The successful implementation of an augmented analytics solution for business users is not just about choosing a cost-effective tool and completing a timely deployment, nor does the process stop with training. In order to get business users to embrace and adopt self-serve augmented data discovery tools, the enterprise must approach the implementation with appropriate change management processes.
The concept of advanced analytics can seem out of reach for many businesses. Business execs and managers often picture a team of data scientists and IT staff busily analyzing data and, included in that picture, they imagine the bags full of cash required to fund that team of professionals.
The Merriam-Webster dictionary defines the word ‘augment’ this way: ‘to make greater, more numerous, larger or more intense’. If you are wondering how this applies to the term ‘augmented analytics’, you are not alone. Let’s take a closer look at Augmented Analytics and talk about why it has gotten so much attention in the business intelligence world.
Whether you are a consumer or a business user, today’s technology users are savvy and they are used to having easy-to-use tools and features that make them more productive and allow them to quickly complete tasks. The same holds true for users of advanced analytics users. These users are also consumers when they are off the job and they are used to simple search technology like Google and other search methodologies that allow them to think, search and get results in a way that is fitting for normal speech and language.
Data visualization may not seem important, but the way you see data can provide additional insight or it can muddle the picture to the point where you will miss critical issues or opportunities. The importance of data visualization is even more evident when that data is being analyzed by business users who are not likely to see data in an analytical way and probably do not have the knowledge or skill required to change visualization techniques to accommodate a particular type of data.
As business organizations fight for competitive advantage, funding for projects and large expenditures can fall by the wayside. In today’s competitive business market, every senior executive looks at risk, value and calculations like return on investment (ROI) and total cost of ownership (TCO) before approving a budget.
If you want your business users to function and perform at their highest level and you want to optimize all resources, your data discovery solution must provide the guidance and ease-of-use that a business user requires to analyze and share data and make confident decisions that will improve the business bottom line.
If Data Science was once the sole domain of analysts and data scientists, Augmented Data Science represents the democratized view of this domain. With Augmented Data Science, the average business user can engage with advanced analytics tools that allow for automated machine learning (AutoML) and leverage sophisticated analytical techniques and algorithms in a guided environment that uses auto-recommendations and suggestions to lead users through the complex world of data science with ease and intuitive tools.
As business becomes more competitive, as markets get tighter, there is a need to leverage and optimize your resources to the greatest extent possible. Using the knowledge and skill of each individual on your team and allowing these skills to inform business decisions and business analysis is key. As businesses democratize data and look for ways to improve collaboration and ensure that information makes it to everyone who needs it, a new business role has emerged.
As the need for advanced analytics increases in organizations, enterprises large and small struggle to find and sustain the professional resources they need to meet their requirements for data, analysis and strategic direction.
