Analytics have become important nowadays, with data becoming more complex and difficult to understand or interpret.
Augmented Analytics is a tool to help businesses or users understand data easily using machine learning and artificial intelligence. In this article, we are going to discuss in detail what augmented analytics is.
Importance of understanding data for business
Data for a business is a collection of facts or statistics such as raw analytical data, customer feedback data, sales numbers, or other. For multiple reasons, gathering and analyzing data is an integral part of a business.
Here are a few reasons why it is important to understand data for your business:
- Understanding data can help a business make better decisions about finding new customers, increasing sales numbers, improving customer service, and many more.
- Tracking and reviewing data can help businesses solve problems and uncover performance breakdowns.
- Data helps business leaders to make the most accurate market predictions using real-time intelligence.
- In addition to increasing sales and revenue, data helps businesses improve cash flow as well helping money management efficiently.
- Data helps teams and employees to reduce money and time, increasing productivity and improving business processes.
What is Augmented Analytics?
Powered by artificial intelligence (AI) and machine learning (ML), augmented analytics help users with data preparation, insight generation, and insight explanation. Augmented analytics help businesses and analytics to work with data faster, more accurately, and more efficiently.
Augmented analytics is for business users and executives who would benefit from getting insights and value from data without having any great technical skills. The technology helps users quickly find relevant data, analyze them and uncover insights for their business.
Let’s have a look at some of its essential features:
- Augmented analytics helps in automatic data identification along with reading data in multiple formats such as PDF, text documents, etc.
- It offers statistical analysis to result in certain outcomes or insights into data.
- It helps users to prepare data faster and more accurately, minimizing manual work.
- It helps users with the best AI-driven recommendations for data preparation, discovery, analysis, and more.
- It allows natural language interactions so users can type a data query in plain simple language instead of any code or data query language.
Augmented Analytics Components
There are three critical components of Augmented Analytics:
Machine Learning (ML)- Machine learning is a branch of artificial intelligence that uses algorithms and historical data to predict outcomes more accurately. ML helps businesses get valuable insight from customer behavior and business operational patterns. It uses statistical methods to make predictions and classifications.
Natural Language Technologies (NLP)- NLP is another branch of artificial intelligence that helps computers understand human language (text and spoken words). It allows computers to respond to verbal commands, translate language, and summarize texts in real-time.
Automation- Machine Learning technologies help automate data analytics tasks and reduce the time to build, train, and deploy ML models.
How can Augmented analytics integration help business growth?
Integrating augmented analytics can be transformational, especially for big industries such as Manufacturing, Pharma, retail, Healthcare, CPG, etc. It helps businesses to increase revenue, and customer retention, improve customer satisfaction, cash flow, and much more. Businesses can become more agile, help business processes and make better decisions.
Benefits of Augmented Analytics
Maximize Productivity
Augmented analytics can help increase human productivity by automating repetitive, time-consuming, and manual tasks. AI is a great help when it comes to saving time and costs and scaling tasks that require less intelligence.
Provide Greater Value
The process of developing business solutions and decision-making requires human intelligence, but it can be repetitive and time-consuming. With Augmented analytics, most processes can be automated, such as data preparation, insights discovery, etc. It helps businesses deliver more value faster.
Improved Analytics
Augmented analytics help make analytics accessible to every user regardless of their analytical skills. It increases productivity by bringing analytics to users of all levels. With Artificial Intelligence fueling augmented analytics, businesses can benefit from improved analytics and informed decisions.
Improved Decision-making Process
Augmented analytics help businesses make the most informed decisions through data analysis.
Improved Efficiency and Accuracy
Machine learning and artificial intelligence technology accurately perform repetitive tasks and calculations. The technology automates tasks that help businesses to save time and energy and increase human productivity.
Making organizations Agile
AI can help organizations by increasing insight speed, making them more agile. It can help trip the search area, suggest relevant data, and recommend multiple analysis methods. Augmented analytics can track user behavior and recommend subsequent actions.
Traditional Analytics vs. Self-Service Analytics vs. Augmented Analytics
| Traditional Analytics | Self-Service Analytics | Augmented Analytics | |
| Definition | Traditional analytics uses a complex IT environment, Data warehousing, and IT staff to perform data analysis. | This is a type of business intelligence where users perform data queries and generate reports independently using BI software and minimal IT support. | This helps users to understand and analyze data using machine learning and artificial intelligence technologies. |
| Skill/Expertise | Skill, Training, and expertise are needed here. It is heavily dependent on IT staff. | Business users need little Training and expertise to perform data analysis and build and modify reports. | No prior skill or expertise is needed to analyze data using augmented analytics. |
| Resource | This type of analytics is heavily dependent on IT Staff. | This type of analytics needs manual resources and BI tools. | It is dependent on Augmented analytics technology. |
| IT Setup | Traditional analytics need the constant involvement of IT and Data specialists. | Self-service analytics needs minimal IT involvement, mostly in the initial setup. | Augmented analytics doesn’t need IT or any data specialists. |
| Kind of Data | Needs data structuring before its utilization. | Harness data from various formats and multiple resources. | Harness data from various formats and multiple resources. |
Augmented Analytics Challenges
Despite all the great benefits augmented analytics have for businesses, it still bears some challenges, and here are some of them:
- Employees might fear that AI technologies will replace them without knowing that however advanced these technologies can be, AI has limitations.
- Augmented analytics depend on humans to follow best practices and courses of action to perform as expected. It performs based on the programs and algorithms it has been trained for.
- Augmented analytics models need to be trained correctly, which takes time.
- The larger the data needs analyzing, the more computing power augmented analytics ML algorithms will need.
Author’s note on Augmented Analytics
Augmented analytics is still an emerging technology with much scope for future use and development. Businesses can utilize this technology through cutting-edge software powered by artificial intelligence and machine learning. Although this is the future technology, and as per Gartner, only 10% of analysts are using it to its full potential, your business can leverage augmented analytics for its growth.
Before deciding whether or not to implement augmented analytics in your business, ask yourself the following questions:
- Does your business have an analytics team?
- Do you have extensive data?
- Why do you need augmented analytics?
If you are clear about using augmented analytics, the next step is to follow best practices to benefit from it.
Final Words
Augmented analytics is a great help for all businesses that need data analysis. Accurate and timely data is a driving factor of business growth. Augmented analytics combines human intelligence with AI to analyze data and gain valuable insight. Businesses from around the world (Big to small) are using this technology, and so should you to get ahead in the competition.
You may also explore Augmented Intelligence.
