How Can I Jump Start My Digital Transformation (Dx) Project?
Digital Transformation (Dx) may seem daunting, but with the right planning and execution, a Dx project will reap many benefits for your business.
Digital Transformation (Dx) may seem daunting, but with the right planning and execution, a Dx project will reap many benefits for your business.
Whether you are a business owner, a business executive or a business manager, or you just like to keep up with industry trends, you no doubt have read about the transition of business users to Citizen Data Scientists. The topic has been in industry journals and publications for years, and it is still relevant today.
Technology research and consulting firm, Gartner, predicts that, ‘By 2023, data literacy will become an explicit and necessary driver of business value, demonstrated by its formal inclusion in over 80% of data and analytics strategies and change management programs.’
If your business is considering a Digital Transformation initiative, it may be mired in questions about the value vs. the cost and time to make this change. While it is true that a Digital Transformation initiative takes some effort, there are numerous long-term benefits to this effort. In this article, we explore four of the benefits of Digital Transformation, in hopes of helping your organization articulate and affirm the advantages to make a decision that is right for your enterprise.
Business users are business professionals who have expertise in an industry or market arena or perform a function to support the ongoing operation of the business – professionals who may be front line workers on a production line, finance professionals, sales representatives, non-profit office workers, medical researchers, middle managers, regional managers for retail chains, transportation dispatchers or…well, you get the idea. These team members know their job and they do it well. But, they probably don’t have the technical skills to write a SQL query, or to filter out the columns and fields for an analytical search in order to get the results they need to make a decision.
If your business is considering an advanced analytics solution, your IT and management team has probably already done some research and concluded that the concept of augmented analytics designed to support business users is the right way to go. To democratize data, improve data literacy and transition business users to the Citizen Data Scientist role, the business must select the right solution and plan for success.
Gartner predicts that ‘… augmented analytics will be ubiquitous, but only 10% of users will use it to its full potential.’ One of the primary reasons that augmented analytics is not adopted and leveraged to its full capacity is that the business chooses a solution that is not easy enough for team members to adopt – one that is restrictive, inaccessible or requires sophisticated skills.
Small and medium sized businesses (SMEs) are often challenged to satisfy all the roles and responsibilities in the organization and most team members wear more than one hat. That feeling of being overstretched is typical of growing businesses and, in an increasingly competitive market with businesses fighting for skilled resources, it is difficult to meet budget and scheduling goals and get it all done.
Natural Language Processing (NLP) may not be a term that everyone is familiar with but all consumers and business users are certainly aware of its power. You use natural language processing every day when you perform a search in Google using questions written in the same way one would speak or write to another person. Whether you know it or not, you are using NLP to process, interpret and return results that meet your criteria.
What is Natural Language Processing (NLP)?
Natural Language Processing utilizes artificial intelligence to translate computer code and language into real world, human language. While the goal is to simplify human interaction with computers, NLP is a complex mix of computational linguistics and computer science. When a business is considering an augmented analytics solution that leverages natural language processing, it need not concern itself with the complicated underpinning of code and design, but should rather consider what NLP can do for its users and for its business results.