We live in the Data Era. Not just data, but Big Data. Datasets have become complex, fastmoving, and increasingly huge. Legacy BI systems that handled data cannot fathom the volumed data coming at live speed.
The automation of data management tells a different tale. Not only does it automate data management but simplifies the task of data scientists.
Coined by Gartner, augmented analytics is the future of data analytics that harnesses disruptive technologies like machine learning/ artificial intelligence techniques to automate data preparation, insight discovery and intelligence sharing.
The Future of Data Analytics
Data analytics software that integrates augmented analytics interacts with data as humans would do but on a large scale to cater to the big data needs. The analysis process often starts with public or private data collection.
Legacy data pipelines were created by data scientists, who spent 80% of their time on collection and data preparation, and just the remaining 20% on finding insights. The goal of augmented analytics automates the processes of data collection and data preparation to save 80% of data scientist’s time. However, ultimately augmented analytics would completely replace the manual work of the data science teams with AI. Augmented Analytics would take care of the entire analysis process from data collection to presenting business recommendations for decision-makers.
Augmented Analytics for Data Management
Ultimately, when data experts perform data analysis, they’re trying to find the answer to a question. This question could be as simple and straightforward, like “What were the sales figures last year by channel and region?”.
These kinds of questions seek out facts and hard numbers, and are usually the precursor to more advanced questions like, “Why did sales increase last quarter?” and “How market share grow next year?”
- BI tools that incorporate augmented analytics can automate these questions. For instance, users can type a question into a search box and receive an answer in natural language, accompanied by visualization and insights.
- It is also important to consider a solution’s adaptability for an increasingly digital world, where data is a hot and volatile topic, and new technologies develop quickly.
- With the massive technological improvements, businesses have the potential to acquire more data than they have before. This data influx gives an opportunity to acquire more insights into the consumer life cycle. It also presents a challenge as companies must determine how to host and structure data sources.
With developments into data privacy and adherence to the GDPR (General Data Protection Regulation), companies that leverage e-commerce and online communication channels must contend with changing legislation when they decide how to capture online data.
As such, augmented analytics needs to anticipate the complex data structures that multichannel sales and marketing now require the simplification of data management processes.
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