A data discovery tool is a crucial tool for every business user in your organization. With so many sources of data, in so many locations with your enterprise, it is impossible for users to know whether they have access to complete, accurate data to make decisions.
If a business is trying to implement self-serve business intelligence tools, it must look at its organization, processes and culture to determine whether it is prepared to roll-out data democratization and provide direct access to business intelligence tools. Changes in the environment and the expectations can help to empower business users and hold them accountable and to encourage data popularity and data sharing to work toward transformation of business users to Citizen Data Scientists.
SSDP (otherwise known as self-serve data preparation) is the logical evolution of business intelligence analytical tools. With self-serve tools, data discovery and analytics tools are accessible to team members and business users across the enterprise.
Data prep can slow down analytics and cause delays. Self-service data preparation (when done right) can enable business users to leverage sophisticated, easy-to-use tools for self-serve ETL. Data extraction, transformation and loading (otherwise known as ETL) can be time-consuming and requires professional skills but self-service ETL will walk business users through an augmented data preparation process and take the complicated, confusing steps out of the process by helping the user make decisions on how to prepare, clean, reduce and use the data in the best way possible.
The benefits of advanced analytics are many and the current support in the market for business user access and data insight provides expanded advantages of advanced analytics.
How does one measure the effectiveness of a new Augmented Data Discovery solution? Once the business has chosen data democratization and implemented a self-serve analytics solution, it must measure ROI & TCO and establish metrics that will compare business results achieved before and after the implementation.
What is predictive analytics? Put simply, predictive analytics is a method used to forecast and predict the future results and needs of an organization using historical data and a comprehensive set of data from across and outside the enterprise.
Business intelligence and analytics is not just for data scientists or IT professionals anymore. If your business is going to be successful, it must compete and it must solve problems, find opportunities and plan with accuracy. There is no time for a misstep in the market!
Are your business users trying their best to leverage advanced analytics tools, only to find that the software solution requires them to be an expert in data science? Do your users have to ask IT to professional analysts for data preparation assistance in order to ensure that they can access, prepare and use the data they need to analyze.
