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building a cyber physical grid for energy transition part 4 of 4
Building A Cyber-Physical Grid for Energy Transition (Part 4 of 4)

The new distributed energy market imposes new data and analytics architectures


Part 1 provided a conceptual-level reference architecture of a traditional Data and Analytics (D&A) platform.

Part 2 provided a conceptual-level reference architecture of a modern D&A platform.

Part 3 highlighted the strategic objectives and described the business requirements of a TSO that modernizes its D&A platform as an essential step in the roadmap of implementing its cyber-physical grid for energy transition. It also described the toolset used to define the architecture requirements and develop the future state architecture of TSO’s D&A platform.

This part maps the business requirements described in part 3 into architectural requirements. It also describes the future state architecture and the implementation approach of the future state D&A platform.

TRANSPOWER Future state Architectural Requirements

In order to develop the future state architecture, the business requirements described in part 3 are first mapped into high-level architectural requirements. These architectural requirements represent the architectural building blocks that are missing or need to be improved in each domain of TRANSPOWER enterprise architecture in order to realize the future state architecture. Table 1 shows TRANSPOWER high-level architectural requirements.

Table 1: TRANSPOWER high-level architectural requirements

The Future State Architecture of TRANSPOWER Data and Analytics Platform

Figure 1 depicts the conceptual-level architecture of TRANSPOWER digital business platform.  Modernizing the existing D&A platform is one of the prerequisites for TRANSPOWER to build is digital business platform. Therefore, TRANSPOWER used the high-level architectural requirements shown in Table 1 and the modern data and analytics platform reference architecture described in Part 2 to develop the future state architecture of its D&A platform. Table 2 shows some examples of TRANSPOWER business requirements and their supporting digital business platform applications as well as the D&A platform architectural building blocks that support these applications. These D&A architectural building blocks are highlighted in red in Figure 2.

Figure 1. Conceptual-level architecture of TRANSPOWER digital business platform

Table 2: Examples of TRANSPOWER business requirements and their supporting digital business platform applications and D&A platform architectural building blocks

Figure 2: Examples of the new architectural building blocks

The Implementation Approach

After establishing the new human capital capabilities required for the implementation of the digital business transformation program, TRANSPOWER started to partner with relevant ecosystem players and deliver the program.

The implementation phase of the D&A platform modernization was based on the Unified Analytics Framework (UAF) described in Part 3. The new D&A applications and architectural building blocks described in Table 2 are planned and delivered using Part II of the UAF (including Inmon’s Seven Streams Approach).  According to Inmon’s Seven Streams Approach, stream 3 is the “driver stream” that sets the priorities for the other six streams, and the business discovery process should be driven by the “burning questions” that the business has put forward as its high-priority questions. These are questions for which the business community needs answers so that decisions can be made, and actions can be taken that will effectively put money on the company’s bottom line. Such questions can be grouped into topics, such as customer satisfaction, profitability, risk, and so forth. The information required to answer these questions is identified next. Finally, the data essential to manufacture the information that answers the burning questions (or even automate actions) is identified. It is worth noting that the Information Factory Development Stream is usually built topic by topic or application by application. Topics are usually grouped into applications and a topic contains several burning questions. Topic often spans multiple data subject areas.

Figure 3 depicts the relationship between Burning Questions, Applications, Topics Data Subject Areas, and the Information Factory Development Stream.

Figure 3. Relationship between Burning Questions, Applications, Topics Subject Areas, and the Information Factory Development Stream



In many cases, modernizing the traditional D&A platform is one of the essential steps an enterprise should take in order to build its digital business platform, therefore enabling its digital business transformation and gaining a sustainable competitive advantage.  This four-part series introduced a step by step approach and a toolkit that can be used to determine what parts of the existing traditional D&A capabilities are missing or need to be improved and modernized in order to build the enterprise digital business platform. The use of the approach and the toolkit was illustrated by an example of a power utility company, however, this approach and the toolkit can be easily adapted and used in other vertical industries such as Petroleum, Transportation, Mining, and so forth.

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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

                                                     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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