One of the main challenges businesses face with ever-accumulating masses of data is to find ways of making it available to those who can derive business value from this information. Self-service data analytics is a powerful method that enables users of all backgrounds and skill levels to quickly and easily gather insights from data. Even end users who are not formally trained in data analytics can use self-service analytics tools to access, explore, and visualize data, as well as create reports based on the information. Self-service analytics tools provide non-technical users with key insights at the moment when they’re most needed and offer informed guidance recommendations. This in turn increases data literacy for all employees who interact with the data.
Common Uses of Self-Service Data Analytics
Self-service analytics tools have a variety of uses across industries. Self-service BI tools help users grasp what’s happening at their organization, and self-service predictive analytics tools aid in painting a picture of why it’s happening so that hypotheses can be developed for further predictions.
Here are just a few uses of these powerful tools:
- Data visualization: User-friendly dashboards are available for trend identification and problem-solving endeavors.
- Operational Tools: These tools are used to aid in operational decisions and to provide insight into reports.
- Customer Service: CRM packages, as well as other software, enable users to view customer data in order to pinpoint trends and areas for improvement.
- Statistical Analysis: Reports and other statistical tools can be adapted to perform a variety of tasks, like analyzing data, running models, and offering conclusions.
The framework that’s created using these self-service analytics tools makes it possible for those who may not have formal training with IT or data management to draw insights from large stores of information.
Benefits of Using Self-Service Data Analytics
The use of self-service data analytics provides companies and organizations with many benefits that allow them to work more efficiently and effectively in a data-driven culture. Here are just a few of the perks of using self-service data analytics:
- Quick Insights: Self-service data analytics cuts down drastically on the hours spent corresponding with IT teams to field report requests. Users can instead generate their own insights without the need for as many IT requests or requirements. This means that businesses have more time to explore data, evaluate insights, and come up with a decisive plan of action.
- Increased Data Literacy Across Your Organization: Once employees are trained to improve their analytical skills, they have the power to read, manipulate, and analyze data.
- Democratization of Big Data: In order for big data to be democratized, it must be used by a majority. Self-service analytics is progressing toward this goal by raising awareness among users who are or will be involved with self-service analytics.
- Cost Reduction: While BI software licenses are a legitimate expense many businesses must budget for, they generally are much lower when compared to the money spent to pay an IT staff to support and maintain analytics solutions. This is why companies that use self-service reporting spend considerably less on internal IT support than those that do not use this technology.
- Teamwork: Data science teams can work with self-service analytics users to achieve the best results in the most streamlined manner. This involves business users taking initiative and helping themselves via self-service, while the data science team can build from their input and use it toward more complicated tasks and advanced analytics.
Relying on self-service data analytics not only reduces operational costs but can provide quick insights that can be accessed by all members of an organization or team.
Drawbacks of Using Self-Service Data Analytics
In addition to the various benefits of incorporating self-service data analytics into your workplace, there are a few challenges to be aware of as well:
- Lack of Necessary Training: For self-service practices to be successfully implemented, it is important to select the right people and to provide them with training on how to use self-service tools. Without this training, wrong decisions or negative results can be reached by users.
- Inconsistency of Data: It is vital to ensure data consistency before self-service can be implemented. Data inconsistencies can yield an inconsistent or erroneous output.
- User Adoption Barriers: The more an end user must go back and forth between applications to find insights in data, the higher the risk of human error.
- Data Governance Concerns: It’s crucial that proper governance be implemented throughout the entire self-service process. Clean data and standardized data definitions are essential to the user’s decision-making process. In addition, sharing capabilities and permissions leads to data access, as well as increased security and transparency.
While it’s good to be aware of the above-mentioned drawbacks, most businesses and organizations cite the many benefits as reasons to continue using self-serving data analytics.
Hands-On Data Analytics Classes
Are you interested in learning about the most current practices for analyzing, cleaning, and visualizing data? If so, Noble Desktop offers data analytics classes for students with no prior coding experience. These full-time and part-time courses are taught by top New York Data Analysts and provide timely and hands-on training for those wishing to learn more about topics like Python, SQL, Excel, or data science, among others.
In addition, a variety of live online data visualization courses are also offered for those who want to learn how to create engaging data visualizations in the virtual format. More than 80 classes are available, varying in length from three hours to ten weeks, and costing between $219-$12,995.
Those who are committed to learning in an intensive educational environment may also consider enrolling in a data analytics or data science bootcamp. These rigorous courses are taught by industry experts and provide timely instruction on how to handle large sets of data. Over 90 bootcamp options are available for beginners, intermediate, and advanced students looking to master skills and topics like data analytics, data visualization, data science, and Python, among others.
Noble Desktop’s Data Visualizations Classes Near Me tool is designed for those who want to locate and learn more about the various data visualization courses in the area. Over 200 courses are currently listed, in-person and live online. Classes cost between $119 and $12,995 and vary in length from three hours to ten weeks. In addition, more than 90 rigorous data analytics bootcamps can also be found using this tool. Bootcamps cost between $549 and $27,500 and span 18 hours to nine months.