We live in an age of data, and in the age of big data specifically, where data sets are becoming increasingly complex, fast-moving and massive, and legacy systems that process data cannot understand the aggregate data that comes quickly, and data management automation tells a different story, and analytics promises. Articulated by Gartner, the future of data analysis that harnesses technologies such as Machine learning or Artificial intelligence.
The future of data analysis:
Data analysis software that integrates augmentative analytics interacts with data as humans do, but on a large scale to meet big data needs, as the analysis process often begins with collecting public or private data.
Ancient data lines were created by data scientists, who spent 80 percent of their time gathering and preparing data, and only 20 percent searching for insights.
The goal of augmented analytics is to automate Operations Collect data and prepare data to save 80 percent of a data scientist’s time, however, the enhanced analytics will completely replace the manual work of data science teams, as the enhanced analytics will take care of the entire analysis process from data collection to providing business recommendations to decision makers.
Analytics Enhanced Data Management:
When data experts analyze the data, they try to find the answer to a question. This question can be simple and straightforward, such as: What were the sales numbers in the past year by channel and region?
These types of questions look for accurate facts and numbers, and are usually an introduction to more advanced questions, such as: Why did sales increase in the last quarter? How will the market share grow next year?
Business intelligence tools that include augmented analytics can automate these questions, for example, users can type a question in the search box and get an answer in natural language, accompanied by visualization and insights.
It is also important to consider the adaptability of a solution to an increasingly digital world, where data is a hot and volatile topic, and new technologies evolve rapidly.
With tremendous technological improvements, companies have access to more data than before, as the flow of data provides an opportunity to gain more insights into the consumer life cycle, and it is also a challenge as companies must determine how to host and structure data sources.
With developments in data privacy and compliance with laws (GDPRThe General Data Protection Regulation, companies that benefit from e-commerce and Internet communication channels must deal with changing legislation when they decide how to capture data over the Internet.
As such, augmented analytics needs to anticipate the complex data structures that now require multi-channel sales and marketing to streamline data management processes.