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.
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.
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.
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?
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.
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.
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.
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?
A few months ago, I was talking to a friend (someone who has worked, for years, in the IT industry) and happened to mention data democratization. In this age of modern business intelligence, augmented analytics and Citizen Data Scientists, one would think that every IT professional would know the term ‘data democratization’ and its crucial importance to the business environment and success. But, my friend did not know or understand the term or its importance. In fact, she was even a bit skeptical about the potential and the movement toward self-serve business intelligence and analytics.
