are important for ensuring the data produces insights that are accurate, relevant, and actionable.
Data collection
Business intelligence software has to have some way to collect the data that it needs for its analytics. Most BI tools have connectors or integrations that allow them to communicate with other types of software, and users can also usually upload data directly to the BI tool.
Data analytics is only as good as the data it has to work with. If a BI tool can’t collect all the data it needs to do its analysis, then its analysis won’t be very useful.
Data transformation
Once it’s been collected, data often has to be translated into a format that a BI tool can understand. BI tools can also process data, giving the data a structure that it might not have had originally. This helps the BI tool to analyze all the data from various different sources in the same way.
A business intelligence tool’s data collection and transformation components are often called its ETL suite. ETL stands for ‘extract, transform, and load.’ BI software extracts the data from its original software, transforms it into a format that the BI software can understand and analyze, and loads all the data from all of its sources into one place.
Connection options in Grow's integration suite.
Data cleaning
At some point in the ETL process, the BI tool looks through the data it’s collected to find errors. These incorrect, irrelevant, or incomplete data points are then discarded. Some tools allow their users to take an active role in this process—using a human eye to analyze data in this way is called ‘data wrangling.’ Other tools use algorithms and scripts to do it automatically.
Data analysis
Data analysis is the core of what a business intelligence tool does. At this point in the process, the BI tool applies various types of statistical analysis to the data, attempting to find trends, relationships, and patterns. Some tools only use fairly simple statistical models to find insights, while others use far more complicated techniques like neural networks and deep learning.
Some tools completely automate the data analysis process. Using these tools, employees can go straight from data collection to data visualization without any extra work. However, these tools often can’t find deep insights. Most tools allow users to take at least some part in the analysis process, which is more time-consuming and resource-intensive, but allows for more thorough analysis.
Data visualization
In this final step of the data analytics process, the analyzed data is presented to the end user in a graphical, easy-to-understand way. There are many different types of data visualization, and most BI tools allow users to present data in many different ways. Some tools offer graphically stunning visualizations, while other tools mostly ignore aesthetics.
Data analytics features
Business intelligence tools often allow users to interact with the data analytics process. These are some of the common features that data analytics tools have:
Querying and reporting
With these features, users can ask their BI software for specific information (make a query) and get a response back, often in the form of a formatted table of data (a report).
Sometimes, users may want to access information in their BI tool without building a data visualization; they may just want a quick answer to a simple question. Querying and reporting features allow users to find data quickly without going to all the work of putting together a dashboard or visualization.
Online analytical processing
Online analytical processing (commonly known as OLAP) is a complex process that analyzes how data relates to other data across multiple dimensions of analysis. This process allows users to see far more complex relationships than would be visible with traditional analytical methods.
OLAP lets users drill down into specific data points, showing a data point within a more complete context, and displaying more information than is available using surface-level analysis.
Predictive analytics
Predictive analytics is a type of analytics that uses some simple techniques, like data modeling, and some complex strategies, like data mining and machine learning, to make educated guesses about the future. It uses the data that a business has already collected to make guesses about what that data will look like in the future.
Predictive analytics has many use cases, from estimating projected audience numbers at a yearly concert to predicting the revenue for an entire company over the next decade. It’s one of the more useful tools that data analytics tools offer.
Using predictive analytics to build forecasts in Qlik.
Semantic analysis
Many businesses collect a large amount of data as text. Text can’t be easily analyzed in the same way that quantitative data can. Business intelligence tools need to use special techniques to glean insight from this data. Those techniques are known as semantic analysis.
These tools use algorithms and machine learning to figure out the meaning of text, and translate these insights into data points that can then be analyzed further. Semantic analysis can be especially helpful for companies that have a large social media presence or collect a large amount of customer feedback.
Some data analytics tools offer a similar feature called natural language processing. With natural language processing, users can interact with a business intelligence tool using simple text queries instead of whatever method the BI tool would normally use.
Without data, businesses can’t compete in today’s tech-focused environment. Data analytics tools help businesses access and utilize data, leading to insights at every level.
Our team of experts can connect any business with a tool that’s right for them. There are data analytics tools that can fit any use case and any budget. Sign up for a free consultation to learn more.
