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Calculate the ROI on Infrastructure Automation

Programmable infrastructure and a world where you can take your data with you wherever is the future.

A new era has arrived, one in which software development practices are being applied to physical objects such as roads or bridges for greater efficiency; this idea of transparent skies could very well become a reality soon!

You can’t put a price on efficiency. The return on investment (ROI) of infrastructure automation is essential to consider before you start implementing changes that could be expensive and time-consuming.

How is Automation Valued?

The process of automating a long, manual task can be very beneficial. If you run it frequently enough and with the right system, your savings will grow exponentially as time goes on!

Regarding infrastructure automation, one of the first questions businesses ask is, “What’s the ROI?” In other words, what are the tangible benefits of automating tasks such as provisioning, configuration management, and deployments? And more importantly, how can we quantify those benefits?

In this blog post, we’ll walk you through a simple process for calculating the ROI of infrastructure automation. By the end, you’ll have a clear understanding of the financial benefits of automation and be able to make a strong case for why your business should invest in it.

The Advantages of Infrastructure Automation

Infrastructure automation is the process of automating IT infrastructure configuration, provisioning, and management. It can help organisations to manage their infrastructure more efficiently, improve service quality, and reduce operational costs. In this blog post, we will explore some of the main advantages of infrastructure automation. 

  • Improved Efficiency and Productivity

Infrastructure automation can improve efficiency and productivity. By automating configuration, provisioning, and management tasks, organisations can free up time for other activities, such as developing new features or products and providing customer support. These tasks can reduce errors and improve accuracy.

  • Improved Service Quality

Another advantage of infrastructure automation is that it can improve service quality. By automating tasks such as monitoring and maintenance, organisations can ensure their infrastructure is always running smoothly and efficiently. Additionally, automating these tasks can help identify problems early before they cause significant disruptions. Organisations can provide better service to customers.

  • Reduced Operational Costs

Finally, another advantage of infrastructure automation is that it can help to reduce operational costs. This is because automating tasks such as provisioning and management can help to reduce the need for manual intervention. Additionally, automating these tasks can help improve efficiency and productivity, which can lead to reduced labour costs. In addition, automating these tasks can also lead to reduced energy consumption and waste generation. As a result, organisations can save money on their operating costs. 

There are many advantages of infrastructure automation. Automating tasks such as configuration, provisioning, and management can help to improve efficiency and productivity, improve service quality, and reduce operational costs. If you are considering implementing infrastructure automation in your organisation, carefully weigh all of these factors to make the best decision for your business.

Calculating the ROI of Infrastructure Automation

Now that we’ve looked at some of the benefits of infrastructure automation let’s talk about how you can calculate its ROI. To do this, we’ll use a simple formula:

 (Total savings from automation – Cost of automation) / Cost of automation = ROI%

For example, you spend $5,000 per month on labour to manually provision and manage your servers. You estimate that by investing in an automated system, you could reduce that cost by 50%. The cost of the computerised system itself is $10,000 upfront plus $500 per month in maintenance costs. Using our formula, we get: 

 ($5,000 * 0.5 – $10,000 – $500) / ($10,000 + $500) = -9%

This means that over two years—the amount of time it would take to fully recoup your upfront investment—you would see a negative return on your investment (ROI). In other words, investing in automation wouldn’t make financial sense for your business now.

Conclusion:

As you can see from our example above, calculating the ROI of infrastructure automation is relatively simple. However, it’s important to note that other factors besides financial ones should be considered when deciding whether or not to automate your infrastructure. These include the size and complexity of your infrastructure, your company’s culture, and your willingness to embrace change. That said, we hope this blog post has given you a better understanding of how to calculate the ROI of infrastructure automation and why it’s such an important consideration for businesses today. Thanks for reading!

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Redefining Business Process Outsourcing through Business Process Automation

The business process outsourcing (BPO) industry is worth an estimated $190 billion. But what is BPO, and how has it evolved? In this blog post, we’ll explore the history of BPO and how business process automation (BPA) is redefining the industry.

Business Process Outsourcing: An Introduction

Business process outsourcing (BPO) has been a popular cost-cutting measure for businesses for many years. The concept is simple enough: rather than having an in-house team handle a specific process or task, you outsource it to a third-party provider. This often results in significant cost savings, as BPO providers can leverage economies of scale to deliver services at a lower cost than most businesses could achieve on their own.

However, new development is beginning to change the BPO landscape: business process automation (BPA). BPA involves using technology to automate tasks previously performed by human workers. This includes data entry, customer service, and even complex financial processes.

The History of Business Process Outsourcing

BPO had its roots in the late 1800s when American companies began outsourcing manual labour to countries with lower wages, such as China and India. This practice continued into the 20th century with the rise of telephone operators and data entry clerks. However, it wasn’t until the 1990s that BPO began to take off. 

This was primarily due to technological advances that allowed communication and collaboration across vast distances. Suddenly, businesses could outsource not just manual labour but also knowledge work to countries with lower living costs. This led to the rise of call centres and other forms of customer service outsourcing.

In recent years, there has been a shift away from traditional BPO models. This is due to several factors, including the increasing cost of labour in countries like China and India, as well as the advent of new technologies that make it possible to automate many business processes.

Benefits of BPA 

There are several benefits that businesses can reap from implementing BPA: 

  • Cost savings: One of the primary benefits of BPA is that it can help businesses save money. Companies can reduce their labour costs by automating tasks that human workers previously performed. In some cases, BPA can also help enterprises improve their efficiency and reduce other costs, such as errors and rework.
  • Improved quality: Another benefit of BPA is that it can help improve the quality of work. BPA is designed to follow pre-determined rules and procedures. This contrasts with human workers, who may make mistakes or take shortcuts that result in lower-quality work.
  • Increased capacity: A final benefit of BPA is that it can help businesses increase their capacity without incurring additional costs. This is because BPAs can work faster and for longer hours than human workers. In some cases, this increased capacity can help businesses meet spikes in demand or complete time-sensitive tasks more quickly. 

Implementing BPA

If you’re interested in implementing BPA within your business, there are a few things you’ll need to do:

  1. Determine which processes you want to automate: The first step is to evaluate your business processes and determine which ones would be candidates for automation. To do this, you’ll need to consider factors such as the complexity of the process, the frequency with which it needs to be performed, and the availability of data and applications required to support it.
  2. Identify the right tools: Once you’ve identified which processes you want to automate, you’ll need to select the right tools. There are many different types of BPAs available on the market today, so you’ll need to evaluate your needs and choose the tool best suited for your specific requirements.
  3. Define success criteria: Before beginning any automation project, it’s essential to define what success looks like. This will help you select an appropriate tool and set realistic expectations for the project’s outcomes.
  4. Implement and test: Once you’ve chosen an agency and defined success criteria, you’re ready to implement your BPA solution. Be sure to test it thoroughly before rolling it out into production to address any potential issues before they cause problems for your business operations.

Business Process Automation: The Future of BPO?

Business process automation (BPA) is the use of technology to automate repetitive, low-value tasks typically performed by human workers. BPA can be used to automate a wide variety of business processes, including data entry, invoice processing, and lead generation.

One of the critical benefits of BPA is that it can help businesses reduce their dependence on human labour. This is especially important in today’s economy, where many companies struggle to find enough qualified workers to fill open positions. By automating low-value tasks, companies can free their employees to focus on more strategic initiatives.

BPA is also more efficient and accurate than human workers, and machines can work 24 hours a day, 365 days a year, without getting tired or making mistakes. This increased efficiency can help businesses save money and increase profits.

Conclusion:

The business process outsourcing industry is evolving thanks to advances in technology rapidly. Business process automation is redefining what is possible in terms of outsourcing and helping businesses save money and increase efficiency in the process. In the future, we can only expect BPA to become more prevalent as companies continue to search for ways to cut costs and improve performance.

How to Embed Tableau or Power BI Dashboards into Web Pages without Using an Iframe

Iframe: An Introduction

Iframes are an HTML element that allows you to embed one HTML document inside another. While they are commonly used to embed videos or maps on websites, they can also be used to embed dashboards created in Tableau or Power BI. However, iframes can cause problems with security and website loading times, which is why some developers prefer to avoid using them. So, how can you embed a Tableau or Power BI dashboard on a webpage without using an iframe? Keep reading to find out.

Ways to Embed iFrame to your Web Pages

Many web developers shy away from using iframes because they can be difficult to work with. However, iframes are often the only way to embed Tableau or Power BI dashboards into web pages. If you’re looking for a way to embed your dashboards without using an iframe, read on!

Let us walk you through 3 methods for embedding Tableau or Power BI dashboards into web pages. These methods are:

1. Use Tableau’s or Power BI’s JavaScript API

2. Use a third-party service like Publitas

3. Use an open-source solution like Koalas

We’ll also provide a brief overview of each method so that you can decide which one is right for you. 

Method 1: Use Tableau’s or Power BI’s JavaScript API

  • If you’re aTableau or Power BI user, then you’re in luck! Both platforms offer a JavaScript API that allows you to embed your dashboards into web pages without using an iframe. 

The biggest advantage of using the JavaScript API is that it gives you full control over how your dashboard is rendered on the page. For example, you can choose to display the dashboard as a lightbox pop-up or inline within the page. You can also specify the size and position of the dashboard on the page. 

Another advantage of using the JavaScript API is that it’s relatively simple to set up and use. However, one downside is that it requires some knowledge of HTML and CSS in order to properly configure it. 

Method 2: Use a Third-Party Service like Publitas 

  • If you’re not a web developer and don’t have any knowledge of HTML or CSS, then using a third-party service like Publitas is probably your best bet. Publitas offers an easy-to-use platform that allows you to embed your Tableau or Power BI dashboards into web pages with just a few clicks. 

The biggest advantage of using Publitas is that it’s very user-friendly and doesn’t require any knowledge of HTML or CSS. Another advantage is that Publitas offers a wide range of customization options so that you can control how your dashboard looks on the page. 

However, there are some downsides to using Publitas. First off, it’s a paid service, so you’ll need to sign up for one of their subscription plans in order to use it. Additionally, because Publitas is a third-party service, there’s always the potential for compatibility issues between their platform and your dashboard software (e.g., Tableau or Power BI). 

Method 3: Use an Open-Source Solution like Koalas

  • Koalas is an open-source solution that allows you to embed Tableau or Power BI dashboards into web pages without using an iframe. The advantage of using Koalas is that it’s free to use and doesn’t require any knowledge of HTML or CSS. Additionally, Koalas offers a wide range of customization options so that you can control how your dashboard looks on the page. 

There are some downsides to using Koalas, however. First off, because it’s an open-source solution, there’s always the potential for compatibility issues between Koalas and your dashboard software (e.g., Tableau or Power BI). Additionally, Koalas doesn’t offer as many features as Publitas (e .g . , lightbox pop – ups), so keep that in mind when deciding which solution is right for you. 

Choosing the right method for embedding your Tableau or Power BI dashboard into a web page depends on several factors, including your level of technical expertise, budget, and desired features. We hope this blog post has helped you better understand your options so that you can make an informed decision about which method is right for you.

Conclusion: 

Iframes are commonly used to embed dashboards created in Tableau or Power BI onto websites. However, they can cause problems with security and website loading times. As such, some developers prefer to avoid using them altogether. Luckily, there are two methods that you can use to embed a Tableau or Power BI dashboard on a webpage without using an iframe. So, whether you’re a developer who wants more control over the code or someone who just wants an easy solution, there’s a method here for you.

Top Six Business Intelligence Trends 2019

Top Six Business Intelligence Trends 2019

Much like in 2018 and the years before, business intelligence is set to be among the hottest 1% of technologies in 2019 as well. With the new year in mind, both organizations and aspiring business intelligence professionals will be on the lookout for new business intelligence trends in 2019. Here is all that you need to know about ten of the most happening things in the business intelligence domain.

Before diving deep into new business intelligence trends, it is befitting to understand why business intelligence is that important a technology in the Industry 4.0 environment. At its core, the purpose of Business Intelligence is to decisively dig into past information and data to help organizations make better-informed business decisions. From that standpoint, the reason that propels companies to invest time and resources in BI remains quite consistent.

Like with all emerging technologies, the variations are often in the manners and manifestations in which a process or the organization as a whole embraces technology. With that, here are your top six business intelligence trends for 2019.

1. More dedicated time and resource for artificial intelligence

Artificial Intelligence has taken off – big time. Companies that have not yet invested time, resource, or research in AI have a lot to lose in 2019. However, that is a highly unlikely occurrence as one of every two companies that have not invested in Artificial Intelligence are all set to do so sometimes by 2019.

Quality AI platforms have the potential to work with data inputs at far greater speeds than humans. In addition, technological reliance helps address small details and prune minute errors that might easily miss the human eye. Some specific AIs have the power to provide streamlined information to customers.

By 2020 alone, 8 out of 10 companies will be using some of the other application of chatbots. A lot of valuable user interactions remain to be gleaned with the use of chatbots. For example, chatbots could easily make you a list of the words most commonly expressed or user sentiments that recur during conversations. On the face of it, this does not seem like much of a discovery. But such observations give rise to important discoverable patterns.

Put together, all these statistics reaffirm why AI is not another flash-in-the-pan moment in tech. The longer a business takes to deploy AI from here on, the harder it will become for them to catch up in the future.

Prioritized data governance

A bi-survey poll on business intelligence conducted with over 2600 business intelligence professionals concluded that the overwhelming majority of the participants rated business intelligence as a factor with above-average importance in data governance. The participants also unanimously agreed that business intelligence will have even greater say on data governance in 2019.

Consumer-centric industries like telecommunications and banking are the most likely to receive a makeover courtesy of business intelligence. Any industry that wishes to learn the value of business intelligence efforts should prioritize data governance over many other aspects.

Simultaneously, a lot of effort must also go into ensuring the accuracy of the data. If the information relayed is faulty, duplicate, or not completely accurate, it could result in serious repercussions. For one, employees would lose faith in the business leaders, making focusing on business intelligence not worth the effort in the first place.

Apart from this, it is important to note that the General Data Protection Regulation (GDPR) is in full swing across the European Union. With that, it becomes even more important to lay emphasis on business methods that treat consumer data most sensitively. Moreover, a regulation similar to the GDPR is set to come to California in 2020. Given that so much happening around data policies, it does not come as a surprise that business intelligence is molding accordingly.

Big data and for nuanced organizational support

Application of Business Intelligence goes beyond suggesting improvements in a company’s operations. The use of data for a positive social change keeps growing by the day. It is especially important to do so with the help of structured data banks. By streamlining efforts across a range of organizations, labor outputs can be maximized and extra data can be pooled into the development of new ideas.

There’s also been growing talk about making use of data for purely altruistic purposes in 2019. There have been companies that have focused on it for a reasonable amount of time. Way back in 2013, MasterCard made launched an independent subsidiary for data applications dedicated to the greater good. The best way for this to work out is to club big data into corporate social responsibility plans of organizations.

Success in achieving this could help businesses realize success beyond business profits. Sample this: most studies suggest that most millennials are more likely to work at companies that give back. So businesses have the chance to appeal to a larger pool of talent while figuring new ways to leverage business intelligence for social good.

Greater business intelligence integration in small companies

In 2018, Business Intelligence was particularly favored by organizations with fewer than 100 employees. This is the most conclusive evidence that the size of an organization does not go a great deal in influencing their decision to adopt Business Intelligence. It’s also a common observation that in smaller organizations, employees have a greater feeling of ownership toward individual business processes. This makes adoption of Business Intelligence easier in smaller organizations.

The rising use of Business Intelligence software across smaller organizations is all set to continue throughout 2019. Some credit would definitely go to an increasingly large pool of providers that are striving hard to make their services both accessible and affordable. The most crucial takeaway is that even employees with a tight shoestring budget can make decisive use of business intelligence.

Data interpretation through storytelling

Data storytelling has never been as big as it will be in 2019. Numerous conferences highlight the impact of data storytelling throughout industries. Conferences like the Data Visualization Summit in San Francisco explore the interpretation of data through the nodes of storytelling. Owing to the way data storytelling assigns the necessary context to data, data storytelling will remain a vital aspect of Business Intelligence throughout the length of 2019.

Skill in data literacy is required irrespective of how advanced the business intelligence platform is. Without that skill, figures cannot be interpreted and shaped into meaningful insights from which a business can learn. To rephrase, data literacy skill can add meaning to advanced data and how it can be meaningful to the business.

Not many people have the skill to make meaning of a spreadsheet filled with random numbers. And even the best-explained bar graphs are not easy pies for a bulk of the audiences. Nevertheless, the ability to storify data is still an indispensable skill.

Extended outputs by self-service business intelligence

With a self-service business intelligence interface, most companies might feel they do not need help from data scientists. But it is the opposite scenario when you dive deep into analytics. That is why four out of five businesses in a study involved in the UK plan on hiring data scientists in 2019.

The same study also pointed out how companies that use or plan on using BI believe that it gives them an edge over the competition. For companies that are yet to decide on taking the plunge into BI, self-service software would be a great start. Before making an investment into such tools, a business much look at what they really want in return. More importantly, a business should evaluate how seamlessly Business Intelligence gels with the overall operation of the company.

Futran Solutions specializes in delivering composite Business Intelligence solutions and resources. As applications of Business Intelligence in business is evolving, so are the resources that shoulder these needs within an organization. Speak to a Futran Business Intelligence specialist today. Find out how we help you achieve your business and marketing objectives.

Seven Hottest Analytics And Big Data Trends For 2019

The Big data is the vast volumes of data generated from a number of industry domains. Big data generally comprises data collection, data analysis and data implementation processes. Through the years, there’s been a change in the big data analytics trends – businesses have swapped the tedious departmental approach with data approach. This has seen greater use of agile technologies along with heightened demand for advanced analytics. Staying ahead of the competition now requires businesses to deploy advanced data-driven analytics.

When it first came into the picture, big data was essentially deployed by bigger companies that could afford the technology when it was expensive. At present, the scope of big data has changed to the extent that enterprises both small and large rely on big data for intelligent analytics and business insights. This has resulted in the evolution of big data sciences at a really fast pace. The most pertinent example of this growth is the cloud which has let even small businesses take advantage of the latest technology.

The modern business is floating on a stream of never-ending information. However, most businesses face the challenge of extracting actionable insights from vast pools of unstructured data. Despite these roadblocks, businesses are deriving from the tremendous opportunities for growth presented by big data. Here is all that would count as the hottest big data analytics trends of 2019.

Booming IoT Networks

Big-Data-Trends-2

Like it’s been through 2018, Internet of Things (IoT) will continue to trend through 2019, with annual revenues reaching way beyond $300 billion by 2020. The latest research reports indicate that the IoT market will grow at a 28.5% CAGR. Organizations will depend on more structured data points to gather information and gain sharper business insights.

Quantum Computing

 

Industry insiders believe that the future of tech belongs to the company that builds the first quantum computer. No surprise that every tech giant including Microsoft, Intel, Google and IBM are racing for the top spot in quantum computing. So, what’s the big draw with quantum computing? It allows seamless encryption of data, weather prediction, solutions to long-standing medical problems and then some more. Quantum computing allows real conversations between customers and organizations. There’s also the promise of revamped financial modeling that helps organizations develop quantum computing components along with applications and algorithms.

Analytics based on Superior Predictive Capacity

 

More and more organizations are using predictive analysis to offer better and more customized insights. This, in turn, generates new responses from customers and promotes cross-selling opportunities. Predictive analysis helps technology seamlessly integrate into variegated domains like healthcare, finance, aerospace, hospitality, retailing, manufacturing and pharmaceuticals.

Edge Computing

 

The concept of edge computing among other big data trends did not just evolve yesterday. Network performance streaming makes use of edge computing pretty regularly even today. To save data on the local server close to the data source, we depend on the network bandwidth. That’s made possible with edge computing. Edge computing stores data nearer to the end users and farther from the silo setup with the processing happening either in the device or in the data center. Naturally, the entire procedure will see an organic growth in 2019.

Unstructured or Dark Data

 

Dark data refers to any data that is essentially not a part of business analysis. These packets of data come from a multitude of digital network operations which are not used to gather insights or make decisions. Since data and analytics are increasingly becoming larger parts of the daily aspects of our organizations, there’s something that we all must understand. Losing an opportunity to study unexplored data is a big-time potential security risk.

More Chief Data Officers

 

The latest trendy job role on the market is that of a Chief Data Officer. Top-tier human resource professionals are looking for competent industry professionals to fill this spot. While the demand is quite high, the concept and value of a CDO are largely still undefined. Ideally, organizations are preferring professional with knowledge in data analysis, data cleaning, intelligent insights and visualization.

Another Big Year for Open Sourcing

 

Individual micro-niche developers will invariably step up their game in 2019. That means we will see more and more software tools and free data become available on the cloud. This will hugely benefit small organizations and startups in 2019. More languages and platforms like the GNU project, R, will hog the tech limelight in the year to come. The open source wave will definitely help small organizations cut down on expensive custom development.

Making of a Storm: What Happens to Dark Data in Analytics and Big Data?

Making of a Storm: What Happens to Dark Data in Analytics and Big Data?

Dark data is the kind of data that does not become a part of the decision making for organizations. This is generally the data from logs and sensors and other kinds of transactional records which are available but generally ignored. The largest portion of the yearly big data collected by organizations is also dark data.    

Dark data does not usually play a vital role in analytics because:

  1. Companies do not want to use their bandwidth on additional data processing
  2. There’s a lack of technical resources
  3. Organizations do not believe dark data adds any value to their analytics

All of these are valid reasons for the data taking the back seat. But today we have a string of data-centric technological advances. Together, they present a heightened ability to ingest, source, analyze, and store large volumes of data. With that, it becomes important for organizations to recognize this largely untapped volume of data.   

The conventional way to use this data would be to systematically drain all of it into a waterhouse of data. This is followed by the identification, reconciliation, and rationalization of the data. The reporting follows soon after. While the process is pretty methodical, there might not be as many projects that truly call for such a need.   

The Immense Volume of Dark Data in Enterprise

Dark Data Big Data Analytics 2

At the moment, we have solid  evidence to suggest that as much as 90% of all data used in enterprises could be dark. Since industries are now storing large data volumes in the ‘lake’, it should be natural to tag the data appropriately as it gets stored. Perhaps the key is to extract the metadata out of this data and then storing it. 

Profiling and exploring the data can be done using one or a combination of tools that are already available in the market. Cognitive computing and machine learning can further increase processing power and open up possibilities of making intelligent use of dark data.  

Dark data may or may not have an identifiable structure. For example, most contacts and reports in organizations are structured. But over the course of time, they add up to the pile of dark data. Unstructured data can be small bits of personally identifiable info like birth dates and billing details. In the very recent past, this type of data would remain dark.

Machine learning can help organize this data in an automated manner. It can then be connected to other attributes of data to generate the complete view of the data. Using geolocation data is slightly trickier though. While it is extremely valuable, the lifespan is rather short. A collection of historical geolocation data sets can be further leveraged using machine learning to aid in predictive analysis of data.    

Recognition of regular data as dark data

Other sets of data often considered “dark” in the past include data from sensors, logs, emails, and even voice transcripts. The longest stretch they would get in terms of application would be vested in troubleshooting purposes. Not many would look to make such data a part of actual decision making. Now that we can convert voice or text (and vice versa) and use the data to gather intelligence, there are many use cases that draw advantage of data traditionally considered dark.    

An IDC estimate suggests that the total volume of data could be somewhere close to 44ZB (zettabytes) in 2020. This data explosion will be influenced by many new data generators like the Internet of Things. And unless we light up this data with new technology and processes, a large volume of it will continue to stay dark.  

The first and obvious step will be to make all the dark data available for exploration. The second step is to categorize the data, scrape out the metadata and do a quality check for all the extracted data. Modern tools for data management and data visualization provide the ability to explore the data visually. This determines whether or not the data can be illuminated to remove the visual noise.      

The myriad advances in Artificial Intelligence (AI) will definitely aid in uncovering the secrets of the oft-ignored “dark data”. However, the trick is still in using the data prudently. Wrong use of data will inadvertently result in incorrect predictions and may invite regulatory sanctions.

The vastness of dark data demands handling by Big Data and AI experts. In addition, there needs to be a clear plan about the application of the data once it is sorted. At Futran Solutions, we work with a pool of incredibly talented Big Data and Artificial Intelligence experts who can help your organization make the most of dark data. Contact us today to talk solutions in big data and artificial intelligence.