British mathematician Clive Robert Humby has famously said, “Data is the new oil.” Even though the phrase was coined by the mathematician way back in 2006, it is only most recently the world realized the gravity of his words. Data today has become an acute business asset that is result-oriented innovation in every sector. The humongous amount of data available today has also found its processing match with AI/ML, making processes like Big Data analytics a streamlined operation for every business. Today, many businesses are considering investing in AI analytics solutions to scale their business services and uncover new growth opportunities.
In fact, the AI analytics platform market size is expected to grow up to USD 9.88 Billion by 2022, growing at an exponential Compound Annual Growth Rate of 30.5%. From BI platforms to research studies, these platforms are actively being used to generate billion-dollar worth of insights for businesses and hence have become one of the most sought-after commodities. As such, if you are looking to invest in a custom-built AI-based analytics platform, we can assure you that it’s going to be a good investment.
A Complete Guide on AI-based Data Analytics Platform Development
British mathematician Clive Robert Humby has famously said, “Data is the new oil.” Even though the phrase was coined by the mathematician way back in 2006, it is only most recently the world realized the gravity of his words. Data today has become an acute business asset that is result-oriented innovation in every sector. The humongous amount of data available today has also found its processing match with AI/ML, making processes like Big Data analytics a streamlined operation for every business. Today, many businesses are considering investing in AI analytics solutions to scale their business services and uncover new growth opportunities.
In fact, the AI analytics platform market size is expected to grow up to USD 9.88 Billion by 2022, growing at an exponential Compound Annual Growth Rate of 30.5%. From BI platforms to research studies, these platforms are actively being used to generate billion-dollar worth of insights for businesses and hence have become one of the most sought-after commodities. As such, if you are looking to invest in a custom-built AI-based analytics platform, we can assure you that it’s going to be a good investment.
Also Read – Big Data Analytics- The Next Generation Intelligence
What is an AI-based Data Analytics Platform and Why is It Important?
Artificial Intelligence (AI) is a combination of technologies, algorithms, and logic that helps machines simulate human cognitive functionality to perform tasks such as problem-solving, learning, reasoning, and more. An AI platform for data analytics simply utilizes these capabilities to derive meaningful analytical insights.
Earlier, analysts used to spend days deriving insights from any given data. But with the advent of the internet, both the frequency and quantity of the data being generated have exploded. Today, it is impossible for human analysts to clean all the available data, let alone process it to derive meaningful insights.
Now, through AI development services with parallel processing capabilities can do all that within a matter of minutes on a dedicated business analytics platform. It can process historical data to derive predictive insights for the future. It can also use data on similar problems and solutions to provide prescriptive analytics insights. Another use case of using AI in the analytics platform is augmented analytics, which simply deals with automating pre-analysis tasks like data preparation, cleaning, and exploratory data analysis. Now any platform that can perform any of the above-mentioned analytics operations through AI capabilities is called an AI-based analytics platform.
Advantages of AI-based Data Analytics Platforms
AI data analytics platforms have the capability to automate every task associated with data analysis. From data loading and integration to processing and presentation, the platform has tools for every operation and can help businesses use the available data in the most effective way. Here are some of the more important advantages of an AI-based analytics platform.
Efficient Data Management
An AI-based analytics platform is one of the most effective tools in the arsenal of business intelligence analysts for data management. The software can seamlessly integrate various systems and organize the data pipeline in a manner that all the databases are generated through at least the first few levels of normalization. This eventually speeds up the analysis process of the AI platform and offers companies major cost savings by efficiently managing the available data repositories and resources.
Scalability
With the data management and pipelining tool, AI-based analytics platforms are the ideal software to support the scalability of a business. A well-integrated data analytics platform can easily transfer data into chosen repositories without sacrificing the system’s performance. The users only need to ensure that cloud services are configured well enough to support the additional flow of data. The rest of the operation will easily be handled by the analytics platform.
Actionable Insights
An AI-based data analytics platform can be integrated with various touchpoints to gather all the behavioral data from customers and logistics data from supply chains. Then the company can create distinct and clear data models to define different entities and the variables it would like to track to derive the required insights. The analytics platform will then automatically generate the databases and analyze the same in real time. It can then also be used to generate reports with the required parameters to help businesses gain actionable insights from a holistic viewpoint.
Better Data Representation
Data and business analytics platform powered with AI software development services are effective in generating a visual representation of the insights they derive from the available data. Most AI-based analytics platforms use dedicated AI/ML development services to come up with various data visualization tools, including dashboards, reports, collinearity graphs, interactive charts, and more. These presentation tools make it easy for the analysts to explain various variables to stakeholders and then eventually create the report with insights that are most useful for them.
Features to Develop in an AI-based Data Analytics Platform
A business analytics platform can prove to be the most significant tool for your business intelligence and administration. It can gather data from various touchpoints to efficiate all your business processes, including customer services, inventory management, supply chain optimization, machinery maintenance, and more. As such, if you consider developing an AI-based analytics platform, you can easily get confused with the variety of capabilities available. This is why it is ideal that you first create a list of all the features and functionalities that you would require from your AI data analytics platform. While listing them, ensure that you can integrate sources for all the data required for such functionalities. Here is a brief description of the more common features that you should have in your custom-built AI analytics platform.
Data Integration
The foremost feature every data analytics platform must have, whether or not it has AI capabilities, is data integration. The software must be able to integrate with various data sources and repositories and must not require manual data gathering from analysts. The analysts must be able to pull all the required information from CRMs, website analytics tools, ERPs, and manually created spreadsheets, for a report. It must also be able to store all the data in pre-designed data models in a unified data warehouse. This feature will lay the foundation of simplified data extraction, which further will simplify data processing since all the data will be stored in the standard format and fields.
Data Filtering
Filtering and sorting is yet another important feature without which analysts would have to spend hours just to organize and manage the data they require for any given report. The more advanced filters you can add to your data analytics software through AI software development services, the better it will perform in the rest of the other analysis tasks. To support these features in the front end, you will need strong indexing functionalities in the backend too. This will facilitate the search capabilities of the software, making it easy for the analysts to scan through the datasets and then use their business understanding to custom-create reports with more meaningful insights.
Data Transformation
Now extracting and integrating data and then availing filter capabilities to scan through it is just the beginning of any analysis process. The actual operation begins with data transformation. This is the stage where analysts, before the invention of data analytics software, were spending days simply cleaning data and changing it into required formats to apply their algorithms. It is essential for any AI tool to be able to study data without any discrepancies. The transformation feature gives users the capability to decide the format of every parameter so that no erroneous value can tamper with the final insights.
Data Visualization
Now deriving insights is not enough for a data analytics platform. The users must be able to understand those insights and represent them to the stakeholders with comparative ease. As such, you must use AI/ML development services to enable data visualization as an essential feature of your business analytics platform. Since it is such an important feature, ensure that you offer as many representation tools to your users as required based on the data models and insights they are looking for. For example, you can add visualization tools with histograms, correlation matrices, pivot tables, pattern indicators, etc.
Controlled Sharing
Traditionally, the information gathered by different departments used to get stored in siloed data sources. This made it difficult for users to collaborate their data management and visualization efforts. This is why when it comes to building analytics platform, you must ensure it contains tools for data and report sharing. Users must be able to collaborate with each other through complete report sharing, interactive annotations, tools to add or redact data, etc., for a more effective business analysis procedure.
Remote Access
Today, most people are using digital tools through their mobile phones than through desktops. As such, to stand strong in the competitive market, ensure that your data analytics platform is accessible through varied devices. You would want your analysts to work remotely through their mobile phones. You may also want to collaborate with your global clientele and team. As such, simply making a legacy mobile application would not solve the problem. Ideally, you should have your AI-based analytics platform built on the cloud so that any device can access its functionalities, data, and insights from anywhere and at any time.
Unify Customer Data
A basic data analytics platform can simply do with data integration capabilities. But when it comes to AI-based platforms, you will require much more to make effective use of the technology. Through deep learning and NLP sub-domains of AI, you can unify all the customer data, including the call logs, voice recording, and sentiments and crux derived from the same. Such data is often far more powerful in deriving meaningful and truly impactful insights. Such analytics will give you an unparalleled competitive edge, helping your business serve, acquire, and retain more customers effortlessly.
Forecast Demand
Again, when it comes to AI-based data analytics platforms, predictive analytics has become an indispensable feature. Your software must have this capability since it will more often than not have historical data to facilitate model building. With such a piece of predictive analytics software, you can streamline more processes in your organization than you would have thought possible, from managing and manufacturing inventory as per the market demand to optimizing marketing investments based on expected returns. Adding this simple feature to your data analytics tool will help you make incredibly better business and marketing decisions.
Also Read- Top Data Analytics Business Ideas of 2023
Conclusion
Artificial Intelligence and data analytics are the most sought-after technology combo today. Formerly, the data that remained in repositories stagnant is now creating businesses worth billions, all thanks to AI. AI, too, for a long time, remains a secondary technology to streamline redundant tasks. Today with data analytics, it is performing even those jobs that are beyond human comprehension. This is why the AI-based data analytics platform is among the most sought-after enterprise analytics platform solutions today.
A custom-built AI analytics platform can be used to uncover micro-targeted insights that are not easily found by human analysts. AI can process several variables efficiently and apply it on a clean data to derive the required insights. The tailored platform can also be used to generate reports in the required format and with the required fields. This is why, when it comes to AI analytics platforms, it’s considered ideal to develop one from scratch than to subscribe to one with assorted features. If you’re looking to hire AI development services, Matellio can be your ideal choice. With years of experience in AI and analytics enterprise software, we have everything you would want from your tech partner. All you need to do is fill up this form and request a quote for your project today!
