Experts refer to Artificial Intelligence and ML as the most important general-purpose technology of our era. Machines continuously are evolving and perform with accuracy without the intervention of humans.
The intersection of technology and business is a big challenge for many organizations. In order to become data-driven, companies must be willing to adapt artificial intelligence.
AI is an elusive and misunderstood technology most of the time, but now analytics have become an integral part of any organization to function successfully. Hence, an amalgamation of data analytics and AI is a good way to start the journey.
Data to Analytics to AI
There has been a gradual evolution in analytics, ranging from descriptive to diagnostic to predictive, and culminating with prescriptive as per Gartner. The most organization is at the descriptive stage of analytics still using the traditional BI approaches to gather the data and use visualization to get quick views on what has happened.
Diagnostic analytics is about finding out the reason behind an event occurs for which the techniques used are drill-down, data discovery, data mining, and correlations.
In order to dig deeper to understand the reason, experts try to use predictive analysis for projection. Typically the existing data was used to train predictive machine learning (ML), which has to lead us to Artificial Intelligence now.
Whether you have to make predictions using AI ingrained into ML or having analytics in place is a prerequisite.
AI solutions have been evolved long before the concept of analytics. An expert system is one of the examples of AI, which is used in various domains, such as medicine and agriculture by the organization. However, AI has evolved with time where the traditional AI were knowledge-based that require assembly and curation by an expert and treated as a single version of the truth.
Today, where there are abundant data sources with varying degrees of reliability, the sources may contradict, and the size & ingestion rate also differ on levels. The global market for data analytics has been predicted to exhibit a CAGR of 30.08% between 2017-2023 to surpass a valuation of USD 77.64 billion.
Due to the increased ability to use statistical algorithms and machine learning techniques, the increased generation of data can deliver actionable results for businesses. Information is no longer the powerful as data analysis, use of data, and digging into it has become powerful.
Taking from the perspective of business, data analytics can be used to increase revenue, respond to emerging trends, improve operational efficiency, and optimize marketing to create a competitive advantage.
Structuring the Data
One of the biggest challenges faced by any organization in the field of analytics is the analysis of the data sources. As data sources differ, the requirement for manual data cleansing before analysis. Studies show that this process of data preparation takes around 80% of the average analyst's time.
Additionally, the information generated has no formal structure, contracts, surveys, and emails, which has a lot of information to extract from, that analysts can use to uncover opportunities.
In order to unlock the opportunities, often involvement of external consultants is asked. As a result, businesses would need an 'opportunity cost', which is often restricted for the adoption of data analytics or business intelligence platforms. Hence here is where analytics can come in to assist.
After the emergence of machine learning, text analytics has advanced to a level where you can explore large numbers of interrelated features, bringing structure and clarity to documents and data.
Performing the Analysis
Deep Learning has raised the potential solution to automatically extract meaningful patterns from large sets of data for decision making. The key here to define and understand the goals of your analysis. The prescribed rules with logic and decisions are still invaluable that uncovers meaningful opportunities for the users to comprehend the origin of the information.
Linking the data with video analytics companies in India, specialized in a single pursuit and with the focus on the problem you are trying to resolve so that you can get consistent insights into the most relevant opportunities for the business. The collaboration can also help in uncovering the unique perspective that working by yourself might be difficult.
To Conclude -
Businesses will have to follow the chain of evolution in analytics from descriptive and diagnostic to predictive and finally to prescriptive. Machine learning tools will give a clear picture of project understanding and its goals. Partnering with an analytics service provider can be advantageous. The direct link can help you focus on the problems and try to solve them immediately.
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