When businesses take on the task of forecasting and planning, it is important to provide the tools to go through this process at all levels with data integrated from the various systems and databases across the enterprise and to provide simple tools for all users that can be adopted and used to take on the various tasks and responsibilities involved in planning and accurate forecasting.
When combined, Digital Transformation and Data Literacy provide a rich and profound value to the organization. A properly structured initiative will prepare the business for an ever-changing market and business environment and make the business and its team members agile, nimble, and capable of responding to change. The initiative, once employed, will provide many benefits to offset the efforts involved in completing the initiative.
Download the Whitepaper: Preparing Your Business for Digital Transformation (Dx) and Data Literacy
An advanced analytics solution that is designed for augmented analytics, must be easy enough for the average business user to achieve results without IT skills or the skills of a data scientist. When a business user can leverage clickless analytics search capability, the user can achieve rapid, clear results and use those results to solve problems, share information and optimize opportunities for the business.
If you stay up-to-date with your industry and market publications, you have probably heard a lot of buzz about natural language processing or NLP. In fact, whether you know it or not, you use NLP every day. When you type a question into Google, you are using NLP! The system translates your question and returns results to meet your needs.
Wise business managers are embracing the Digital Transformation initiative in an effort to improve accuracy, productivity and the integration of technology and analytics within the organization. Augmented Analytics is a logical and critical addition to the Dx initiative and will significantly enhance the success of the Digital Transformation agenda. When a business wishes to consider and implement a Digital Transformation and Augmented Analytics initiative, it must shift its cultural assumptions to support continuous improvement. There will be no Digital Transformation or Data Literacy success if the enterprise does not embrace the concept of continuous improvement.
When a business is considering an advanced analytics solution, it must sell its management team and investors on the value and benefit of that solution and the merits of implementing such a solution or upgrading an existing solution.
Smarten is pleased to announce the introduction of its eLearning Workshop, ‘Be a Citizen Data Scientist – Aspire, Inspire, Become‘. This online course is designed to provide a solid foundation for one’s business and, more importantly, for those members of the team who may be considering as early adopters or champions of the Citizen Data Scientist transformation within their organization.
What is a Citizen Data Scientist and what is the role of a Citizen Data Scientist? It isn’t as complicated as you might think. A Citizen Data Scientist is a business user working in a professional capacity within an enterprise – one who is collaborative and curious by nature and willing and ready to adopt augmented analytics tools to prepare and analyze data and use that data to gain insight into business issues and opportunities. The role of a Citizen Data Scientist is to interact with and liaise with IT and data scientists to define use cases and refine data output to be used in making fact-based decisions.
Elegant MicroWeb is pleased to announce that its Smarten Augmented Analytics solution is once again included as a Representative Vendor in the Gartner Market Guide for Data Preparation Tools, published July 9, 2020 (ID G00719343).
It is a great idea to get your business users involved in analytics and to encourage and build data literacy in your enterprise but one of the things that often stops an organization from implementing self-serve analytics is the need to gather and prepare the data for analytics. How can IT or data scientist staff turn over day-to-day analytics when data is spread across the enterprise and it is so difficult to integrate that data and prepare the data for analytics?
