Data is driving businesses in today’s world. Everyone is making efforts to gather and use data to provide enhanced services to their customers. But raw data is of no use. It’s only after analysing data that we get actionable insights to make it useful. Hence, the field of data analytics is gaining a lot of traction.
For several years, data analytics was siloed to only data scientists. Not everyone has the mindset to understand complex data structures and generate insights from them. Thus, the demand for data scientists and their salaries are reaching the sky.
Inadequate amount of data scientists and their high salaries are good reasons you might not afford a data scientist. But that does not stop you from making the most data analytics. That’s where augmented analytics scoop in.
What is augmented analytics, and how to leverage it?
Augmented analytics is a term coined by Gartner. It is the use of Machine Learning (ML) and Natural Language Processing (NLP) to streamline data with Business Intelligence (BI) tools.
With augmented analytics, you can reduce the time for preparing data, generate insights, give voice to data, eliminate dark data, and much more. In fact, the benefits are such that its market size is expected to grow to $29,856 million by 2025*.
To leverage augmented analytics, you need to ensure a constant flow of data. Hence, you need to streamline the management and exchange of data in your SQL database. You might also require SQL server database emergency services to ensure no downtime and data flows seamlessly across the organisation.
What are the benefits of augmented analytics?
Automating and streamlining data with BI tools offers numerous benefits. Some key benefits are:
Automating data preparation
To get insights from raw data, data scientists must first prepare data to make it available for analytics. Data preparation involves steps such as data cleansing, eliminating irrelevant data, reformatting data, etc. The cleaner the data is, the better is the result of the analytics.
A massive volume of data is being generated and stored within a few seconds in today’s world. In such a scenario, preparing and cleaning data has become a hectic task for data scientists. According to experts, data cleansing is one of the most time-consuming and least enjoyable tasks for the analytics team.
Augmented analytics has the potential to automate this process of data preparation. With the help of NLP, BI tools can easily understand a human query and prepare the data accordingly.
Generating proactive insights
Data is usually gathered and stored until a business leader requests it to be processed. This is a form of reactive insights where a manager or other leader reacts to a business need and asks for analytics.
Augmented analytics can turn reactive insights into proactive insights. Instead of waiting for the data scientists to analyse the data, systems can interweave data to auto-analyse it.
Proactive insights can help you get prepared even before a business need arises. Also, augmented analytics can save and learn from previous queries to create patterns and connections between data and automatically analyse it.
Giving a voice to data
You might have interacted with a personal assistant like Siri, Alexa, or Google assistant. It’s all the result of NLP technology. Since augmented analytics uses NLP and ML, it can give data a voice of its own.
Business leaders can interact with data and ask questions in human language. NLP can then process it, and systems can extract the relevant data based on the query.
By giving a voice to data, augmented analytics will make data accessible to everyone. For instance, even a sales manager who is not an analytics expert will generate actionable insights from the data.
Eliminating dark data
Dark data is the data that businesses store but fail to use. It is evident that since the volume of data is enormous, extracting insights from all of it by a human is not possible. But automation can do that.
With its automation ability, augmented analytics can analyse every bit of data gathered and stored to optimise its use. Thus, it has the potential to eliminate dark data.
Also, due to intentional or unintentional bias, you might find only the insights that are necessary as per your thinking. Machines are not biased to anything. Hence, they will provide every insight that you might ever need.
There are several real-world examples like Microsoft Power BI and IBM Cognos Analytics that use the power of augmented analytics. Despite the benefits it provides, augmented analytics is not yet into the mainstream of the business world. But with further advancements in technology, the adoption of augmented analytics will undoubtedly grow in the near future.