Data Sciences

Data Integration

Base your decisions on complete, consistent data Business users depend on having easy access to trustworthy, complete data. But data integration is no easy task, because data is spread across disparate systems and data volumes are rapidly increasing.

SES Data Integration is a simple, flexible solution that addresses data integration challenges for small and midsize businesses. It ensures data credibility and consistency – so organiza¬tions can easily manage all their data integration projects while reducing costs and increasing overall productivity.

Reduce costs by eliminating overlapping, redundant tools and complex system architectures.

Data integration involves combining data from several disparate sources, which are stored using various technologies and provide a unified view of the data. Data integration becomes increasingly important in cases of merging systems of two companies or consolidating applications within one company to provide a unified view of the company's data assets. The later initiative is often called a data warehouse.

Probably the most well known implementation of data integration is building an enterprise's data warehouse. The benefit of a data warehouse enables a business to perform analyses based on the data in the data warehouse. This would not be possible to do on the data available only in the source system. The reason is that the source systems may not contain corresponding data, even though the data are identically named, they may refer to different entities.

Data Integration Areas

Data integration is a term covering several distinct sub-areas such as:

Data warehousing

Data migration

Enterprise application/information integration

Master data management

This article concentrates on the process of data integration. More detailed information about the above areas can be found in related articles.

Challenges of Data Integration

At first glance, the biggest challenge is the technical implementation of integrating data from disparate often incompatible sources. However, a much bigger challenge lies in the entirety of data integration.

Data Integration Techniques

There are several organizational levels on which the integration can be performed. As we go down the level of automated integration increases.

Manual Integration or Common User Interface - users operate with all the relevant information accessing all the source systems or web page interface. No unified view of the data exists.

Application Based Integration - requires the particular applications to implement all the integration efforts. This approach is manageable only in case of very limited number of applications.

Middleware Data Integration - transfers the integration logic from particular applications to a new middleware layer. Although the integration logic is not implemented in the applications anymore, there is still a need for the applications to partially participate in the data integration.

Uniform Data Access or Virtual Integration - leaves data in the source systems and defines a set of views to provide and access the unified view to the customer across whole enterprise. For example, when a user accesses the customer information, the particular details of the customer are transparently acquired from the respective system. The main benefits of the virtual integration are nearly zero latency of the data updates propagation from the source system to the consolidated view, no need for separate store for the consolidated data. However, the drawbacks include limited possibility of data's history and version management, limitation to apply the method only to 'similar’ data sources (e.g. same type of database) and the fact that the access to the user data generates extra load on the source systems which may not have been designed to accommodate.

Common Data Storage or Physical Data Integration - usually means creating a new system which keeps a copy of the data from the source systems to store and manage it independently of the original system. The most well know example of this approach is called Data Warehouse (DW). The benefits comprise data version management, combining data from very different sources (mainframes, databases, flat files, etc.). The physical integration, however, requires a separate system to handle the vast volumes of data

Data Mining

Data Mining is the ability to explore organizational data right from their desktop. In one affordable and easy-to-use, easy-to-deploy package, the solution delivers data-driven insights to organizations of all sizes and skill levels. Make reliable, evidence-based decisions.

With SES, you can harness all your organizational data to uncover patterns and hidden relationships that help identify future trends, flag emerging issues and determine potential opportunities. Using powerful data mining capabilities combined with data preparation, exploration and enrichment, you can explore large amounts of relevant, quality data in a multidimensional manner to discover what's most significant for decision making.

It uses data and analytics to identify best practices that improve care and reduce costs. Researchers usedata mining approaches like multi-dimensional databases, machine learning, soft computing, datavisualization and statistics. Mining can be used to predict the volume of patients in every category.

Data Mining is primarily used today by companies with a strong consumer focus — retail, financial, communication, and marketing organizations, to “drill down” into their transactional data and determine pricing, customer preferences and product positioning, impact on sales, customer satisfaction and corporate profits. With data mining, a retailer can use point-of-sale records of customer purchases to develop products and promotions to appeal to specific customer segments.

Econometrics

Proactive, strategic and tactical planning for shaping a more profitable future.

A broad array of econometric techniques for understanding the impact of economic and marketplace factors on your organization so you can plan better for the future.

Hidden Markov models Powerful HMM procedure models and predicts hidden Markov models.

Spatial econometrics modeling Conduct spatial regressions using the CSPATIALREG procedure. Incorporate data with a spatial element (e.g., location and mapping data) into analyses, and improve the econometric inference and statistical properties of estimators.

Econometric models for cross-sectional data Includes count regression, severity regression, qualitative and limited-dependent variables, and copula methods with compound distribution for cross-sectional data analysis.

Forecasting models for time series data Model complex economic and business scenarios to analyze the impact specific events might have over time. Time series models include user-defined ARIMA and exponential smoothing models. Time series analysis includes decomposition capabilities and diagnostic testing.

Open, cloud-enabled, in-memory engine Provides high availability, faster in-memory processing and native cloud support of the SAS Viya engine. Procedures are available for both public and private cloud delivery in a scalable and elastic environment. All analytical assets are managed within a common environment for a single, governed model inventory across applications.

Panel data econometric models Includes panel data models, count regression models, and regression models for qualitative and limited-dependent variables for analyzing data that combines both time series and cross-sectional dimensions.

Economic capital models Combines frequency, severity and copula modeling to simulate portfolio risks and estimate VaR, TVaR, etc. Enables you to model the need for capital reserves, and comply with prudential regulation and capital adequacy directives.

Forecasting

Plan confidently with automatically generated, reliable forecastsSmall and midsize businesses often depend on simplistic forecasting systems and spreadsheets that can't handle large data volumes and support only a few basic forecasting methods.

SES Forecasting is an affordable, easy-to-use solution that gives organizations advanced forecasting capabilities in a PC environment. The solution selects the most appropriate forecasting method from a comprehensive model repository that includes a full range of forecasting methods – enabling forecasters to automatically generate reliable forecasts without any manual programming.

Machine Learning

On-demand programming access to machine learning algorithms in the cloud. Combining data preparation, feature engineering, modern statistical and machine learning techniques in a single, scalable, in-memory processing environment for developing, testing and deploying models.

Faster coding of machine learning algorithms

Reduces the learning curve and expedites model development with automatic code generation and reusable code snippets.

Hyperparameter autotuning

Enables faster, better model results with best-in-class hyperparameter autotuning.

More efficient programming

Advanced data transformation

Provides robust coding language for data transformation and manipulation. Provides a single interface that facilitates fast, efficient movement from data preparation, to exploration, to model development.

Easy, cloud-based access to data science applications Provides instant access to powerful data science applications via SES Analytics Cloud. We take care of the infrastructure, setup and maintenance so you can focus on using the applications.

Statistics

The study of collecting, analyzing, organizing, interpreting data is called statistics. They are numerically expressed facts to present and infer the samples using logical reasoning. It’s a broad concept and is important in almost every field. Many researchers, promoters depict this information to influence their results. It is useful in calculating per capita income, population, gross domestic product, unemployment, etc., of a country. It is applied in the field of economics, business, physics, maths, agriculture, astronomy, and much more. The calculations and outcomes of this subject help in decision-making in these fields.

Machine Learning

On-demand programming access to machine learning algorithms in the cloud.

Combining data preparation, feature engineering, modern statistical and machine learning techniques in a single, scalable, in-memory processing environment for developing, testing and deploying models.

Faster coding of machine learning algorithms

Reduces the learning curve and expedites model development with automatic code generation and reusable code snippets.

Hyperparameter autotuning

Enables faster, better model results with best-in-class hyperparameter autotuning.

More efficient programming

Advanced data transformation

Provides robust coding language for data transformation and manipulation.

Provides a single interface that facilitates fast, efficient movement from data preparation, to exploration, to model development.

Easy, cloud-based access to data science applications

Provides instant access to powerful data science applications via SES Analytics Cloud. We take care of the infrastructure, setup and maintenance so you can focus on using the applications.

Statistics

The study of collecting, analyzing, organizing, interpreting data is called statistics. They are numerically expressed facts to present and infer the samples using logical reasoning. It’s a broad concept and is important in almost every field. Many researchers, promoters depict this information to influence their results. It is useful in calculating per capita income, population, gross domestic product, unemployment, etc., of a country. It is applied in the field of economics, business, physics, maths, agriculture, astronomy, and much more. The calculations and outcomes of this subject help in decision-making in these fields.

Application of Statistics in Different Fields

The application of statistics is extensive, so let us discuss the fields where the subject methods are commonly implemented:

Mathematics

The formulas used in math are reliable, but to get more precision and exactness, statistics methods are important. In fact, it is called the branch of applied math. There are common techniques that both the fields have adopted from each other such as statistical methods, namely probability, dispersion, etc., used in math and mathematical concepts like integration and algebra are used in former.

Business

Business students must be aware of the importance of statistics in the field. There are times when a businessman has to make quick decisions, and this can be done by using its concepts which make the decision-making easy. He strategizes the marketing, finance, production, resource through it. What are the tastes and preferences of consumers? What should be the quality? What should be the target market? All these questions are answered using statistical tools.

Economics

There are so many concepts of economics that are completely dependent on statistics. All the data collected to find out the national income, employment, inflation, etc., are interpreted through it. In fact, theory of demand and supply, relationship between exports and imports are studied through this subject. The perfect example of this is census; the bureau uses its formulas for calculating a country’s population.

Country’s Administration

Many national policies are decided using statistical methods, and administrative decisions are taken based on its data. Statistics provides most accurate data which helps government to make budgets and estimate expenditures and revenues. It is also used to revise the pay scale of employees in case cost of living is rising.

Astronomy

When scientists measured the distance between sun and earth, or moon and earth, they did not use any measurement scale or ruler for that. It was these statistical methods that helped them to find out the best answers and estimates that are possible. It is difficult to measure the mass, size, distance, density of objects in the universe without any error, but statistics formulas do this with the best probability.

Banking

When someone deposits his money in banks, the idea is that he will not withdraw the amount in the near future. So, banks lend this money to other customers to earn profit in the form of interest. They use statistical approach for this service. They compare the number of people making deposits against the number of people requesting loans and at the same time ascertaining the estimated day for the claim.

Accounting and Auditing

Although accounting needs exactness in calculating the profit and loss of the business, certain decisions can be taken according to approximation which is done through statistics. For example, sampling may be used to find out the current trends in the market as it does not require any precision.

Natural and Social Science

Almost all fields of science such as biology, chemistry, physics, etc., use statistical methods for experimenting and analyzing their results. In biology, it is used in biostatistics, biometrics, which includes investigating about medicines, pharmacy, agriculture, fishery, etc.

Same way, probability theory is used in physics while estimating large population, results of thermodynamics use statistical tools. Sociology is also based on the subject to analyze and interpret data and test hypothesis.

Statistics is a varied subject, and there are many other uses of it in different fields related to daily human activities. Few other examples are weather forecasting, communicate information, data analysis, etc. If you are a statistics student, then you can decide your field you want to opt for according to your interest.

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Applications of Statistics

Statistics and Government

The functions of a government are more varied and complex. Various depts. in the state are required to collect and record statistical data in a systematic manner for an effective administration. Data pertaining to various fields namely population, natural resources, production both agricultural and industrial,finance,trade,exports and imports, prices, labor, transport and communication, health, education,defence ,crimes etc are the most fundamental requirements of the state for its administration. It is only on this basis of such data; the government decides on the priority areas, gives more attention to them through target oriented programmes and studies the impact of the programmes for its future guidelines.

Statistics and Planning

Modern age is an age of planning and statistics are indispensable for planning. According to Tippett planning greater or lesser degree according to the government in power is the order of the day and without statistics, planning is inconceivable. Based only on a correct assessment of various resources both human and material of the country proper planning can be made. A study of data relating to population, agriculture, industry, prices, employment, health, education enables the planners to fix up time-bound targets on the social and economic fronts evaluation of such economic and social programmes at different stages by means of related data gathered continuously and systematically is also done to decide whether the programmes are on towards the goal or targets set.

Statistics and Economics

In the fields of economics it is almost impossible to think of a problem which does not require an extensive use of statistical data. Most of the laws in economics are based on a study of a large number of units and their analysis is enabled by statistical data and the statistical methods. The important economic aspects like production, consumption, exchange and distribution are described, compared and correlated with the aid of statistical tools. By a statistical study of time series on prices, sales, production one can study their trends, fluctuations and the underlaying causes. Thus statistics is indispensable in economic analysis.

Importance of Statistics in Different Fields

Statistics plays a vital role in every field of human activity. Statistics helps in determining the existing position of per capita income, unemployment, population growth rates, housing, schooling medical facilities, etc., in a country.

Now statistics holds a central position in almost every field, including industry, commerce, trade, physics, chemistry, economics, mathematics, biology, botany, psychology, astronomy, etc., so the application of statistics is very wide. Now we shall discuss some important fields in which statistics is commonly applied.

(1) Business

Statistics plays an important role in business. A successful businessman must be very quick and accurate in decision making. He knows what his customers want; he should therefore know what to produce and sell and in what quantities.

Statistics helps businessmen to plan production according to the taste of the customers, and the quality of the products can also be checked more efficiently by using statistical methods. Thus, it can be seen that all business activities are based on statistical information. Businessmen can make correct decisions about the location of business, marketing of the products, financial resources, etc.

(2) Economics

Economics largely depends upon statistics. National income accounts are multipurpose indicators for economists and administrators, and statistical methods are used to prepare these accounts. In economics research, statistical methods are used to collect and analyze the data and test hypotheses. The relationship between supply and demand is studied by statistical methods; imports and exports, inflation rates, and per capita income are problems which require a good knowledge of statistics.

(3) Mathematics

Statistics plays a central role in almost all natural and social sciences. The methods used in natural sciences are the most reliable but conclusions drawn from them are only probable because they are based on incomplete evidence.

Statistics helps in describing these measurements more precisely. Statistics is a branch of applied mathematics. A large number of statistical methods like probability averages, dispersions, estimation, etc., is used in mathematics, and different techniques of pure mathematics like integration, differentiation and algebra are used in statistics.

(4) Banking

Statistics plays an important role in banking. Banks make use of statistics for a number of purposes. They work on the principle that everyone who deposits their money with the banks does not withdraw it at the same time. The bank earns profits out of these deposits by lending it to others on interest. Bankers use statistical approaches based on probability to estimate the number of deposits and their claims for a certain day.

(5) State Management (Administration)

Statistics is essential to a country. Different governmental policies are based on statistics. Statistical data are now widely used in making all administrative decisions. Suppose if the government wants to revise the pay scales of employees in view of an increase in the cost of living, and statistical methods will be used to determine the rise in the cost of living. The preparation of federal and provincial government budgets mainly depends upon statistics because it helps in estimating the expected expenditures and revenue from different sources. So statistics are the eyes of the administration of the state.

(6) Accounting and Auditing

Accounting is impossible without exactness. But for decision making purposes, so much precision is not essential; the decision may be made on the basis of approximation, know as statistics. The correction of the values of current assets is made on the basis of the purchasing power of money or its current value. In auditing, sampling techniques are commonly used. An auditor determines the sample size to be audited on the basis of error.

(7) Natural and Social Sciences

Statistics plays a vital role in almost all the natural and social sciences. Statistical methods are commonly used for analyzing experiments results, and testing their significance in biology, physics, chemistry, mathematics, meteorology, research, chambers of commerce, sociology, business, public administration, communications and information technology, etc.

(8) Astronomy

Astronomy is one of the oldest branches of statistical study; it deals with the measurement of distance, and sizes, masses and densities of heavenly bodies by means of observations. During these measurements errors are unavoidable, so the most probable measurements are found by using statistical methods.
Example: This distance of the moon from the earth is measured. Since history, astronomers have been using statistical methods like method of least squares to find the movements of stars.