Merger adds 550 experienced software engineers, complementary telecom industry expertise, Americas nearshoring

EDISON, NEW JERSEY AND MIAMI, FLORIDA–Orion Innovation (“Orion”), a leading digital transformation and product development services firm, and Sryas, Inc. (“Sryas”), a global technology company, today announced they have entered into a strategic combination. Sryas specializes in providing digital transformation services and solutions to companies in the telecommunications industry. The combination deepens Orion’s telecommunications industry offerings and expands its global footprint with significant nearshore resources in Canada.

Sryas’ capabilities in data management, analytics, and business support services complement Orion’s digital products and solutions for telecommunications operators. Sryas brings new intellectual property to Orion with Analance by Sryas, an all-in-one, modular plug-and-play data analytics platform. The company’s clients include one of Canada’s largest telecommunication companies.

Sryas adds more than 550 associates to Orion, including engineers in Canada, Mexico, India, and the Philippines, resulting in a combined team of approximately 7,000 associates and 12 major global delivery centers. As part of the transaction, Vish Ramesh, President and CEO of Sryas, will join Orion’s management team.

“Our combination with Sryas strengthens Orion’s nearshore capabilities and adds significant nearshore resources in Canada,” said Patil. “Sryas’ telecom expertise, strong set of complementary solutions, and proven track record of growth with one of Canada’s top communications companies is a perfect fit for Orion’s leading telecom practice. Adding Sryas will enable us to deliver even greater value to our combined clients with a comprehensive set of transformative solutions. This merger is another step in building a geographically balanced footprint across the Americas, Europe, and APAC. We are excited to welcome Vish and the entire Sryas team to Orion.”

“We are thrilled to join Orion and become part of the company’s global digital transformation platform,” said Ramesh. “Joining forces strengthens our collective nearshore capabilities and significantly expands our combined solution set to serve clients in strategic industries and geographies. It also gives our team new opportunities to work with a broad set of global enterprises, and become part of Orion’s unique global-local culture focused on growth opportunities for our talent.”

Orion is a portfolio company of One Equity Partners (OEP), a middle market private equity firm focused on building market-leading companies by identifying and executing transformative business combinations.

“Adding Sryas to Orion’s industry-leading digital transformation platform continues to scale its global operations and brings new opportunities to drive long-term growth in the Telecommunications & Media vertical,” said Chip Schorr, Senior Managing Director, One Equity Partners. “This is a powerful combination that adds value for both companies’ clients and strengthens their collective capabilities in high-growth sectors and geographies.”

o3 Capital acted as the exclusive financial advisor to Sryas for the transaction.

About Orion Innovation

Orion Innovation (“Orion”) is a leading digital transformation and product development services firm. Rooted in engineering and design thinking, along with a unique combination of agility, scale and maturity, its team of approximately 7,000 associates helps Fortune 1000 companies improve efficiencies, enhance customer experiences, and develop new digital offerings. Through its delivery centers in North America, Europe, India and Latin America, Orion serves clients across Hi-Tech, Telecom & Media, Sports & Entertainment, Professional Services, Financial Services, and Healthcare industries. For more information, visit www.orioninc.com.

About Syras

Sryas is a global technology company specialized in data, advanced analytics, software development, application modernization, and systems integration.

Empowered by the collective expertise of global talent, and a network of Delivery Centers across North America, LATAM, and APAC, Sryas delivers powerful insights and business transformations at scale. Our diverse teams hyper-collaborate with ambitious Telecom and High-Tech organizations to help accelerate their data, product, and technology roadmaps. All backed by a proven 20-year track record of delivering on-time, on-budget, and consistently exceeding quality expectations.

In addition to original tech and data solutions, Analance by Sryas is an all-in-one, modular plug-and-play data platform that enables organizations to visualize and tap deeper into their most powerful insights, all while capturing smarter opportunities with unprecedented speed. Bring on change and fearless innovation. Learn more at www.sryas.com.

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Data analytics is a complex process that demands time and effort from data scientists. From cleaning and prepping data to performing data analysis, data scientists go through an extensive procedure to uncover hidden patterns, identify trends, and find correlations in data to make informed business decisions.

The task of integrating, cleaning, and organizing data assets often take up the bulk of the data scientist’s time. After all, in order to extract quality insights and engage in effective decision-making, you need clean, quality data. And not only that, you also need a unified view of all the different data source systems across your organization.

This requires an effective master data management strategy, which when done manually, can be equally as time-consuming. To streamline integral parts of this process, organizations are adopting automated strategies such as machine learning. This way, you can fast-track and automate data matching, data cleansing, and data preparation workflows; boost productivity; and continue to achieve objectives at a timely pace.

Why do organizations need master data?

Before we talk about the role machine learning plays, let’s cover the aim of master data management first. Consider a customer data platform (CDP). We created a CDP with the goal of unifying customer data from several source systems, but we ran into the following business challenges:

1. Customer information is fragmented, duplicated, and inconsistent across multiple systems.
2. There is no trusted view of a single customer profile for customers across various segments.
3. Information is, for the most part, product-based which makes it difficult to recognize customers in their entirety.
4. There is a limited ability to up-sell, cross sell, enhance systems, and improve processes due to the current IT system’s complexity.
5. It is challenging to deliver a bundled products and services strategy, which relies heavily on a customer-centric view.

To create a 360-degree view of the customer, we needed a comprehensive master data management strategy to create a trusted record, golden record, or MDM record. And not only that, we also needed data governance. This is a process foundation to understand the nature of data, understand patterns, and understand business processes, which can in turn help the data team build algorithms to form consistent, unduplicated data for enterprises.

We had the data and the resources but only limited time. This is challenging especially if you’re working with massive amounts of data. In our case, we were dealing with almost 20 million accounts, more than 200K business customers, and nearly 20 different source systems to bring the data to MDM.

Data governance helped us put rules around data standardization and helped us identify critical data attributes, which can be used for matching. Typically, an enterprise uses name, email address, and other business attributes for customer data, but with data governance, we were able to add many other attributes for matching.

Automating MDM matching through Machine Learning

To understand MDM with machine learning, you need to understand the concept of data matching. It refers to the task of identifying, matching, and merging data records of the same entity from one or multiple data warehouses.

Basically, it is the ability to identify duplicates in large data sets. These duplicates could be people with multiple entries in one or many databases. With data matching, not only can you isolate these potential duplicates, you can also facilitate certain actions such as merging them into one single entry. You can also identify non-duplicates, which are equally important, because you would want to know that two similar things are not the same.

The traditional data matching approaches like deterministic matching and probabilistic matching do yield results, however, they can be very manual and time-consuming. By inserting machine learning into the mix, data matching can happen in a more accurate and faster manner. Machine learning provides a greater control over data and an opportunity to study and understand it.

Some of the key components of this process are:

1. Standardization providers to standardize data. 
2. NLP usage to standardize data.
3. Classification Machine Learning Models for data matching.
4. Ensembled Machine Learning approach for better accuracy.
5. Apache Spark for power processing.
6. Elastic Search for first-level matching and data bucketing.
7. Java/Python/R/SSIS support for data management.

So, how exactly does this work? Well, an organization’s current data matching process might require a black box product and manual intervention by data stewards to work on data. Generally, MDM projects are heavy and require a lot of manpower and skillsets to achieve matching with the product. They also require a lot of manual effort to clean the suspected data.

To automate this, we built Classification Machine Learning Models. The Model was built with the below ideas and steps:

1. Data Sampling for Historical Data – Statistical Sampling with techniques was performed on the existing data. Stratified sampling technique was used.
2. Data Standardization – Many custom rules and NLP were used to perform the data standardization.
3. Data Preparation and Bucketing – The sample data was looked up into an Elastic Search instance, so as to prepare the first matching set. This process is called data bucketing. The sample data was termed “FROM” Data and the match results from ELK was termed “TO”.
4. Data Preparation by Python – The sample data with bucketed results was further enhanced, where the fuzzy scores were prepared between “FROM” and “TO” keywords. Different patterns were used for matching. For example, FROM as Name + Address versus TO as Name + Address. Different fuzzy scores would then become features or predictors for our Machine Learning
5. Training Data Set Preparation – The data was labelled by our testing team in order to prepare a historical data or training data set for model preparation.
6. Statistical Data Analysis – The data was tested statistically to arrive at the significant predictors/features.
7. Machine Learning Model – An ensemble Machine Learning Model was built using the Training Model.
8. Model Optimization and Error Reduction – The Model was optimized using several techniques to reduce the false positives, hence increasing the accuracy.

The new, incoming data was then fed to the Machine Learning Model, where the data was classified by the model into different classes (Y, N & S) and different probabilities. “Y” indicates a perfect match or in other words, a duplicate record. “N” indicates an improper match, which means the record should be discarded. And “S” means that the probability of a match is low and manual intervention is required.

The Model also provideda classification probability, which we referred to as scores. The scores vary depending on different models, built on differenttraining data sets. The current scores are probabilities ranging from 0.5 to 1. A sample test on sample data would look like below:

A more efficient way to create master data assets

Master data management can help organizations deal with fragmented and inconsistent data, facilitating deduplication to create a trusted and unified view of customers, products, services, and so on.

By incorporating machine learning into the mix, you can make the process more streamlined and efficient, freeing up your data scientist’s and other employees’ time to focus on customer-focused strategies and more proactive approaches. The results from machine learning can then be incorporated into the mix of MDM implementation styles, where our different MDM projects are on Registry and Transactional styles.

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Unless you’ve been living on Mars for the past few years, I’m sure you’ve heard the buzzword “microservices”, also known as microservices architecture.

A distinctive development approach, this natural evolution in software engineering came about due to the ever-increasing complexities of enterprise applications. Traditional applications are usually monolithic in design, which makes them bulky and very difficult to adapt to the changing needs of the business.

Microservices architecture offers to address these problems by splitting up complex software into smaller independent building blocks or services. These modular services can be deployed and managed individually, making large applications more malleable to new requirements.

Today, microservices architecture has been embraced by the industry as an efficient solution to application development and maintenance.

Microservices Architecture, explained

Microservices is an architectural style that logically structures an application as a collection of services. The software is broken down into multiple components that are built as independently replaceable, upgradeable, highly maintainable, and testable services. They are also loosely coupled and can be independently deployed. Microservices are often organized around business capabilities.

Because microservices architecture is structured as a set of loosely coupled, collaborating services, this approach allows for designing highly flexible, scalable, and available enterprise systems and applications. It facilitates rapid application development and delivery, plus enables a scalable and evolving technology stack.

In terms of microservice architecture design, there are different approaches: request-driven microservices, in which the different services communicate through a synchronous request-response pattern; event-driven microservices which facilitates independent and asynchronous computing; and hybrid architecture, which employs a mixture of both approaches.

9 benefits of microservice-oriented applications

From decentralized governance to resiliency, there are many advantages of adopting the Microservices Architecture style in software and application development.

1. Application is more accessible

As mentioned above, the microservices architecture is modular in design. This helps the users of the API better understand the type of services offered.

2. It’s easier to find bugs

When implemented correctly, microservices makes finding and isolating bugs much easier. Whenever an errors occurs, developers will know exactly where to look, which saves valuable time and effort. What’s more, the modular approach allows for failure isolation—other independent components of the application won’t be affected, and there would be no need to deploy the entire application after fixes.

3. Application is easier to maintain

The microservices architecture design allows for easier maintenance and management of the individual components. Since each service has its own code and database, it can be managed independently by a different team. Redeployment can be performed without affecting the whole system.

4. Each service is autonomous

Microservices are loosely coupled and have a standardized way of communicating with each other. Because of the modular design, the different services often use different programming languages or technology. This means that each service can be uniquely designed based on the required functions.

5. Application can easily evolve and adapt

Business requirements for applications are ever changing and evolving. There will always be a need to add new features or incorporate new technology. In the traditional architecture approach, updates often mean changing the entire business logic and potentially introducing errors. Microservices architecture, with its independent component, doesn’t have this problem.

6. Application can easily scale

This is one of the main benefits of microservices: its ability to scale horizontally. This means that any deployed service can be duplicated in order to avoid slow execution bottlenecks. Another option to increase performance is to run the service on more powerful hardware or multiple machines that can process data in parallel.

7. Testing and security monitoring are more manageable

Single software components are much easier to test than a complex application. The application is likely to be more robust if each individual component is tested properly. What’s more, the ability to isolate each service facilitates better security management and monitoring—it’s easier to isolate threats with the modular arrangement.

8. Operational complexity is reduced

Microservices have become much easier to develop, deploy, and test over the years due to various factors: technological developments such as cloud computing, drastic improvements in infrastructure automation techniques, and the principle of continuous delivery through DevOps.

9. Components can be reused

An often forgotten but great benefit of microservices is the fact that the independent components that make up the entirety of the software can be reused for future projects. By reusing successful processes and only modifying them as needed, development teams wouldn’t need to reinvent the wheel every time—consuming fewer resources.

Final thoughts

Modern systems are moving towards microservices architecture for obvious reasons. The advantages of decoupling systems allow companies to be able to scale and grow with the ever-changing business landscape. Scalability is affordable with cloud technologies, and many companies need to take advantage of this in order to move forward—or otherwise be left behind.

Having said that, it’s important to remember that transitioning over to microservices must be well thought out, because success can only be achieved from a system that is so loosely coupled and where all the pieces work with the same heartbeat.

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Gone are the days when marketers can simply claim that they have the leading product or service in the industry and then just hope for the best.

Today, the effectiveness of marketing is maximized by focusing on the customer—planning around them, addressing their needs, and making them feel valued and heard. And incorporating data analytics in marketing is one way that businesses can discover insights into their preferences and trends to improve the customer experience.

In a world where there are a lot of best t-shirt brands, best sandwiches, and best accounting software, customers need to know that what they’re buying or signing up for is more than just the best—the product or service must also provide the solution that they’re looking for in the most convenient way.

This is why businesses need to make the shift from being company-focused to becoming customer-focused. Joe Chernov puts it simply: “Good marketing makes the company look smart. Great marketing makes the customer feel smart”.

Why is Data Analytics important in marketing?

In order to address your customers’ needs, you must first understand who they are, what they’re interested in, and what relevant problems they’re facing. But how does one get a hold of this information?

This is where data and analytics come in.

Marketers often deal with a large number of data on a regular basis: customer profiles, sales history, interactions, industry trends, and more.

This is a goldmine of information that when uncovered, can lead to a wealth of insight and patterns for better decision making. Predicting customer behavior can help improve processes and strategies, examining web analytics in marketing can help inform effective design or revise a strategy, and understanding channel effectiveness can help identify the most optimal media placements for customers.

To make sense of all the data available, you should turn to AI-powered intelligence: descriptive analytics to answer the question “What has happened?”, diagnostic analytics to answer “Why did it happen”, predictive analytics to answer “What will happen?”, and prescriptive analytics to answer “What should we do?”.

By getting answers to all these questions, businesses can get a deeper insight into every component of the marketing funnel. Armed with this knowledge, you can confidently make strategic, data-driven decisions that can lead to business growth and customer delight.

How can Analytics be used in marketing?

Advanced marketing analytics can help professionals cater to and anticipate customer needs, send personalized marketing messages, and provide a delightful customer experience.

Let’s dive deeper into how marketers can make the most out of analytics in offline and digital marketing. These are just some of the many possibilities:

1. Profiling leads and customers

While marketing teams often reach out to specific markets with specific characteristics, it still helps to drill down into what exactly makes your prospects and customers distinct. Use clustering methods to create customer segments based on certain dimensions such as their interest, preference, or activity.

With clearly defined segments, you can understand your audience better and draw helpful conclusions that will further guide marketing strategies and initiatives.

Segmentation can also help you create highly targeted messages and develop campaigns that are tailored to customers’ needs. For example, you can perform a market basket analysis to provide personalized product recommendations in real time. This way, you can provide the right information to the right person at the right time—and capture their interest.

2. Increasing engagement

Effective marketing involves not only capturing the audience interest, but also sustaining it. As a marketer, you must keep prospects and customers engaged enough to take the desired course of action, whether that’s adding a product to their shopping cart, booking a free consultation, or even downloading a free e-book.

Aside from identifying your customer’s needs, leverage predictive analytics in marketing campaigns to anticipate their needs as well. By proactively designing campaigns or experiences around these predicted needs, you can expect a high level of engagement.

For example, when you notice that a substantial number of customers have stopped using your product or service, you can comb through historical data and utilize predictive models such as regression analysis to identify the factors that contribute the most to churn.

You’ll get visibility into your customers’ reason for leaving and have the capability to predict whether incoming prospects are likely to churn as well. All these insights will help you understand churn that’s specific to your business and what you can do to prevent it.

You can then add measures in place to reengage prospects where and when necessary, such as offering customized offers, relevant content, or relevant product recommendations.

3. Monitoring feedback

One of the best ways to understand and know your customers is to listen to them. Find out what they have to say about your product or service through a customer sentiment analysis.

Make sense of social media feedback, emails, customer service interactions, and any other messages from communication channels through text analytics, which leverages Natural Language Processing to convert text into levels of sentiments—from positive to negative.

With customer sentiments, you can determine what your audience feels or thinks about your brand on a regular basis, monitor feedback to new changes (like new features or location changes), and even identify potential brand advocates or influencers who can help with your marketing efforts.

Use all these insights to inform decision-making and make necessary changes in order to delight and maximize customer satisfaction.

How to get started

The marketing world isn’t new to data or analytics. In fact, research has shown that 70% of senior executives and professionals in the US consider sales and marketing data analytics as “very” or “extremely” important to their organization, with 52% heavily investing in the technology.

While businesses acknowledge the importance of analytics for marketing, two-thirds of executives say that they don’t have sufficiently trained staff to manage this capability. But imagine what they could achieve if they had access to a self-serve marketing analytics solution?

Enter Analance, an end-to-end enterprise analytics solution that lets you manage, visualize, and analyze data—all in a single platform. To learn how the platform can help you get insight for your toughest marketing questions, we are happy to conduct a demo for you.

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When people ask you what IoT means, what phone model to get, or what’s the best way to understand your customers, you tell them “google it.” But when they ask you how to do something—track your data usage, pay bills, stream your favorite movies—you say “you know, there’s an app for that.”

Because most likely, there is. There’s an app for almost everything these days—and the telecom industry is no exception. In fact, as of this year, there are 1.76 million apps available in the Apple App Store alone.

The app industry is poised for growth, and for organizations looking to get into that space, it helps to know exactly how the process works.

7 steps to building an effective telco application

How do you design, develop, and deploy an application for the telecom industry? Whatever development methodology is followed—waterfall, RAD, agile—there are a couple of steps involved in the application development process.

1. Planning

The first step, like for anything else, is to prepare a clear and actionable plan for the application. Start by thinking about what problem or pain point you’re trying to solve and consider whether it can be answered with an app. You have to be as specific as possible here. Basically, you should fill in the blanks: When an employee or customer needs to ____, my app will come in handy.

By identifying this need, you can also define what outcome is expected. What real-life application will users get by using the app? Is it the ability to track their delivery, generate a quote, or connect with a customer service representative? During this process, you should also be able to define who your target users are: young or old, male or female, local or worldwide.

This would also be a good time to conduct a competitive analysis. Are there competitors and if so, is there enough market space for your organization to tap into? If you determine that there’s enough demand, you can also take a look at the features of competing applications to get insight and inspiration.

And lastly, you have to ensure the profitability of the application you’re trying to build. Define the monetization strategy. Will you be requiring an upfront cost or in-app purchases? It’s worth noting that the subscription business model generates the most money, with 36% of developers attesting to the fact.

2. Analysis

Now that you’ve laid out your plan, it’s time to get into the specifics. In this stage of the process, your goal is to document all the user, software, and hardware requirements of the app. This way, you can determine what features to focus on and anticipate what problems might come up during development.

For example, will your application be available in several platforms—mobile phone, tablet, and on the web? Having multiple touchpoints means different requirements and optimization strategies. If you envision just a mobile app, you’ll also need to decide whether it should be built for Android or iOS or both.

As for functionalities, will it need internet connectivity? And will it benefit from integrating with the user’s camera, smart devices, or even customers records? These integrations will also need to be analyzed and laid out in detail.

And lastly, but certainly not the least, make sure that the application complies with government or app store regulations. If your app turns out to be noncompliant, it risks being removed, fined, or shut down altogether.

3. Design

Based on the analysis conducted, visualize how the app will work and how users will experience it. After all, it’s not enough to just build a great app—it must also provide a great user experience. A customer trying to book an appointment will use an app because it is convenient. If it has a poor user interface that obstructs them from doing a simple function, they’ll just pick up a phone to get the job done.

To do it right, start by finalizing the application features list. Remove unnecessary ones and focus only on the core value, or what the desired outcome is. This will enable you to create an MVP (minimum viable product) of your app that will shorten the time-to-market. Any nice-to-have features can come later as updates.

Next is the actual app design. It’s during this stage where a mock-up or prototype of the app is created. This usually includes high-level design details such as the flow control and structure and different navigation patterns, and more granular aspects such as buttons, font section, and other UI objects.

4. Construction

Now comes the actual building. This is the stage where all the previous planning, analyzing, and designing efforts are implemented. Using the requirements and design plan as a guideline, your in-house or outsourced development team will begin the actual programming and code the app.

5. Testing

When the app’s built out, you’ll need to make sure that it works as intended, that document requirements are met, and that it’s free of major bugs. As many know, there is not much room for error in the telecom space—much less its apps.

It is a mandatory that the app goes through a stringent security audit as well as system tests, integration tests, performance tests, user acceptance tests, quality tests, and debugging to make sure everything is in order. It is critical to ensure your app is secure to protect the app users and all their sensitive data.

You’ll also want to get as much feedback as possible during this stage, so consider running the app past target users by conducting a beta test. Use the positive and negative feedback gained to make iterations to the application. Repeat this process until you deem the application is ready for release.

6. Implementation

Congratulations! Your application is ready to go live. Make the app available to use in the platforms that you’ve identified in Step 1. It will likely go through a review process first, but this is also a good time to make some noise. Promote your new app internally, on social media, or your network.

7. Support

Building apps is not a one-time thing. You’ll most likely go through this process over and over again, especially if you’re following the Agile methodology.

So when the first version of the app is live, don’t just stop here. Invest in an application maintenance and support  process as well. Monitor feedback, make iterations, continue debugging, and offer support to your users. Remember those nice-to-have features you identified? This will be a good time to pursue those functionalities.

Time to develop that app

The app market will only continue to grow—and for good reason. Because it offers a much-needed convenience, apps are being used everywhere to do a myriad of tasks and activities. There are a lot of factors that go into how application development is done, but the process outlined above will give you a solid foundation.

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Service Level Agreements (SLAs) are commitments given to customers in relation to the product or service being provided. If breached, not only are organizations expected to compensate through penalties and credit fees, but they can also face a significant dip in brand reputation and loss of customer trust.

This is why preventing SLA breaches is a top priority for any customer-facing organization. To stay on top of breaches, agents traditionally check the ticket status of each incident manually. But this process is laborious, time-consuming, and risky—in fact, it’s easy to miss a potential breach. Read on to learn how you can optimize and automate this workflow using machine learning.

Leveraging ML to prevent SLA breaches

A fortune 500 telecom organization was experiencing a high number of SLA breaches and customer complaints. To help them reduce this number, we implemented a ML-powered alert system that flags tickets nearing an SLA breach using Analance.

Sourcing and preparing the data

We used the telco’s 2019 incident management data to identify variables or predictors that are significant in predicting SLA breaches. There was a total of 659,875 tickets with 78 attributes such as creation date, device type, description of the ticket incident, and day of the week. Only user-created incidents for specific services were considered for analysis.

To effectively categorize tickets, a text clustering analysis was conducted using the data from the descriptions provided by customers. Five ticket clusters were identified: SNMP tickets, test tickets, Wi-Fi tickets, DNS/VLAN tickets, and tickets on router and other connection issues. These clusters were then included as a predictor for the machine learning model.

Data then went through the usual cleaning methods, plus a Univariate and Bivariate Analysis (Chi-squared) so that analysis is restricted to only significant predictions.

Building the machine learning model

To train and score the SLA breach prediction model, we used classification algorithms like Random Forest, Naïve Bayes, K-Nearest Neighbor, Support Vector Machine, and Decision Tree. Training was done in an ensemble mode and went through different iterations until the output was satisfactory. If the data distributions deviated significantly from the original data set, the model would be retrained.

We utilized libraries in Python—sklearn, nltk, pickle, and more—to implement the algorithms, and we leveraged natural language processing to save the trained models. To help improve model performance, categorical columns with textual data was encoded using the LabelEncoder function. We had to make sure that the different levels in each categorical variable were consistent for both the training and scoring stage.

To illustrate, we had a column pertaining to the day of the week in which the ticket was created. During training, this column contains only Monday, Tuesday, and Wednesday as levels. Now if the scoring data contains levels other than those 3 days, we would get an error in the code. This was fixed by storing the trained models in a pickle file and using this to score the new data.

Model results

Analance has 41 prebuilt machine learning algorithms, with 8 categorized as classification algorithms. We used an ensemble approach to train the model and analyzed the results to identify the best performing one.

To visualize the performance of the model, we used the ROC plot. The area under the curve value is 0.69, which means that there is a high chance the model is able to distinguish between breached and non-breached tickets.

Sourcing new data

The model built was used to score new data in 2020 with favorable results, as seen in the confusion matrix below.

In the table, the rows represent the actual values and the columns represent the predicted values. For example, for the first value, 73.97% represents the percentage of ‘0’ (non-breached) tickets that have been classified as ‘0’ (non-breached).

This means that 73.9% of non-breached tickets have been classified correctly. Similarly, 72.6% of breached tickets have been classified correctly. As for misclassification, the rate is only about 26% for breached and non-breached and 27% for non-breached as breached.

Streamlined workflows, happier customers

The prediction system can be used as an alerting system—tickets with a high likelihood of being breached would be tagged in an interactive dashboard that is updated in near real time. Agents or technicians can simply consult this report to prioritize high-risk tickets.

Furthermore, the NLP-powered clustering system that groups similar tickets can be used to optimize the incident resolution process. When working on new tickets, agents can use insights from historically similar incidents and replicate the resolution method where applicable. There would be no need to refer to the knowledge base every time to solve a ticket.

Lastly, the predictive engine can help in resource allocation as well. The machine learning model can determine specific time periods when a large number of incidents is expected. By anticipating this spike of potential SLA breaches, managers can plan accordingly and make sure that qualified agents are available whenever there’s a higher risk. Managers can also consider the foresight into potential number of false positives to keep expectations in check.

With foresight into potential breaches, the telco organization was able to facilitate timely resolution of tickets, prevent breaches from happening, and reduce the Mean Time to Repair (MTTR)—boosting customer satisfaction and trust in the process.

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Data is an essential asset for any business, with comprehensive efforts made to generate, source, and prepare it for analytical use. But just as important as collection and cleaning is ensuring its accessibility for users across the organization.

This highlights the need for an organized data inventory—a directory that makes it possible to easily sort, search, and find the data assets required. In other words, you need a data catalog, a core component of master and meta data management.

Let’s talk about why such a system is important, plus how machine learning can help your organization fully optimize the processes involved.

Data cataloging, explained

As enterprises scale, there will come a point when they will have acquired a massive amount of data from various sources. Usually this stack of databases is siloed, with different kinds of sizes and technologies—from RDBMS to MongoDB—that have evolved over time.

To eliminate silos and create a unified view of products, customers, and operations, enterprise data is usually housed in a shared data resource such as a unified data warehouse or master data repository. But even with a one-stop shop like this, organizations find that they still have challenges in accessing the data assets they need.

Without enough visibility into the content and context of existing databases, too much time will be spent on finding and understanding data. And the data management process is already lengthy as it is. This is why a data catalog, a powerful tool that lets you “order” the data you need from the relevant databases, is essential.

Essentially, a data catalog involves the following aspects of data management:

1. Data acquisition – the process of bringing data into a database using ETL or streams
2. Data searchability – the process of making data accessible so users can easily search for the data they need.
3. Data visibility – the process of providing a relevant view of enterprise data assets, such as a 360-degree view of the customer or product.
4. Data dictionary – a collection of the different features and attributes of data assets, also called a metadata repository.

For example, a customer in your organization might have different data elements and accounts across departments. A master data management solution groups this customer as one asset and will then feed this into a data catalog. With this set up, a customer service representative can just input any of the customer’s information into the catalog (a unique ID, an email address, etc.) and get access to the single source of truth.

However, this enterprise-wide view can be overwhelming. Different users have different data needs, after all. So, a data catalog will also enable the quick access of relevant and meaningful information that’s fit for the purpose of the search. In the case above, the representative will be able to understand who owns the different data elements and whether it’s relevant to them.

Data catalogs will also come in handy when organizations decide to implement cloud infrastructure. With the recent focus on global collaboration and remote workforces, the fast migration of assets is critical. A data catalog will help accelerate this process by ensuring data readiness capabilities in a modern cloud environment.

What does ML bring to the data cataloging process?

Data catalogs enable efficiency and productivity, so it will be counterintuitive if the actual process is done manually. Fortunately, the use of artificial intelligence technologies has become widespread to automate functions that were previously manually handled. Below are a few ways ML is utilized to create a better data catalog.

1. Auto cataloging capabilities

Machine Learning can be used to automate various aspects of the data cataloging process. For example, you can build an algorithm to group a customer’s accounts and their identifiers automatically for a golden record. This enables efficient deduplication, schema detection, tagging, and even outlier detection.

2. A more powerful way to search

Organizations can also leverage Natural Language Processing to enhance searching capabilities in a data catalog. This way, you can extract meta information from various unstructured datasets such as images, videos, and audio. NLP can also help when dealing with corrupted or dirty data.

3. Intelligent recommendations

Much like the product recommendations you see on retail websites, data catalogs can also provide users with ML-powered recommendations about other data elements and datasets that might be relevant to the search criteria. This is particularly useful for sales specialist when they’re trying to upsell or cross-sell products to customers, or even to technical experts when they’re dealing with constantly evolving products.

4. A strong foundation for data governance

The digital economy has numerous rules and regulations surrounding data, and it can be challenging to comply to all of them. Data catalogs can be used to fully understand each data element, what it’s being use for, and what kind of protection it needs. To take this a step further, machine learning can address consistency issues in data definitions and quality.

5. Ready for analytics

Often, machine learning specialists take too much time deciding which data to use for a data modeling project. This is usually because of a siloed approach in creating ML/AI assets such as data sets, feature sets, and models. A ML-powered data catalog can improve traceability between data, experiments, pipelines, and code.

Simplify and streamline data discovery

By incorporating machine learning capabilities, your data catalog can become even more powerful and scalable. It can help organize your enterprise’s various business assets efficiently, implement effective meta data management, and empower decision-making. With this tool, users across the data pipeline can easily search, evaluate, and apply the data they need for analysis and other uses.

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In today’s technological landscape, businesses are more data-dependent, with vast pools of data at their disposal. What to do with it and how to make sense of it all is a challenge we attempt to solve with data science—starting with data discovery and ending with analytics that aid in critical business decision making.

Now, there are numerous ways to extract meaning from data. Buzzwords like business intelligence and advanced analytics—though quite crucial for every enterprise—can easily be misunderstood in their role in making sense of data.

For example, what exactly is advanced analytics? Is it the same thing as machine learning? And where does artificial intelligence come into play? In this article, we’ll answer those questions and more by discussing the difference between business intelligence vs advanced analytics—two strategies that are similar in nature, but with some key differences.

A showdown of analytical strategies

When comparing advanced analytics to business intelligence, one thing is clear: both approaches provide answers. The question is, how different are those answers? Let’s look at those differences in detail in the infographic below:

Purpose

Business intelligence is an information provider. It offers hindsight, giving users information about past metrics and visibility into performance. To illustrate: companies can use BI to track real-time performance metrics much faster than they otherwise could, improving operational efficiency in the process.

Advanced analytics, on the other hand, offers foresight. It’s a troubleshooter, anticipating future business challenges and behavior to prescribe actions to maximize beneficial outcomes. For example, when a manufacturing professional forecasts an unplanned downtime for machinery, that’s advanced analytics. In this case, they are provided with a clear prescription on the next steps of the decision-making process.

What’s the common thread? Both attempt to uncover information that would support critical business decision making.

Questions answered

BI answers the question “what happened?”, historically and operationally. It also provides answers to “why did it happen?”, allowing users to dive deep into data and get visibility into what might be causing the problem.

Users can also leverage advanced analytics to find answers to questions like “what will happen?” and “what can we do to make it happen?”. It offers a more comprehensive “why” and “how” to the “what” of BI.

What’s the common thread? When comparing advanced analytics vs. business intelligence, it can’t be denied that both approaches still provide analysis that can help drive business outcomes.

“Together, we can fuel an industry culture of joint innovation in which no risk is missed, and no opportunity is left behind.”

Methods used

Business intelligence utilizes the more traditional reporting and visualization methods as a form of analysis: dashboards and scorecards, multi-dimensional analysis, automated monitoring and alerts, and more. These processes are employed to gather data from different sources and deliver it in a clean, easy-to-digest format for reporting or monitoring purposes.

For advanced analytics, more sophisticated quantitative methods are used to discover trends and find patterns. Think predictive analytics, data mining, simulations, and machine learning algorithms such as regression and decision trees. When people mention artificial intelligence in the realm of data science, they usually refer to advanced analytics.

What’s the common thread? Both techniques still belong to the overarching field of data science, with roots in mathematics and statistics.

Problem-solving approach

When it comes to business intelligence, users often come up with a solution in a reactive manner. Without visibility into what might occur, problems are usually dealt with as or after they arrive. Given the limited capabilities of BI, context is often lacking too.

Advanced analytics offers the functionality to anticipate future issues, which is why problem-solving is more proactive and pre-emptive. With the ability to uncover new correlations, patterns, and opportunities, solutions are often more dynamic and comprehensive too—eliminating guesswork in the process.

What’s the common thread? Both approaches still give weight to quality, each requiring sound solutions that can address a business challenge.

Data required

Business intelligence systems are usually designed to handle highly structured data and only some forms of unstructured data. But they can work with different data sources with no problem, through a traditional data warehouse.

On the other hand, advanced analytics systems can work with unstructured and free-form data: social media comments, images, videos, and more. These systems can also handle high-speed, high-volume, and complex multi-structured data from a variety of sources. In fact, most kinds of data can be collected, cleansed, and prepared for analysis.

What’s the common thread? Both techniques can work with real-time data sources. Also, both provide answers that are only as effective as the data being used. Erroneous data will still produce erroneous results. 

Output

After a business intelligence project is completed, the result is usually predefined and in highly formatted reports—dashboards, pivot tables, and the like. The goal is to analyze historical performance, so users usually go back to these reports to extract information.

As for an advanced analytics project, the output is usually a machine learning algorithm, built and trained to find hidden relationships between factors and their outcome and come up with forecasts.

What’s the common thread? For both approaches, reporting still plays a huge role. After a machine learning model is built, the data scientist still relies on reporting to visualize the output and ensure that models are performing as intended.

Both make a powerful team

In a nutshell, business intelligence tells a story about what the data is saying, and advanced analytics takes it a step further, uncovering patterns that will give insight into what might come next and what should be done about it.

So, on one hand, you’re asking, “who defaulted on their credit card and loans last quarter?” and on the other, the question is “who is likely to default on their credit card and loans in the near future?”

Still, both approaches are integral to data science. Both strategies take data and transform it into insights. Both are used to inform decision making for better business outcomes.

Instead of choosing the best approach, it’s time for companies to understand that both business intelligence and advanced analytics are critical to analytical projects. If you are considering investing in a data science platform, opt for one that offers both the capabilities to realize the full potential of these technologies. With Analance, you can visualize data in dashboards and drill through for further insights, but you can also brilliantly complement your efforts with advanced analytics and machine learning to solve greater challenges and grow to new heights.

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It’s been over three years since enterprises around the world had to pivot and transition to work-from-home setups. While some employees are still trickling back into the office, remote and hybrid work is not going anywhere.

This modern workforce has brought out an increasing reliance on cloud infrastructure, an essential tool for collaboration and business continuity. Technology like this isn’t without its risks though. In order to effectively mobilize a borderless workforce, it’s important to be aware of the cybersecurity risks reinvolved.

Cloud security is more essential than ever

Cloud services used to be a “good to have” competitive edge, but now it has become an integral business enabler. According to an IDG report for 2022 Cloud Computing research, 69% of organizations have accelerated their cloud migrations over the past 12 months and cloud budgets continue to increase – on average, organizations will spend $78 million on cloud computing over the next 12 months, which is up from $73M in 2020.

Aside from playing a huge role in disaster recovery and operational resiliency, cloud computing and infrastructure facilitates a free flow of information, making it easier for employees to access what they need and collaborate with others—even from their home office.

But this benefit is also its drawback. Information can easily be accessed by employees wherever they are, but the same is also true for cybercriminals looking to exploit vulnerabilities in the system.

With a broader attack surface on the cloud, there’s a pressing need for robust security controls and an increasing demand for technicians well-versed in the space. In fact, 19% of cybersecurity professionals in Canada consider cloud security an important technical skill or concept to learn. It can be the difference between mobilizing or jeopardizing the flexible workplace.

5 cybersecurity concerns in the remote work era—and how to address them

Security is one of the biggest barriers to taking full advantage of cloud resources. Without policies and controls to protect critical data, applications, and other assets in the cloud, organizations can face anything from financial loss to reputational damage.

According to a survey on IT and cybersecurity professionals, below are five significant risks posed by the remote work model.

1. Data leaking through remote endpoints

Organizations use the cloud for building data lakes and data-driven applications, housing massive amounts of customer, product, or organizational information.

With people working off premises, there may be a lot of remote endpoints being used to access these critical assets—from laptops to mobile phones. Any endpoint that’s unmanaged can be exploited to access sensitive data, install malware, or tamper core business applications.

To address authorization and authentication, implement Identity and Access Management  policies to control and monitor who has access to critical organizational assets. Any access to the cloud should also have some form of Multi-Factor Authentication (MFA).

2. Users connecting through unmanaged devices or networks

Mitigating risk can be challenging for organizations with a BYOD (bring your own device) policy. Employees are connecting from their personal devices or their home networks that may or may not be secure, especially if they’re using older OS versions or legacy anti-malware solutions.

It becomes easier to attack corporate systems when edge environments are attached to unsecure devices or networks. There’s also a risk of theft; employees may lose their devices or have it stolen, leaving it compromised to potential cyberattacks.

To ensure both cloud and network security, regulate personal device use, secure VPNs, and deploy antivirus solutions that have central monitoring capabilities for remote devices. Consider encryption as well; encrypt sensitive information that is stored on, sent to, or sent from remote devices.

3. Adhering to regulatory requirements

While it may vary by country, regulations are in place to govern how sensitive data is being collected, used, and disclosed.

The goal is to protect critical assets and IT infrastructure, so compliance frameworks and audits must be part of any cybersecurity strategy. For example, in Canada, it’s important to remain compliant with the Personal Information Protection and Electronic Documents Act (PIPEDA).

To ensure compliance, it’s important to provide vigilant IT support where need. It’s also something to address when migrating to the cloud—any compliance functions existing on premise must be replicated on the cloud environment as well.

4. Access to core business apps

Employees working remotely often need constant access to core business applications, such as email and collaboration tools. These modern software applications, while convenient, are also vulnerable to potential cyberattacks.

For instance, there’s been a prevalence of digital fraud related to Covid-19, with email phishing scams being the most common. A lot of third-party software also allows easy access from personal devices, even if employees aren’t authorized to do so.

To decrease the risk of cyberattacks, make sure to secure communication channels and collaboration tools that employees regularly use. Educate employees by launching an awareness campaign on phishing and malware risks through email as well.

5. Lack of user activity visibility

A good cybersecurity strategy involves constant threat detection and monitoring, especially in infrastructure and systems that affect critical enterprise data.

With a distributed workforce however, there can be a proliferation of unmanaged personal and mobile devices accessing corporate IT environment and assets.

This partial or nonexistent visibility on relevant employee activity makes it difficult to monitor, audit, and address security risks and misconfigurations. This is why it’s important to set up and communicate any remote work security policies. Employees should be aware of the risks involved and measures to take.

Empower employees with cloud security

Ensuring security in the cloud should be a priority for any organization with a remote workforce, and this starts with mitigating the risks above. Enterprises should aim to strike a balance between convenience and security—empowering employees and protecting critical assets at the same time.

Cloud security strategies should address this and more, ensuring data encryption, implementing access control layers, and enabling proactive threat detection all while providing an optimal environment for the modern workforce.

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Why is Predictive Analytics important?

Customers are always expecting more and more from businesses, including financial institutions. They expect their needs to be met, their complaints heard, and their experiences optimized for their benefit.

To keep up with customer demands at a pace that they expect, it has become essential to incorporate data science in banking for on-demand customer intelligence and insights. This allows for better decision-making and the capabilities to transform asset and facility management.

Banks may already have substantial insights from descriptive analytics and diagnostic analytics with beautiful visualizations, however it is now critical to go beyond and adopt advanced analytical methods like predictive analytics.

Predictive analytics is a subcategory of data science that uses historical data to create and train machine learning models for forecasting future outcomes to improve customer experience and operational efficiency. There are other statistical methods as well, like prescriptive analytics and cognitive analytics, but they will not be the focus of this article.

What are the Uses of Predictive Analytics in Banks?

1. Fraud management

Fraud spells bad news from both the company’s and customer’s perspective. On one hand, it can negatively impact a business and its profitable growth. On the other hand, it can put customers at risk and threaten their privacy. Either way, fraud is a risky occurrence that banks can’t afford to ignore.

Through machine learning, one can reliably identify suspicious patterns and detect fraud well in advance. An enterprise-grade platform like Analance can comb through both structured and unstructured historical data to determine the patterns of fraudulent transactions and raise alerts for potentially risky transactions.

This allows banks can launch necessary investigations and adopt other preventative measures. In the long run, this can save millions of dollars and help mitigate fraud.

2. Predicting credit card and loan default risk

In the same vein, predictive analytics can also help banks identify accounts that are likely to default on their credit card and other loans. Considered one of the biggest concerns in the industry, loan default can become a preventable issue through machine learning.

With predictive analytics, determine the patterns of high-risk borrowers by analyzing historical trends that led or did not lead to loan repayments. Several dimensions can be considered, including payment history or customer traits.

Not only will this type of insight help reduce the financial risks that often come with unpaid loans, it can also streamline the loan approval process, even automating it for a more efficient procedure.

According to a study by McKinsey, a bank optimized its corporate credit assessment using advanced analytics, consequently achieving an 80% higher productivity.

3. Transforming customer service

Customers are the lifeblood of most businesses, banks included. This is enough reason to invest in innovation and digital enhancements to continually improve customer experience, but it can also be considered as a way to reduce churn and differentiate your bank from other financial institutions.

After all, 72% of banking customers who had a negative customer service experience either engaged less or switched banks altogether, according to Cisco.

Incorporating data analytics in banking can greatly enhance your organization’s customer service. You can use machine learning to provide the right information at the right time, utilize chatbots to provide timely responses, and employ predictive modeling to provide personalized experiences. And of course, all of this would be easier with a 360 degree view of the customer.

4. Customer acquisition and retention 

Hand in hand with customer service is acquisition and retention. To maintain a customer base that helps your organization meet its business goals, it’s important to acquire quality customers and engage them enough to retain them.

The key here is personalization by segment, which is more than possible with advanced analytics. Predictive models can help you identify high-value leads and launch individualized communications and personalized campaigns to acquire these segments.

On the retention side, you can benefit from machine learning to come up with and promote the right loyalty programs to the right customers. Insights gained from predictive analytics can also inform product development, so the organization can focus on products that meet customer needs and at the same time, generate maximum revenue.

Incorporating Predictive Analytics

While the scenarios listed above are just some of the many examples of predictive analytics in banking, the advantages are crystal clear.

In fact, incorporating predictive analytics in just one business area can create ripple effects across the organization: improving data literacy, streamlining data collection processes, and adopting the mindset of making data-informed decisions.

The analytical approach is especially important in an industry like banking (and the financial sector in general), which is often characterized by information explosions and volatility.

Just imagine the massive amounts of data banks handle on a regular basis: customer accounts and preferences, credit scores, ATM and online transactions, customer feedback, interest rates, and various macroeconomic variables.

In order to effectively utilize and make sense of all these data sources, one would need some machine learning and predictive analytics magic. Basic reporting just won’t do the trick anymore. You have to go all-out with more advanced capabilities, like predictive analytics to start, in order to gain the confidence needed to support and make crucial business decisions.

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