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Category: Technology

The Three Rs of Automation Discovery

Technology | Banking Automation | Digital Transformation | Enterprise Automation

The Three Rs of Automation Discovery

April 29, 2022
Automation Discovery

By Saurabh Bhatia

Before you begin the process of automation at your company, you need to check for the Three Rs of Automation Discovery which will ensure the long-term success of automation implementation. In this article, we will look at:

Automation Discovery’s 1st R: Reimagine the vision

Automation Discovery’s 2nd R: Return on investment calculation

Automation Discovery’s 3rd R: Reusing Automation

Why are The Three Rs of Automation Discovery so important?

 

So you’ve decided that you want in on the automation game. Great! Now you’re in the all-encompassing process of discovery and implementation. This is where you, the organization that is looking to adopt automation, needs to articulate your business needs, asses how automation will impact your operations, and then determine a successful implementation roadmap with your tech partner. Here are three components, “The Three Rs”, that should guide your approach towards a successful discovery and automation implementation.

Automation Discovery’s 1st R: Reimagine the Vision

Begin by reinventing the way you look at your operations and its processes. It goes without saying that you should start with standard process automation first to demonstrate its value. But during the discovery phase, explore all tools that will compound the value of automation for your organization. For example, if you are looking to automate the mortgage lending process, take a step back and examine the added benefit your organization will get from adding power BI dashboards or analytics systems that provide more actionable insights or discovery tools which identify which other processes and tasks could be automated.

Keep in mind a vision of an organization fueled by hyperautomation as you explore various options in addition to standard process automation namely, intelligent document processing, business intelligence reporting, process mining, chatbots, etc. There is a full stack of options you can explore to heighten the benefits of technology and establish that future state of hyperautomation.

Automation Discovery’s 2nd R: Return on Investment Calculation

Understandably the most common driver in decision making is about the returns you will see with the option of automation (or any other technology solution). Determine how you will calculate ROI prior to beginning work on the solution. We recommend creating a business case with a very clear prioritization matrix and roadmap of automation and its benefits. This will determine the focus both long term and short term.

Pro Tips:

  • Determine which licenses and automation tools are most useful and cost effective for the overall solution
  • Adopt licenses and tools and build your infrastructure to support the scalability of automation (and sunset those that are no longer in use)
  • Don’t forget, to consider monetary metrics which deeply impact the business in your calculations

Automation Discovery’s 3rd R: Reusing Automation

Some organizations feel like they’re starting from scratch every time they introduce a bot to their processes. In reality, you reutilize some parts of your previous assets (from one bot to another) to make automation faster. For example, if you have multiple processes, teams and departments using the same application or program, we encourage you to create reusable assets with the first bot so when you introduce a second bot, you are reutilizing the same assets with the same application or program. This fuels enterprise scale adoption as a team or functional area and can demonstrate the value of automation to other departments, which will encourage further adoption of the technology.

Pro Tips:

  • Divide your projects into components and create reusable libraries, which makes it easier to reuse automation for further processes
  • When designing the bots, focus on smaller components within them but always keep in mind how this will affect future projects in a positive way

Why are The Three Rs of Automation Discovery so important?

  1. By reimaging your vision, you create a collective, growth oriented and collaborative mindset within the organization as it pertains to digital transformation
  2. The right ROI calculations help you determine which processes will garner better returns both short term and long term
  3. Reusing automations also makes your implementation scalable with faster adoption across teams and departments

Automation is transforming the way we run businesses, transact, and engage in the world. The key to achieving our vision and maximizing the possibilities of technology in our organizations is rooted in having the right approach. And the three Rs in automation is great place to start.

Find out more about enterprise automation, book a meeting with us today to see how we can accelerate your digital transformation journey.

Mastering Quality Assurance

People & Culture | Leadership | Software Development

Mastering Quality Assurance

March 22, 2022
Quality Assurance

I grew up in a small town in the Southern part of India, called Udupi. Today, Udupi is a thriving metropolis known for its trademark cuisine, educational institutions and historical sites. When I was growing up, it still had the charm of a small town and provided the perfect environment for my childhood.

The focus at our home was heavily on academics. My father would tell me to study, get a degree and then chase my goals. I realized very early on that I wanted to be an engineer one day. The action of putting together various elements to create a complete entity was fascinating to me.

My introduction to Quality Assurance

After graduating with a degree in computer science engineering at PES University, an institution on the forefront of technology education and research, I worked as a Quality Assurance (QA) Specialist for a company in India. The role of a QA specialist is to ensure that the product, at every stage of its development and presentation to the client complies with the company’s quality standards. I found myself adept at finding as many issues in the product as possible and it gave me tremendous satisfaction in knowing that my review helped my team succeed in delivering the best viable product to the client.

After four years of being a QA Specialist in India, I made the bold and exciting move of immigrating to Canada. This transition was not entirely seamless. Navigating the job market was challenging for a new entrant such as myself – the cultural nuances through the interview process in itself was a factor. But in November 2019, I found Blanc Labs and joined the team. I was drawn to the sector and the tech mindset, which was very forward thinking. I had never been in such an agile company going through a rapid scale up before. The entrepreneurial culture imparted a growth mindset in me, and I was excited to explore that, and the ability to create more impact on the overall performance of the company.

Exciting new challenges

Taking a deeper dive into my responsibilities as QA Specialist, I had to check the application (built by the developers) against the client’s requirements (provided to me by the business analyst), ensuring there are no gaps/issues with functionality built. Though this seems straight forward, at the time, the developer to QA ratio was 10:1 (a more normalized number being 3:1). In addition, I had to learn the commercial lending process and delivery quality very quickly.

Always the eager student, I took it upon myself to learn as much as I could about the requirements, the stories, the questions, and the testing at very sprint. I am proud to say we saw no delays on any projects or deliveries.

The other challenge that I faced was my perception of the client. I’d always been taught to see the client as an entity that is superior to the company I represented. Their word was law and their needs and wants superseded my opinions. At Blanc Labs, we are completely collaborative with our clients in every project. My manager and mentor taught me that hierarchy should never get in the way of project delivery and success. Our clients are open and receptive to our insights and with that, I have been able to partner with them more effectively.

What does it take to succeed?

Today, I am proud to share I have grown to lead a team of QA Specialists. When I think about what it takes to be effective at what I do, it boils down to these things:

  • Become an eternal student: You have to understand the goal and recognize the outcome. You have to know the application in and which helps the process easier.
  • Stay focused: The quality of an application depends on you and so you need to be attentive to find every issue/ gap and ensure that the product is functioning and delivering in the way the client requires.
  • Agile delivery: With agile, QA is brought in early in the process to uncover any gaps, adding more focus to more effective delivery.
  • Demonstrate leadership: Irrespective of whether I was an individual contributor, or now, where I lead a team of QA specialists, I always take a leadership approach by establishing a foundation of standard practices across all projects, and encouraging those around me to learn and adapt to new tools, for example, to focus more on automation testing, which helps to improve test coverage.

 

I feel at home at Blanc Labs. Here, I am encouraged to learn, grow, and pursue new levels of excellence every day. Leadership has been instrumental as they have nurtured me to rise to new heights and for that, I am grateful. When I think about the success in my career, I go back to the three values that I admired in my father: dedication, responsibility, and focus. I realize that life has come full circle and I carry on the legacy of his work in everything I do and every frontier I choose to explore.

To learn more about the values with which we are growing our team, please explore our mission and vision.

Classification: The First Step in The Art of Speed Reading

Technology | AI | Enterprise Automation | IDP | ML

Classification: The First Step in The Art of Speed Reading

March 15, 2022
Classification

The world is building a solid foundation of machine learning (ML) into various sectors, functions, and tasks in our everyday lives. There is a myriad of components that one may delve into when exploring ML but an area that we engage in extensively and is getting increasingly interesting, is deep learning applied to document and data capture or Intelligent Document Processing (IDP).

The ABCs of IDP

Let’s start with the basics: According to Bernard Marr at Forbes, “Deep learning is a subset of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data.” IDP involves the capturing, extracting and processing of data from a variety of document types.

Now what’s so “exciting” about document and data capture? There are two types of documents from which you’d capture data: Structured and Unstructured documents.

  • Structured: These are standard forms (ex: government forms) that have specific and unaltered fields and patterns of information.
  • Unstructured: These are documents that don’t have any universal pattern. For this article, letters of employment would be good examples. Companies draft them in different formats and with different lengths (and cadence).

And with that established, lets delve into the two stages of how data is captured:

  • Classification: Examining and defining a document
  • Extraction: Capturing and recording key data from a document that has already been classified

There are two ways in which documents may be categorized: image processing and Natural Language Processing (NLP).

The Art of Image Processing

Here is an instance where machine learning models are trained to look at a document and understand the pattern of what it sees. It isn’t ‘reading’ the document but uses Convolutional Neural Networks (CNNs) to identify patterns within an image, which in turn informs its classification. We found this works well for more structured documents where patterns are identical while the content may be varied. However, where we came into a few issues was the ability to scale the process.

Why?

When you train a machine to resolve categorisation, through the image approach, you are identifying objects specifically. However, forms don’t possess visual features for image processing. Every time we had to retrain a new document type, it would need to be trained from scratch with new data. This took days and weeks. In addition, we started to work with unstructured documents and found that we needed a new solution to effectively categorize them faster.

Enter: Natural Language Processing

With NLP, Optical Character Recognition (OCR) and LTSM (long term short memory) networks are used to extract words, run analysis (determining the context of the words), and then classify the documents. Unlike image processing, NLP, along with transformer models considers the relationship between all the words in a sentence and determines the weight of each word to interpret its meaning. This reduces the training time for every new document introduced, making the entire process faster.

Where new document types may take a week to train the machine to classify using image processing, NLP does it in mere seconds.

Setting up for success

There are two things to consider when one starts a project with NLP to make the process easier:

  • Determine goals: What information are you capturing and why. And what type of documents will be required to process?
  • Organize data collection: Maintain the quality of data by building workflows or processes that support consistent quality of information and documents collected.

The Journey Ahead

When you look at algorithms, sentiment analysis and the capacity of machine learning to evolve its capabilities, the term ‘speed reading’ really is taken to a whole new level. Our story doesn’t end here. It is just the beginning; It’s an evolving journey of discoveries and we look forward to exploring other dimensions of our realizations, including our next chapter exploring extraction, with you very soon!

Explore Kapti, our intelligent document processing software to find out how the power of machine learning and automated document workflows can transform your organization’s loan processing experience.

The Human Effects of Hyperautomation: Redefining processes and business models

Technology | AI | Digital Transformation | Enterprise Automation | IDP

The Human Effects of Hyperautomation: Redefining processes and business models

January 31, 2022
Hyperautomation

Now more than ever the phrase “amid chaos lies opportunity” should be resonating with business leaders.  This is not a time to hesitate but rather a time to adapt and move swiftly. The world has changed and those who can adjust quickly will come out on the other side stronger and with a greater market share. Herein lies tremendous opportunities.

According to the 2021 Gartner Board of Directors Survey, “69% of boards accelerated their digital business initiatives in the wake of COVID-19 disruption. Almost half anticipate changing their organizations’ business model as a result of the pandemic.” We live and breathe in a digital world. To stay ahead of the competition, serve our customers better and in the way they want, to grow market share while moving faster and more efficiently, companies must look to technology for assistance.

Robotic Process Automation is dead, long live Hyperautomation

I say this with my tongue firmly planted in my cheek.  Robotic Process Automation (RPA) is the automating of simple, rule-based tasks, and is an excellent starting point in digital transformation. However, as many companies start down the path of transformation, they soon find that they need more than just the automating of tasks; they need to be able to make sense of all the data that they have accumulated and have stored in dispirited backends and repositories.

“Hyperautomation is a revolutionary approach to maximize the benefit of digital transformation to enterprises.” Hyperautomation is the application of advanced technologies such as RPA, Artificial Intelligence, (AI) Machine Learning (ML), and Process Mining to augment and automate processes, enhance the use of analytics in ways that are more impactful than traditional automation.”

Keep in mind that Hyperautomation is not just the bringing together of several technologies; it is the reimagining and redesigning of the entire business model. It is a continuous effort redefining the value you are bringing to your end user and redefining the business models that support those initiatives. In addition, according to Forbes, “Every day we create roughly 2.5 quintillion bytes of data, and that number is growing at an exponential pace.” By applying technology to help you make sense of all your accumulated data you can make impactful and insightful business decisions.

Stakeholders in the Hyperautomation Journey

Reimagine engagement with both your internal and external customers. Your internal customer is the talent in your teams that help you engage with your external customers either directly or indirectly. They are the people you want to help by freeing up their time from mundane and repetitive tasks so that they can apply their creativity and insight to more high-value work. This is the core premise of the ‘digital workforce’. The common definition of a digital workforce is digital tools and technologies that enable your teams to work smarter. They are not encumbered by an office or legacy systems to accomplish their tasks and goals. Providing talent with a digital workforce that can work alongside them doing the tasks of copying and pasting data from one repository to another, as an example, will free them up and improve their productivity and morale.

Managing business processes old school—manually and email-based—can make tracing key information difficult. Executed well, the deployment of a digital workforce should result in humans refocusing their available time to pursue innovative growth strategies and finding new ways to improve the external customer experience.

As for the external customer, the journey has and continues to change at a dramatic pace. If we look at our world today, smartphones grant us access to information, forge connections and transact at the palm of our hands, live streaming has opened up our world of consuming content, crowdsourced GPS provides the fastest way for us to reach our destinations, ride sharing has transformed transportation (as will large scale adoption of self-driving technology), short term rentals have disrupted the way we plan travel – and the list goes on. The commonalities in all these examples are the instant gratification and personalization that they provide. Thanks to digital-native companies, customer expectations have changed too. Customers expect to engage with you seamlessly, with speed and simplicity.  They demand a highly personalized experience and interaction with proactive recommendations and offers. If your legacy processes cannot provide that experience promptly, you run the risk of losing that customer to a savvier competitor.

Hyperautomation can enable that level of personalization by predicting customer needs and accommodate the instant gratification of information and service through prescriptive engagement – all powered by artificial intelligence.

Where to next?

It isn’t enough to simply automate existing processes. Look at that big picture and long-term view and think about how automation (today) and hyperautomation (soon after) is going to transform your business. Here are a few questions to ask yourself:

  • What am I trying to automate?
  • What types of data do I need to use today and what would I need for decision making in the future?
  • Can I reimagine my business model with hyperautomation in mind? What is that vision?

 

Once you’ve asked yourself these questions and are on your way to creating that redefined business model, begin layering automation onto it and then introduce the concept of hyperautomation to the plan. With this long-term view, you will create additional value in your customer base and with your teams. This isn’t just about updating a legacy system; it’s about redefining and evolving your business to stay ahead of the curve.

Hyperautomation in the path ahead

Each year brings with it new market opportunities and scope for growth. The goal of every forward-thinking leader will always be to find ways to empower their talent and enhance the customer journey of their products and services with the mission of sustainable growth. Adding Hyperautomation with ML and AI to the right processes connects disparate data sets, eliminates inefficiencies, lowers lead times, reduces process times, provides greater customer insights and experiences, and results in more empowered and productive team for that brighter future.

We think it’s time to get rid of the swivel chair. Let’s talk about how we can help you on your automation journey. Explore our experience with enterprise automation and let’s accelerate your organization on its digital path.

Decanting Digital Transformation with Equitable Bank