Akscellence Info

Problem Statement

Nowadays enterprises want applications to be deployed in such way that new version of the application can be introduced to production without making user see that application went down even for some time. From the user’s and the company’s point of view it’s the best possible scenario of deployment since new features can be introduced and bugs can be eliminated without any outage.

Possible Solutions

Objective: Nowadays enterprises want applications to be deployed in such way that new version of the application can be introduced to production without making user see that application went down even for some time. From the user’s and the company’s point of view it’s the best possible scenario of deployment since new features can be introduced and bugs can be eliminated without any outage. 

It is more critical in case of Sitecore, since enterprise not only have to deploy new functional change but also upgrade the version of Sitecore time to time. 

There are below two strategies A. Manual Strategy: This is an old strategy where we can follow below steps to avoid any downtime: (Assumption is that we have multiple load balancer CD(content delivery) servers)

Create the copy of web (web_deploy) and core (core_deploy) databases.

Create additional solr cores for web (sitecore_web_index_deploy) and core (sitecore_core_index_deploy).

Change authoring server configuration and point to above solr cores (sitecore_web_index_deploy and sitecore_core_index_deploy).

Re-index web and core database solr cores.

Change authoring server configuration and point back to original solr cores (sitecore_web_index and sitecore_web_index).

Take CD1 out from load balancer.

Change CD1 configuration and point to above created web and core databases along with solr cores.

Add CD1 back to load balancer.

Take CD2 out from load balancer.

Change CD2 configuration and point to above created web and core databases along with solr cores.

Add CD2 back to load balancer.

Run database scripts on master, web and core databases.

Deploy code change to authoring server.

Re-index the content on solr for web and core database.

Take CD1 out from load balancer.

Deploy application/code changes to CD1 server.

Change CD1 configuration and point back to original web and core databases along with solr cores.

Add CD1 back to load balancer.

Take CD2 out from load balancer.

Deploy application/code changes to CD2 server.

Change CD2 configuration and point back to original web and core databases along with solr cores.

Add CD2 back to load balancer.

Pros: This strategy doesn’t need additional infrastructure which in turn save cost. Cons: We need to involve larger team (application, infrastructure, SQL admins etc.) every time whenever there is a deployment. Rollback is complex as we need to perform most of the above steps again. B. Blue Green Strategy: Blue Green is a “deployment strategy that relies on two identical production infrastructures that alternate between active and inactive. One environment is referred to as blue (inactive), and the duplicate environment is dubbed green (active)”. In this approach, as you prepare a new release of your software you do your final stage of testing in the blue environment. Once the software is working in the blue environment, you switch the router so that all incoming requests go to the blue environment (blue becomes green) and the green one is now idle (green becomes blue). Since the databases (SQL and mongo) are shared resources between authoring and production and any change there can affect both. Additionally, since we are dealing with a CMS, we must expect that authors regularly introduce changes to the database. Challenge is more in case of Sitecore since schema is controlled by Sitecore. We can have below approach to overcome this:

Synchronize content from green environment to blue environment.

Upgrade new version of database changes in blue environment.

Upgrade Sitecore application servers in blue environment.

Re-Index content on solr (content is new after synch) in blue environment.

Test application thoroughly.

Make blue environment live (green) and live environment ideal (blue)

Pros: We don’t need to involve larger team (application, infrastructure, SQL admins etc.) every time whenever there is a deployment. Rollback is easier which can be achieved by just switching blue to green and green to blue. Cons: This strategy requires additional infrastructure which in turn cost more. Pre-requisite for this strategy:

Content author will not author during deployment.

Session state is out-proc and session database is common between blue and green.

Problem Statement

The ever-increasing connectivity of people and machines has changed market demands. And, in order to keep up, and at the same time stay competitive, businesses need to adjust to these demands by digitizing their processes and business models

Possible Solutions

The ever-increasing connectivity of people and machines has changed market demands. And, in order to keep up, and at the same time stay competitive, businesses need to adjust to these demands by digitizing their processes and business models. And the content management systems (or digital experience platforms) today serves as the catalyst for a business’ complete digital transformation. When it comes to choosing a CMS that helps create personalized digital experiences quickly, Sitecore is what most enterprise-level businesses prefer. After all, Sitecore has been recognized as the global leader in 2018 Gartner Magic Quadrant for Digital Experience Platforms, and has been among the leaders for the ninth year in a row. Sitecore is a leading enterprise-level CMS built on ASP.NET used by organizations globally to create seamless and personalized digital experiences. What makes the Sitecore CMS unique is the ability it provides to web content editors and marketers to have complete control over all aspects of their website from social integration and blog posts to advanced personalization, e-commerce and more. Further, Sitecore has its own reusable components repository which has modules contributed by developers and many of these modules are free along with paid ones. Built-in analytics: It comes with in-built analytics feature, empowering website owners to create personalized content unlike many popular CMSs such as Drupal. Multi-lingual support: For business serving customers across multiple locations is important, however, it requires massive efforts. Also, displaying the content that speaks the language of customers is equally tough. With Sitecore, you can easily create any number of pages displaying content of multiple languages. Complete solution to meet varied needs: Unlike other content management systems, Sitecore is a complete enterprise management system powered with in-built apps (such as MongoDB), DMS, commerce and digital marketing tools, etc. Thus, implementing this single system can help resolve multiple needs of your business. Browser-compatible: Sitecore-powered website can run on any modern browser which is not the case with all popular contemporary CMSs. Personalized Retail Experience: Unlike many other CMSs, Sitecore offers the capabilities of commerce, content, and experience – tied together in a single platform, and thereby helps you deliver personalized shopping experiences that delight the right customer with the right offer, which in turn helps boost profitability. Conclusion: Do you wish to use the Sitecore CMS for your website development project? Don’t know where to begin? AKSCELLENCEINFO can provide you with consultation service using our knowledge and expertise of the Sitecore platform to advice on how you can use Sitecore to its fullest potential.

Problem Statement

Why Traditional Business Intelligence techniques are failing to resolve today’s Business problems?

Possible Solutions

The goal of Business Intelligence is to provide right-time information to management and function as a decision support mechanism. Below are few challenges in using Traditional Business Intelligence techniques.

IT spends months or even years building out dashboards and reports based on user requirements. Users get their hands on the dashboards and reports and immediately request changes based on what they see. IT then spends more weeks or months implementing those changes. Users spend time with the new dashboards and reports, and then come up with more requirements as new questions come to mind. It’s a long, painful cycle.

The extracted and filtered data need to be transformed into operational data warehouses by ETL (Extract, Transform and Load) processes. Then, techniques such as OLAP cubes are used to enhance the data storage to deliver better analytical performance. BUT as long as they are query- and cube-based solution, maintaining associations in the data requires hand coding — and therefore lots of time and money which results as higher cost solution.

If business user wants to get deeper insight into an aspect of the business which required to add a new data source, then developer would have to go back and re-code the associations by hand all over again. Business users failed to explore data, make discoveries, and uncover insights that can be used to help them solve their problems in new ways.

The performance gains are overshadowed by the enormous complexity and additional delays imposed by an ETL process.

Traditional BI required Specialized consultants to perform ETL tasks and set up the Data Warehouses that few decision-makers will use.

Problem Statement

Spreadsheets aren’t always the right tools for many BI tasks

Possible Solutions

While Microsoft Excel and other spreadsheets have existed now for many years, spreadsheets aren’t always the right tools for many BI tasks.

Creating charts in Excel many times do not give the desired results. Oftentimes you don’t know how to interact with the spreadsheet to show your results in the best possible circumstances.

Spreadsheets also fall down when the data isn’t well-structured or can’t be sorted out in neat rows and columns.

Spreadsheets also have issues if you are trying to create a report that spans multiple data tables or that mixes in Structured Query Language (SQL)-based databases.

Answers to these problems is to adopt a BI solution. BI as a concept has been around since quite a time, but it has now evolved from basics to much more sophisticated information.

This information ranges across everything from customer behaviors to IT infrastructure monitoring to even long-term fixed asset performance.

Separately tracking such metrics most businesses can do regardless of the tools. Combining them, especially disparate results from metrics into understandable and actionable information, is the art of BI.

Power BI is one such BI tool from Microsoft.

Problem Statement

Publishing Power BI reports with Python visuals

Possible Solutions

Courtesy of Blog: https://powerbi.microsoft.com/en-us/blog/python-visualizations-in-power-bi-service/ With our February update of Power BI, you can now share, publish and view Python visuals in your reports and dashboards. This update extends the support for Python in the Power BI Desktop to the Power BI service. This ability completes the support for Python in Power BI, enabling you to use Python scripts to prepare your dataset, apply sophisticated analytics or machine learning in the Power BI Desktop & personal gateway, and then plot the results in your Power BI reports using any of the hundreds of open-source Python visualization packages. 

Publishing your reports to the Power BI service will automatically refresh these visuals with the latest data when a user views the report. Just like the support for R visualizations in Power BI, your Python visualizations will update with cross-filtering in the report, but the visuals are not interactive. Publishing Power BI reports with Python visuals You can add a Python visual to your Power BI report by selecting the Python visual icon from the Visualizations pane in the Power BI Desktop, which adds a placeholder Python script to the report. 

Adding the fields from your dataset to the Values pane of the Python visual automatically adds the fields to the pandas Data Frame in the placeholder Python script. You can then use the Python script to create the visualization you’d like. See Create Power BI visuals using Python for instructions. When you publish the report to your Power BI workspace, the visuals are automatically refreshed when the report is viewed. Note, there are a few constraints on the size of the serialized Python data frame, and the execution resources used by the Python scripts, that are summarized below. Python packages and versions The initial version of the Python runtime supported in Power BI includes versions 2.7.15 and 3.7.1. We will periodically update the runtime to keep it current.

Problem Statement

Why Manufacturing companies need Analytics

Possible Solutions

The top three areas where analytics can improve manufacturing performance:

Better forecasts of product demand and production,

Understanding plant performance across multiple metrics and

Providing faster service and support to customers

Analytics-driven order management drives customer loyalty

Organizations can use predictive analytics to redesign their order management processes to increase revenue, brand visibility, and customer satisfaction.

Analytics that bring out what elements of the order management or supply-chain processes are hampering customer experience are most useful. Is it master data, supply chain visibility, demand planning, operational issues with order processing, or logistics issues?

EVERY Business might be missing huge potential growth opportunities if its not using the power of Data Analytics, Data Science, AI and Machine Learning. The biggest challenge is that these opportunities aren’t visible through your traditional reporting. One needs to mine into its business data to find the gems of wisdom so far hidden to them.

Table of Contents

What are the Challenges?

Best Rewards Generated by Data Analytics

Differences in value

What are the Challenges?

The challenge isn’t that it’s complex and costly to do; the challenge is the mindset. Companies don’t realize their data’s value and are complacent with traditional mundane reporting done for just generating charts and graphs. Honestly, most people think doing digitization of their Business was enough to reap its benefit to the fullest, failing to realize that digitization only sets the launching pad to start storing and organizing your business data.

One needs to go deeper into the data to mine the gold value that it has within it. The good news is that doing this is far easier and much more rewarding than mining gold mines. The need of the hour is to take the first step towards it.

One can ask, where do you start on this. There isn’t any fixed way to do this, but still, there are pretty common ways that could be accomplished quickly.

Best Rewards Generated by Data Analytics

Let’s layout specific steps that one needs to initiate to get the best rewards generated by Data Analytics.

Data Source: Find and list out the current source(s) of data used by businesses. Data experts should be able to see the data formats and suggest how to converge these multiple sources of data into a single source of truth for the Business. This has been the traditional standard way to store and standardize data, even for traditional reporting purposes. The good news is that the exact needs to be done at this stage to enable Data Analytics reports.

The way the Data Analytics report consumes data is similar to traditional reporting, but its data is drastically different. If this point is still in doubt, its better to arrange for a working demo with Data Analytics experts/companies to understand and appreciate the difference. They are listing few points to give it a thought on this.

Reporting is “the process of organizing data into informational summaries to monitor how different areas of a business are performing. “Measuring core metrics and presenting them — whether in an email, a slide deck, or online dashboard — falls under this category.

Analytics is “the process of exploring data and reports to extract meaningful insights, which can be used to understand better and improve business performance.”

Differences in Value

Reporting provides you with information; the analytics give you insights.

Reporting raises questions; analytics attempts to answer them.

Both are valuable but for different purposes.

What’s so special about analytics?

Analytics explains the “why?” and the “so what?”

How? Because it’s dynamic — on more than just a scale of time or interval. You should be able to contort the data to whatever you need. If what you see is a set of standard metrics, it isn’t truly an analytical assessment (or an analytics product).

From this active investigation into the data, you can derive real-life recommendations about your Business. What should you change about your practices to improve your core metrics?

In this way, analytics is also notable because it’s highly actionable. It must be acted upon. The value of analytics is only truly delivered upon your follow-up, only if you come up with those recommended next steps and execute them—more on that below.

3. Find KPIs: There are two ways to approach KPIs (Key Performance Indicators) to generate Analytics reports. Some KPIs are pretty standard, and each Business knows some KPIs that they need to track. This list can be checked against the standard KPI list used by similar businesses to know what is missing and add that to the list.

When using advanced analytics using the power of Machine learning/AI, there is a different approach to KPIs. AI digs into business data and finds the pattern for you. It suggests what right KPIs relevant for the Business and shares corresponding business insights that affect Business directly, either positively or adversely are. 

This is a good approach but is suggested only after Business matures into using business analytics.

4. Dashboard Time: Next is the time to showcase all hidden gems in data, visually as dashboards. They do look similar to regular reports till you start using them. User realizes the ease of changing the dimensions in reports very soon, and one report virtually acts as many reports put together with a few clicks to switch from one to the other. 

This becomes very powerful for a business analyst who uses his/her business knowledge to find a correlation between different reports and find the business insights.

5. Advanced Analytics: Easy to use and easy to switch to different views works well with experienced business analysts; however many times its desirable that Artificial Intelligence itself digs into the data and suggests business insights which can be directly used by Business to know what is working well and what is not and what are the reasons for that. 

Then action can be taken based on that to push right levers to induce engine of growth and halt source(s) of loss.