Data analytic services: Tap into the potential of big data to scale your business multifolds
Data is driving technology today and technology is driving everything from businesses to other facts of our life. Applications and tools that we deploy stand redundant if they do not make use of data analytic services.
Data analytic services are aimed at integrating data and delivering insights across the global digital ecosystem to connect businesses, platforms, customers, employees, and partners.
How our data analytics services help your firm?
Streamline your data and operations
Our data analytics services help businesses enhance their operational efficiency. Gathering and analysing data about the supply chain can reveal the source of production delays or bottlenecks, as well as predict future issues. If a demand estimate indicates that a certain vendor will be unable to handle the volume required for a particular demand season, an organization can augment or replace that vendor to avoid production delays.
Formulate Business Strategy with AI insights
Procuring our data analytics services help businesses make better decisions and reduce financial losses. Predictive analytics can predict what will happen as a result of business changes, while prescriptive analytics can recommend how the business should respond to these changes. For example, a company can simulate changes in price or product offerings to see how these effect client demand. A/B testing of product offerings can be used to validate the hypotheses generated by these models.
Deliver Personalized Customer experience
Customers’ data is collected through a variety of channels, including physical retail, e-commerce, and social media. Businesses can get insights into client behaviour by employing data analytics to construct full customer profiles from this data, allowing them to give a more personalized experience.
We run behavioural analytics models that run on client data to improve the customer experience even more. For example, a company could use e-commerce transaction data to construct a predictive model to identify which products to promote at checkout in order to boost sales.
Mitigate Risks In advance with Predictive Analysis
We develop data analytics solutions that assist a company in identifying hazards and taking preventative steps. A retail chain, for example, may use a propensity model — a statistical model that predicts future behaviours or events — to figure out which outlets are most vulnerable to theft. The company might then use this information to decide the level of protection required at the stores, as well as whether or not it should divest from any of them.
Secure Your IT Infrastructure and thwart any data breach
Forecasting is at the heart of predictive analytics. Businesses use descriptive and diagnostic analytics, as well as other historical data sets, to create a recommendation-based model that employs advanced statistical and machine learning algorithms.
Data security is a concern for all businesses. By analyzing and visualizing relevant data, organizations can use our data analytics services to diagnose the reasons of previous data breaches. For example, to discover the course and origins of an attack, the IT department can employ data analytics software to parse, analyze, and visualize audit logs. This data can assist IT in locating and patching issues.
Data Analytics Services We Provide:
Predictive Analytics
Forecasting is at the heart of predictive analytics. Businesses use descriptive and diagnostic analytics, as well as other historical data sets, to create a recommendation-based model that employs advanced statistical and machine learning algorithms.
For example, by studying a patient’s past health records and general demographics, predictive analytical models in healthcare can determine whether or not he or she is at risk for a heart attack. Predictive analytics can also be used to create a campaign based on consumer buying behaviour at various points of time in the past. Analyzing product suggestion data sets to forecast the likelihood of certain outcomes is an example of predictive analytics.
Descriptive Analytics
Around the world, descriptive analytics is used by 90% of businesses. It’s the most basic type of analytics, allowing you to break down large amounts of data into smaller chunks in order to gain more precise insights. It’s a typical tool used today to pull crucial data from social media and detailed media platforms and websites. Businesses can use descriptive analytics to decode the inner context and reasons for earlier success or failure.
Descriptive analytics aid in obtaining maximum value from data mining in order to create and test a business intelligence system that analyses real-time and historical data to generate insights for the future strategy. Creating financial or sales reports is an example of descriptive analytics.
Diagnostic Analytics
This type of data analytics assists businesses in resolving crucial difficulties by determining what is happening, why it is happening, and what the main cause is. When a company using business intelligence dashboards needs to drill deeper into the data to uncover the reasons or factors that affect the company, diagnostic analytics comes into play. Integrating diagnostic and descriptive analytics assists firms in identifying data relationships and architecture in order to do a quick comparison and construct the most trustworthy data-based decision model. The HR department, for example, might analyze the applicant’s data sets as an example of diagnostic analytics.
Prescriptive Analytics
Prescriptive analytics is the next step following predictive analytics, and it assists firms in developing prescriptions to solve business problems using data-derived elements. Big data is a black box, and it’s never certain what the most dependable inputs will be, but it always reveals why problems occurred. This is where prescriptive analytics may help. Prescriptive analytics gives firms advice on all conceivable outcomes and leads to actions that are most likely to increase business outputs. Prescriptive analytics is a data analytics approach for company optimization that provides insights into “what should a business do” to solve an issue. This method enables organizations to make educated decisions in the face of uncertainty.
Cognitive Analytics
Cognitive analytics is the most advanced type of analytics, using a variety of cognitive technologies such as artificial intelligence, machine learning algorithms, deep learning models, and others to process data and draw conclusions from existing data and patterns. These discoveries are added to the knowledge base for future interferences, and the self-learning feedback loop is designed to mimic human thinking, making cognitive applications smarter and more effective over time. Processing huge parallel/undistributed data (such as contact center conversation logs) computation to draw insights is an example of cognitive analytics.