What Is Data & Analytics?

A Microsoft Gold Data Analytics Competency partner, Holmex Trade LP offers a comprehensive kit of data analytics services to convert our customers’ historical and real-time, traditional and big data into actionable insights. We have the successful projects for manufacturing, retail and wholesale, healthcare, banking, telecoms and other industries.

A Microsoft Gold Data Analytics Competency partner, Holmex Trade LP offers a comprehensive kit of data analytics services to convert our customers’ historical and real-time, traditional and big data into actionable insights. We have the successful projects for manufacturing, retail and wholesale, healthcare, banking, telecoms and other industries.

We are the member of the partner networks of Microsoft, Oracle and Amazon Web Services.

Companies using Data Analytics that can convert data into meaningful insights would evidently be the winners in this hyper-competitive world. Any company harnessing the benefits of Data Analytics can beat its competitors without a hitch.
There are enterprises for whom data analytical tools are the most important weapons in their arsenal. They make use of data in order to build customer profiles to serve them better. This way, they can provide a very customized experience to their customers.
Since they works on ads, they needs to know the pulse of its users by making sure that the ads are up to date in terms of customization and other aspects. 
Data Analytics is one of the vital aspects that is driving some of the biggest and best companies forward, today.

Companies using Data Analytics that can convert data into meaningful insights would evidently be the winners in this hyper-competitive world. Any company harnessing the benefits of Data Analytics can beat its competitors without a hitch.
There are enterprises for whom data analytical tools are the most important weapons in their arsenal. They make use of data in order to build customer profiles to serve them better. This way, they can provide a very customized experience to their customers.
Since they works on ads, they needs to know the pulse of its users by making sure that the ads are up to date in terms of customization and other aspects. 
Data Analytics is one of the vital aspects that is driving some of the biggest and best companies forward, today.


We support companies that would like to own their analytics solution, be it in-house or in-cloud one. We also support the companies that prefer relying on a trusted outsourcing partner, skip the technicalities part and just enjoy the insights. Therefore, we offer two cooperation models:

Implementing a data analytics solution
Under this model, we provide our customers with an enterprise-wide analytics solution consisting of a data lake for big data (if required), a data warehouse, OLAP cubes and reporting. As a result, the customer owns the solution and can continuously benefit from the insights it produces.

Data analytics outsourcing
In this case, our customers share their data with us and we take care of the rest – data quality management, infrastructure and data analysis. When the analysis is done, we provide the customer with a detailed report containing the findings and the recommendations on how to improve the status quo.

We support companies that would like to own their analytics solution, be it in-house or in-cloud one. We also support the companies that prefer relying on a trusted outsourcing partner, skip the technicalities part and just enjoy the insights. Therefore, we offer two cooperation models:

Implementing a data analytics solution
Under this model, we provide our customers with an enterprise-wide analytics solution consisting of a data lake for big data (if required), a data warehouse, OLAP cubes and reporting. As a result, the customer owns the solution and can continuously benefit from the insights it produces.

Data analytics outsourcing
In this case, our customers share their data with us and we take care of the rest – data quality management, infrastructure and data analysis. When the analysis is done, we provide the customer with a detailed report containing the findings and the recommendations on how to improve the status quo.

Services We Render

Applied to various business areas, an analytics solution contributes to fact-based decision-making.

Customer analytics
Customer analytics helps to understand customers’ preferences, measure their response to promotions, as well as to price and product combinations.

Marketing analytics
Marketing analytics helps to measure the success of marketing activities, identify market trends, benchmark against competitors, and analyze a product portfolio.

Sales analytics
Sales analytics helps to check accounts by type and country, latest opportunities and wins, product performance and more.

Ecommerce analytics
Ecommerce analytics helps to conduct a complete customer analysis: segmentation, engagement with products, basket analysis, customer journey analysis, forecast customer demand, analyze the performance of categories, brands and SKUs, implement ‘you-may-also-like-it’ functionality.

Performance analytics
Performance analytics helps to conduct plan/actual analysis, monitor strategic, departmental and individual metrics and always be up-to-date with the progress achieved.

Financial analytics
Financial analytics helps to solve specific financial tasks like effective cash flow and working capital management, as well as contribute to establishing a true partnership between business managers and CFOs.

HR analytics
HR analytics helps to measure staff turnover, identify the ways of fostering engagement and improve employee productivity, conduct recruiting analysis from different perspectives, as well as enable talent management analytics.

Operational and asset analytics
Operational and asset analytics helps to improve business processes, assess supplier- and vendor-related risks, forecast demand and optimize inventory.

Industrial analytics
Industrial analytics helps to optimize production, assure product quality, monitor equipment utilization and foster predictive maintenance.

Services We Render

Applied to various business areas, an analytics solution contributes to fact-based decision-making.

Customer analytics
Customer analytics helps to understand customers’ preferences, measure their response to promotions, as well as to price and product combinations.

Marketing analytics
Marketing analytics helps to measure the success of marketing activities, identify market trends, benchmark against competitors, and analyze a product portfolio.

Sales analytics
Sales analytics helps to check accounts by type and country, latest opportunities and wins, product performance and more.

Ecommerce analytics
Ecommerce analytics helps to conduct a complete customer analysis: segmentation, engagement with products, basket analysis, customer journey analysis, forecast customer demand, analyze the performance of categories, brands and SKUs, implement ‘you-may-also-like-it’ functionality.

Performance analytics
Performance analytics helps to conduct plan/actual analysis, monitor strategic, departmental and individual metrics and always be up-to-date with the progress achieved.

Financial analytics
Financial analytics helps to solve specific financial tasks like effective cash flow and working capital management, as well as contribute to establishing a true partnership between business managers and CFOs.

HR analytics
HR analytics helps to measure staff turnover, identify the ways of fostering engagement and improve employee productivity, conduct recruiting analysis from different perspectives, as well as enable talent management analytics.

Operational and asset analytics
Operational and asset analytics helps to improve business processes, assess supplier- and vendor-related risks, forecast demand and optimize inventory.

Industrial analytics
Industrial analytics helps to optimize production, assure product quality, monitor equipment utilization and foster predictive maintenance.

Data Analytics refers to the set of quantitative and qualitative approaches for deriving valuable insights from data. It involves many processes that include extracting data and categorizing it in order to derive various patterns, relations, connections, and other such valuable insights from it. Today, almost every organization has morphed itself into a data-driven organization, and this means that they are deploying an approach to collect more data that is related to their customers, markets, and business processes. This data is then categorized, stored, and analyzed to make sense out of it and derive valuable insights from it.
The three most important attributes of big data include volume, velocity, and variety.

There are various tools in Data Analytics that can be successfully deployed in order to parse data and derive valuable insights out of it. The computational and data-handling challenges that are faced at scale mean that the tools need to be specifically able to work with such kinds of data.

Prescriptive Analytics: This is the type of analytics that talks about an analysis based on the rules and recommendations in order to prescribe a certain analytical path for the organization.

Predictive Analytics: Predictive analytics ensures that the path is predicted for the future course of action.

Diagnostic Analytics: This is about looking into the past and determining why a certain thing happened. This type of analytics usually revolves around working on a dashboard.

Descriptive Analytics: In descriptive analytics, you work based on the incoming data and for the mining of it you deploy analytics and come up with a description based on the data.

Data Analytics refers to the set of quantitative and qualitative approaches for deriving valuable insights from data. It involves many processes that include extracting data and categorizing it in order to derive various patterns, relations, connections, and other such valuable insights from it. Today, almost every organization has morphed itself into a data-driven organization, and this means that they are deploying an approach to collect more data that is related to their customers, markets, and business processes. This data is then categorized, stored, and analyzed to make sense out of it and derive valuable insights from it.
The three most important attributes of big data include volume, velocity, and variety.

There are various tools in Data Analytics that can be successfully deployed in order to parse data and derive valuable insights out of it. The computational and data-handling challenges that are faced at scale mean that the tools need to be specifically able to work with such kinds of data.

Prescriptive Analytics: This is the type of analytics that talks about an analysis based on the rules and recommendations in order to prescribe a certain analytical path for the organization.

Predictive Analytics: Predictive analytics ensures that the path is predicted for the future course of action.

Diagnostic Analytics: This is about looking into the past and determining why a certain thing happened. This type of analytics usually revolves around working on a dashboard.

Descriptive Analytics: In descriptive analytics, you work based on the incoming data and for the mining of it you deploy analytics and come up with a description based on the data.

Working with Big Data Analytics

The topic of Data Analytics is a vast one and hence the possibilities are also immense.

Prescriptive analytics ensures that it sheds light on various aspects of your business and provide you a sharp focus on what you need to do in terms of Data Analytics. Prescriptive analytics adds a lot of value to any organization, thanks to the specificity and conciseness of this domain. You can deploy prescriptive analytics regardless of the industry vertical based on the same rules and regulations.

Predictive analytics can also ensure that the domain of big data can be deployed for predicting the future based on the present data. A good example of predictive analytics is the deployment of analytical aspects to the sales cycle of an enterprise. It starts with the lead source analysis, analyzing the type of communication, the number of communications and the channels of communication, along with sentiment analysis through heightened use of Machine Learning algorithms and more in order to come up with a perfect predictive analysis methodology for any enterprise.

Diagnostic analytics is used for the specific purpose of discovering or determining why a certain course of action happened. For example, one can work with diagnostic analytics to review a certain social media campaign for coming up with the number of mentions for a post, the number of followers, page views, reviews, fans, and such other metrics to diagnose why a certain thing happened.

Descriptive analytics is the least popular which is basically used for coming up with a methodology for uncovering patterns that can add value to an organization. As an example, you can think about the credit risk assessment. It involves predicting how likely a certain customer is to default based on his credit history. It takes into consideration various aspects like the financial performance of the customer, inputs from past financial institutions that the person might have approached and other platforms like social media, and online presence based on the web-based solutions.

Since no organization today can stay without being inundated with data, it is imperative that Data Analytics is an indispensable part of the life cycle of data in any organization. Based on various types of Data Analytics, today’s forward-looking enterprises can actually go ahead and design a very robust path to success with the data they have.

Working with Big Data Analytics

The topic of Data Analytics is a vast one and hence the possibilities are also immense.

Prescriptive analytics ensures that it sheds light on various aspects of your business and provide you a sharp focus on what you need to do in terms of Data Analytics. Prescriptive analytics adds a lot of value to any organization, thanks to the specificity and conciseness of this domain. You can deploy prescriptive analytics regardless of the industry vertical based on the same rules and regulations.

Predictive analytics can also ensure that the domain of big data can be deployed for predicting the future based on the present data. A good example of predictive analytics is the deployment of analytical aspects to the sales cycle of an enterprise. It starts with the lead source analysis, analyzing the type of communication, the number of communications and the channels of communication, along with sentiment analysis through heightened use of Machine Learning algorithms and more in order to come up with a perfect predictive analysis methodology for any enterprise.

Diagnostic analytics is used for the specific purpose of discovering or determining why a certain course of action happened. For example, one can work with diagnostic analytics to review a certain social media campaign for coming up with the number of mentions for a post, the number of followers, page views, reviews, fans, and such other metrics to diagnose why a certain thing happened.

Descriptive analytics is the least popular which is basically used for coming up with a methodology for uncovering patterns that can add value to an organization. As an example, you can think about the credit risk assessment. It involves predicting how likely a certain customer is to default based on his credit history. It takes into consideration various aspects like the financial performance of the customer, inputs from past financial institutions that the person might have approached and other platforms like social media, and online presence based on the web-based solutions.

Since no organization today can stay without being inundated with data, it is imperative that Data Analytics is an indispensable part of the life cycle of data in any organization. Based on various types of Data Analytics, today’s forward-looking enterprises can actually go ahead and design a very robust path to success with the data they have.

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