Augmented analytics – Leveraging Data-Driven Business Growth
Data reliance has grown extravagantly over time and well, rightly so. As customers have shifted towards digital space even more than before, advanced analytics has become something necessary for enterprises to incorporate into their business to help them stay ahead of the market. However, it is not easily done as easy it is said, there is only a sheer volume of data available for organizations making it difficult to interpret data accurately. So, as the volume and complexity of datasets are increasing, it is said that maybe traditional BI tools may no longer be able to handle them efficiently. In the world today, quick and unbiased data analysis is necessary to meet business objectives.
Data engineers and data scientists handle the organization, processing, and drawing of insights. People in these roles are highly skilled and qualified and are responsible for data ingestion, cleansing, transformation, and developing advanced AI/ML algorithms to gather all relevant data needed by the company. Again, this is easier said than done because there is a shortage of technical roles. However, augmented analytics systems promise to ease this challenge by delivering quick and accurate insights by trying to decrease the dependency on data engineers and scientists. It is also said by Analyticsinsight.net that in 2022 augmented analytics is expected to be the most popular trend in data and business intelligence and by 2023 it is expected to have a worth of US $13 Billion.
So, let’s look at in more detail what exactly is augmented analytics and what is all the hype about.
A Quick Run-Through of Augmented Analytics
Augmented analytics is a process where data is automatically ingested from different raw data sources and then cleansed in an unbiased way to then finally be communicated to all relevant business users. This process is more of less independent and actually uses Artificial Intelligence and Machine Learning to automate data preparation, insights discovery, and information exchange.
Augmented Analytics – High-Level Work Flow
What Makes Augmented Analytics so Valuable?
Augmented analytics is a modern-day approach to yield maximum business benefits for a company. They help a business bring the top of some particular business features. Let’s go over them in more detail.
Automated Data Detection
One of the top most demanding features of augmented analytics is its automation data detection functions. It has the ability to collect large amounts of data based on specific features like schemas, profiles and personal information all collected through different mediums.
Statistical operational algorithms
Pre-built algorithms are very helpful when it comes to planning for the future and estimating forecasts. Statistical operational algorithms come in handy in exactly this place because they eliminate the need for long complex calculations and in fact, simplify the dependency on specialized coding. What is better is that these operational algorithms can be tailored according to business needs and requirements as they are flexible.
Another feature of augmented analytics is Artificial Intelligence and Machine Learning driven recommendations systems. What this does is it trims down the data lifecycle hence saving time. The steps of data preparation, analysis and reporting become smoother and quicker to process. AL and ML driven recommendations can also be generated for diverse users based on their specific requirements.
Leveraging businesses With Augmented Analytics
There is always some sort of ambiguity and double thoughts when taking decisions regarding your business. To reduce this machine learning comes in handy, it makes it possible to pool the most compelling business insights to help make business decisions when needed. With augmented analytics, business users will get better insights, enhanced productivity and help lead to good decision making.
Data Ingestion Automation
Augmented Analytical tools are trained to gather, refine, and process a company’s data automatically. However, this process cannot be done alone, it still requires a data scientist to aid the process of developing machine learning algorithms to help clean and structure data. The process still requires a data scientist to aid the process of developing machine learning algorithms for cleaning and structuring data. But once done, the future processing is quicker, which leads to accelerated data analysis.
Lower Cost of Ownership
Being dependent on human labor to work on on-site pouring data in, cleansing it and then reporting it can actually be costly. The use of Artificial intelligence and Machine learning can help reduce or even eliminate the risk of data redundancy to lesser human dependency. This way the entire process will not be automated and controlled with the help of computers rather than being done by humans, hence resulting in a lower cost of ownership.
Quicker Business Value Generation
Giving off good business value delivery and productivity is an important factor all businesses should be focused on and working towards to. Augmented analytic systems streamline the entire process by leveraging algorithms in a way that they are able to successfully perform repetitive tasks and visualizations to help an organizations response become more responsive.
Augmented Analytics Enterprise Examples
Augmented analytics systems can be implemented to solve any data-related business problem. Some examples and applications of used cases are:
Services pertaining to finance
Financial institutions like banks, mortgages, and insurance firms are faced with different challenges with the fintech industry going through a post-pandemic transformation. As the industry is actively looking for innovative solutions, stringent government regulations make things more competitive. Here augmented analytics can play a vital role in generating authentic data more quickly.
Health care industry
The health care industry is always in need of the latest tools and technologies. A massive amount of data is generated during research, clinical trials, patient records, and experiments. AI/ML-embedded analytics can reduce their research time while ensuring more accurate results in real-time for seamless medical practice.
Demand forecasting and predictive analytics
Augmented algorithms use advanced statistical analysis and pattern recognition to provide unprecedented insights for future demand planning. A large amount of historical data can be automatically analyzed for accurate forecasting and informed decision-making.
Augmented Analytics Tools
- Oracle Analytics
- Yellowfin suite
- Qlik sense
- Salesforce Einstein Analytics Platform
- SAP Analytics Cloud
- SAS Visual Analytics
Take Charge of Your Business
Today, the implantation of data analytics in business tasks is important for uncovering significant experiences, exempting human bias, exact estimations and producing effective suggestions.
The imaginative abilities of augmented analytics are relied upon to exhibit an assortment of advantages across different market verticals. Artificial intelligence and Machine Learning embedded enablers guarantee to change the information scene by smoothing out business methodologies. Augmented analytics is without a doubt an amazing chance to inspire existing and future organizations at scale.
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