Why Data Science needs Data Optimisation ?

EXCELR SOLUTIONS·SUNDAY, 1 MAY 2016

We see the most data scientists in analysing data then publishing reports on insights they found in data to help take decisions. Without decisions, nor actions, business isn’t impacted, hence no value is created. Hence data science value comes from the decisions and actions it enables. This confirms that Data Science needs Data Optimisation.

The role of data science is to enable data based decision making

Data optimisation can be achieved by proper data mapping techniques which is an essential aspect in data integration. Optimisation involves finding the best possible solution from multiple available solutions which is cost effective and high performance solution. Optimisation is used in solving multiple business problems and one of the core concept in advance analytics. Data optimisations is used by several applications in fetching data from data sources so that the data could be used in data view tools and applications such as those used in statistical reporting like Tableau and Qlik.

Data visualisation tools for creating effective reports depend on accurate queries. To have a data view, the database source need to allow retrieval of the desired data and display the expected output. As databases usually deal with high volumes of data, retrieving bulks of data, getting a data view may be a slow and complex process. Employing data optimisation can reduce the complexity of the process while trying to optimise the needed resources by reducing physical processing needs.

Some of the areas where optimisation is used widely are :

  • Crew member allocation in various industries
  • Airlines industries to schedule their cargo flight routes
  • Supply chain optimization
  • Pricing and Revenue optimization
  • Manufacturing sector for production
  • Transportation and logistics

Optimisation techniques not only provides the best optimal solution but also provides feedback on how changing the objective function coefficient, will change the optimal solution.

Every industry faces optimisation problem and different optimisation techniques are used to solve different problems. Different optimisation problems include linear optimisation problem, combinatorial optimisation problem, constrained optimisation problem, multi objective optimisation problem, nonlinear optimisation problem etc.

  • Are you looking to build an optimised routing system and solve a transportation problem;
  • Do you want to identify what is the right volume of spare parts that need to be manufactured based on demand, supply, resource availability;
  • Do you want to know what is the right way to allocated resources to a project without any redundancy or
  • Do you want to know a simple trick to solve Sudoku puzzle, then Optimisation is the right subject that you need to learn.

It is important that we understand which optimisation technique need to be used to solve a problem and also get a clear view on how to identify the objective function, decision variables and constraints to solve the problem correctly. We help you in learning different optimisation techniques by using industry business scenarios and also teach widely used optimisation tools like Solver, @Risk optimiser.

Analytics Without Decisions

Data for a Data Scientist is what Oxygen is to Human Beings. This is also a profession where statistical adroit works on data — incepting from Data Collection to Data Cleansing to Data Mining to Statistical Analysis and right through Forecasting, Predictive modelling and finally Data Optimisation. A Data Scientist does not provide a solution; they provide most optimised solution of the lot. Gartner predicted in 2012 that Data Scientist & Business Analytics jobs will increase to the tunes of Millions by the end of 2015. This is very evident with the rise in job opportunities in various job portals. As a Data Scientist or an aspirant you should not believe us. Go search for your own and confirm with facts and figures.

Reach us at www.excelr.com

Resources :

The Role Of Data Science , IT Best Kept Secret Is Optimisation

Why Data Science needs Data Optimisation ?

EXCELR SOLUTIONS·SUNDAY, 1 MAY 2016

We see the most data scientists in analysing data then publishing reports on insights they found in data to help take decisions. Without decisions, nor actions, business isn’t impacted, hence no value is created. Hence data science value comes from the decisions and actions it enables. This confirms that Data Science needs Data Optimisation.

The role of data science is to enable data based decision making

Data optimisation can be achieved by proper data mapping techniques which is an essential aspect in data integration. Optimisation involves finding the best possible solution from multiple available solutions which is cost effective and high performance solution. Optimisation is used in solving multiple business problems and one of the core concept in advance analytics. Data optimisations is used by several applications in fetching data from data sources so that the data could be used in data view tools and applications such as those used in statistical reporting like Tableau and Qlik.

Data visualisation tools for creating effective reports depend on accurate queries. To have a data view, the database source need to allow retrieval of the desired data and display the expected output. As databases usually deal with high volumes of data, retrieving bulks of data, getting a data view may be a slow and complex process. Employing data optimisation can reduce the complexity of the process while trying to optimise the needed resources by reducing physical processing needs.

Some of the areas where optimisation is used widely are :

  • Crew member allocation in various industries
  • Airlines industries to schedule their cargo flight routes
  • Supply chain optimization
  • Pricing and Revenue optimization
  • Manufacturing sector for production
  • Transportation and logistics

Optimisation techniques not only provides the best optimal solution but also provides feedback on how changing the objective function coefficient, will change the optimal solution.

Every industry faces optimisation problem and different optimisation techniques are used to solve different problems. Different optimisation problems include linear optimisation problem, combinatorial optimisation problem, constrained optimisation problem, multi objective optimisation problem, nonlinear optimisation problem etc.

  • Are you looking to build an optimised routing system and solve a transportation problem;
  • Do you want to identify what is the right volume of spare parts that need to be manufactured based on demand, supply, resource availability;
  • Do you want to know what is the right way to allocated resources to a project without any redundancy or
  • Do you want to know a simple trick to solve Sudoku puzzle, then Optimisation is the right subject that you need to learn.

It is important that we understand which optimisation technique need to be used to solve a problem and also get a clear view on how to identify the objective function, decision variables and constraints to solve the problem correctly. We help you in learning different optimisation techniques by using industry business scenarios and also teach widely used optimisation tools like Solver, @Risk optimiser.

Analytics Without Decisions

Data for a Data Scientist is what Oxygen is to Human Beings. This is also a profession where statistical adroit works on data — incepting from Data Collection to Data Cleansing to Data Mining to Statistical Analysis and right through Forecasting, Predictive modelling and finally Data Optimisation. A Data Scientist does not provide a solution; they provide most optimised solution of the lot. Gartner predicted in 2012 that Data Scientist & Business Analytics jobs will increase to the tunes of Millions by the end of 2015. This is very evident with the rise in job opportunities in various job portals. As a Data Scientist or an aspirant you should not believe us. Go search for your own and confirm with facts and figures.

Reach us at www.excelr.com

Resources :

The Role Of Data Science , IT Best Kept Secret Is Optimisation

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