How does an organization help the self-serve advanced analytics model grow and thrive? Responsibility lies in a number of places within the enterprise.
Author: Kartik Patel
SSDP (otherwise known as self-serve data preparation) is the logical evolution of business intelligence analytical tools. With self-serve tools, data discovery and analytics tools are accessible to team members and business users across the enterprise.
How does one measure the effectiveness of a new Augmented Data Discovery solution? Once the business has chosen data democratization and implemented a self-serve analytics solution, it must measure ROI & TCO and establish metrics that will compare business results achieved before and after the implementation.
Whether we know it or not, we use Natural Language Processing every day. It makes it easier for us to interact with computers and software and allows us to perform complex searches and tasks without the help of a programmer, developer or analyst.
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 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.
Business markets and competition are moving much more quickly these days and predicting, planning and forecasting is more important than ever. It is also important to ensure that every team member is a real asset to the organization and can contribute their knowledge and skill with full Insight into the effects and outcome of activities and processes and the ability to correct the course and make recommendations using clear, concise information. Advanced analytics is the logical tool to help a business optimize its investments and achieve its goals.
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 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 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.
