LUTZ BUSINESS INSIGHTS
4 data analytics tools your company should be using
tony desantis, data analytics director
If businesses wonder where they can obtain more information about customer insights, leaks in productivity and efficiencies, and how to increase their revenue, they need to look no further than the data they already collect each day. While many organizations understand that the data they collect contains vital information, some may be unsure of the simple tools they can use to analyze it. This article reviews four tools for businesses that want to incorporate data analytics into their overall business strategy.
For smaller businesses, there is no better place to get started with data analytics than the Excel spreadsheets you likely use every day. While Excel may seem like a less than momentous choice, it is familiarity and reliability are precisely the reasons why businesses should not overlook this tool. Many businesses already use Excel spreadsheets or at least have the package available, which means they will not have to invest money into another software product. In addition, the learning curve for Excel is minimal, so businesses can get up and running very quickly.
Microsoft has also made some enhancements to Excel over the years, which can help businesses construct a variety of analytics ranging from basic models to more complex forecasting. Two such enhancements include their Power Query feature and their Analysis ToolPak.
The biggest drawback of using Microsoft Excel for data analytics projects is its inability to scale; both in volume of data and the ability to connect multiple data sources. As businesses grow, they continue to collect data, requiring a robust, easily adaptable tool that can handle their data analytics projects’ increased size and complexity.
Data Visualization Tools
Data visualization tools are available for both smaller and larger organizations, with some visualization tools either already part of a larger software package or available as an independent application such as Microsoft’s Power BI or Tableau. Regardless, visualization tools are helpful in analyzing data through their ability to connect large volumes of disparate information and present it in useful formats such as a chart or a dashboard. These visual formats allow users to see both trends and patterns, as well as data anomalies.
The visualization tools today will also allow users to explore their data; allowing users to slice and dice data on the fly. As a result, users can ask questions of the data, identify answers, and highlight anomalies/areas that may need further investigation.
Robotic Process Automation
Robotic process automation (RPA) is an automated technology consisting of software bots or “digital workers” using artificial intelligence. While smaller businesses may have assumed these types of tools are out of their reach, that is not necessarily the case. There are a number of no or low code options available, making these types of tools available to organizations both large and small.
RPAs are capable of performing a multitude of functions, including moving data from legacy applications to a data store or repository, cleaning and standardizing data, saving file attachments to emails, and automatically combining files into a specified folder. By automating the process of aggregating data, RPA can free up resources so users can focus on performing analysis or reviewing analytics output.
Text Analytics Tools
Last but not least are text analytics tools. These tools are invaluable for a multitude of tasks. For companies who have large amounts of unstructured data such as emails, Word documents, call logs, service requests, or customer reviews, text analytics tools can help in the analysis process of these types of information.
Text analytics tools read and process text by way of Natural Language Processing (NLP). They can help determine whether a customer is happy or dissatisfied by identifying high-frequency phrases or words. These tools are also often found in chatbots, utilizing RPA processes, and they can also create written text from analysis of data through Natural Language Generation (NLG).
Selecting the Right Tool
Each company must determine what its particular goals are when it comes to developing a sound analytics strategy. The tools and techniques they employ will depend upon what insights they hope to discover within their data. Depending upon their strategy, organizations may chart a course of gradual expansion of data analytics processes or decide to take a more aggressive approach and employ a variety of tools and techniques to help them reach their goals. If you would like to learn more about leveraging the power of analytical tools to strengthen and grow your business, please contact us.
ABOUT THE AUTHOR
TONY DESANTIS + DATA ANALYTICS DIRECTOR
Tony DeSantis is the Data Analytics Director at Lutz. He began his career in 1999. He is responsible for interpreting and analyzing data, as well as designing report visuals in support of client engagements. In addition, he specializes in data management and the application of artificial intelligence to simplify business processes.
AREAS OF FOCUS
- Data Analytics
- Data Visualization
- Data Management
- Artificial Intelligence
- Forensic Analytics
- BS in Finance and Operations, Minor in Management Information Systems, University of Delaware, Newark, DE
- Junior Achievement, Volunteer
- Gilda's Club Chicago, Past Board Member
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