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the evolution of the enterprise data management industry five years out
The Evolution of the Enterprise Data Management Industry: Five Years Out

Enterprise Data Management industry is predicted to rise with a CAGR of 9.3% over the forecast period by generating a revenue of $126.9 billion by 2026.

Enterprise Data Management program collates all the data related with making major decisions and building a strategy for the organization. Enterprise Data Management helps to identify the compliance, operating efficiencies, risksand build client relationship, which results in data quality, control on the data and information storage.

Rise in the use of data management application in many of the organization is predicted to drive the Enterprise Data Management industry over the forecast period. The demand for data management has increased due to handling large data sets by data integration, data profiling, checking the quality of data, metadata management and many other data related problems. Moreover, enterprise data management helps in sharing, consistency, reliability and governing information to the organization for taking major decisions, and this is predicted to be the major driving factor for the industry.

Data privacy is predicted to hamper the growth of the industry during the forecast period. Most of the companies handle data with the help of open source applications which includes various processes and algorithms. Most of the processes and algorithms are run through open sources which enable hackers to get the source code without difficulty if the data are not highly protected. These are the biggest restraints for the growth of the industry in the forecast period.

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The major players in Enterprise Data Management industry are Amazon Web Services, Inc., TierPoint, LLC., VMware Inc.,Microsoft, HP Development Company, L.P.,Cloudera, Inc.,SAS Institute Inc.,SAP SE,IBM Corporation, andNTT Communications Corporation among others.

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your ecommerce pros can easily use augmented analytics
Your eCommerce Pros Can Easily Use Augmented Analytics

eCommerce and online shopping businesses employ professionals in many roles including sales managers, marketing professionals, social media experts, product and service professionals and others. Together, every role in a business is designed to create a team that will ensure business success and, with eCommerce exploding, it is easy to think that the right people can get the job done.

But, there is another component to success, especially today. Given the competitive environment and market with thousands of eCommerce sites and apps, it is imperative that the business have a measurable, fact-based view of results and enable its team members (no matter their role) to access tools and solutions that will give them the information they need to succeed, to improve results, to come up with new ideas for products and services, to target customers appropriately, to bundle products, to shift pricing and marketing approaches and more!

But, eCommerce business users do not have the time or the inclination to adopt new technology and software. They are often overwhelmed with tasks and responsibilities so, making it easier for them to understand and analyze results is crucial. An augmented analytics solution that integrates with an eCommerce solution like Shopify can provide pre-built templates, reports, KPIs and in-depth analysis of customer lifetime value, customer cohorts, trends, sales results and other important aspects of eCommerce business.

It is easy to implement analytical capability using a solution that integrates Shopify with augmented analytics, and provide your users and business a meaningful way to compete and succeed.

Contact us to today to find out how SmartenApps for Shopify can help you achieve your goals.

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statistical hypothesis testing step by step
Statistical Hypothesis Testing: Step by Step

 

Hypothesis by statisticalaid.com

                                                     Image Source: Statistical Aid: A School of Statistics

What is hypothesis testing?

In statistics, we may divide statistical inference into two major part: one is estimation and another is hypothesis testing. Before hypothesis testing we must know about hypothesis. so we can define hypothesi as below-

A statistical hypothesis is a statement about a population which we want to verify on the basis of information which contained in a sample.

Example of statistical hypothesis

 

Few examples of statistical hypothesis related to our daily life are given below-

  • The court assumes that the indicted person is innocent.
  • A teacher assumes that 80% of the student of his college is from a lower-middle-class family. 
  • A doctor assumes that 3D(Diet, Dose, Discipline) is 95% effective to the diabetes patient.
  • A beverage company claims that its new cold drinks are superior to the other drinks available in the market, etc.

 

A statistical test mainly involves four steps:

  • Evolving a test statistic
  • To know the sampling distribution of the test statistic
  • Selling of hypotheses testing conventions
  • Establishing a decision rule that leads to an inductive inference about the probable truth. 

 

Types of statistical hypothesis

  • Null hypothesis
  • Alternative hypothesis

 

Null hypothesis

 

A null hypothesis is a statement, which tells us that no difference exists between the parameter and the statistic being compared to it. According to Fisher, any hypothesis tested for its possible rejection is called a null hypothesis and is denoted by H0.

Alternative hypothesis

 

The alternative hypothesis is the logical opposite of the null hypothesis. The rejection of the null hypothesis leads to the acceptance of the alternative hypothesis. It is denoted by H1.

For example, with a coin-tossing experiment, the null and alternative hypothesis may be formed as,

H0: the coin is unbiased.

H1: the coin is biased.

 

Depending on the population distribution, the statistical hypothesis are two types,

  • Simple hypothesis:when a hypothesis completely specifies the distribution of the population, then the hypothesis is called a simple hypothesis.
  • Composite hypothesis: when a hypothesis does not completely specify the distribution of the population, then the hypothesis is called a composite hypothesis…(Source)

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