Creativity is the lifeblood of an enterprise! But, it is easy to get into a rut and do what has worked in the past. If a business is to succeed, it must constantly generate new ideas and new ways to address the market and its consumers. Augmented Data discovery and the new world of self-serve tools has added an interesting dimension to this discussion as it encourages business users to become Citizen Data Scientists and encourages organizations to embrace data democratization.
I had a conversation with a dear friend the other day and one of the things we discussed was the ubiquitous mention of Citizen Data Scientists in technology and industry publications and conferences. My friend (who shall remain nameless), insisted that this was just another passing fancy and that Citizen Data Scientists were not really all that important in building the enterprise of the future.
Smarten is pleased to announce SmartenApps, its newest addition to the Smarten augmented analytics suite of offerings. The Smarten: eCommerce Analytics for Shopify by Elegant MicroWeb is an augmented analytics and reporting solution integrated with Shopify eCommerce.
Why would your business need Self-Serve Data Preparation? Perhaps you have a team that includes IT professionals that can prepare data for analysis or maybe you have a business analyst or a data scientist to serve that need. But, there are plenty of good reasons to consider data preparation tools that are easy enough for every team member to use.
If your team is using business intelligence (and it should be), you may be working with a more restrictive business solution that is not designed for a business user. If that is the case, you are missing an opportunity to cascade analytics throughout the organization and to optimize your resources and improve productivity. Self-Serve Business Intelligence solutions are designed with guidance and recommendations to guide a business user with average skills through the process of preparing and analyzing data and to encourage collaboration and data sharing across the enterprise.
Here’s some good news! If you want to predict or forecast results for your business, you don’t need to be a data scientist, a statistician, a business analyst, or a rocket scientist! Today, augmented analytics solutions provide all the help a business user will need to gather data, prepare it for analysis and then choose the right algorithm or analytical technique to analyze the data and get clear, easy-to-understand results.
Digital Transformation (Dx) is achieved when an organization plans and exploits digital technologies, workflow and processes, and its core capabilities and skills with an enterprise culture that evolves and shifts to engender a digital business model that advances business. Any Dx plan must include a review and appropriate modification of businesses processes, business models, operations, team collaboration and customer relationships.
As businesses struggle to remain competitive and to do more with less, every enterprise is looking for a way to optimize resources, and to get the most out of every team member. To do that, the business management team must leverage the knowledge and skills of each team member as those skills relate to their role and their responsibilities. Businesses must use team member knowledge gleaned from experience and education and merge that knowledge with data and analytics to make context-based, data-driven decisions.
As a business grows, it faces the challenge of managing, security and accessing its data in a meaningful way. Often, the concepts of data management, data engineering, data lakes, data warehouses and data mining are well behind business needs and, by the time the business team takes a hard look at the issues, the task of bringing it all together to enable data analysis and a comprehensive data strategy is a daunting one.
Whether you know it or not, you know Natural Language Processing (NLP) very well! You use it all the time to ask questions on Google Ask a simple question, get a list of responses that meet your needs. What did we ever do before natural language processing? How else might we use it to simplify our lives?
