Banking on the future: Financial services and the metaverse

Companies are increasingly investing in the metaverse, which Gartner describes as “a collective, virtual shared space, created by the convergence of virtually enhanced physical and digital reality.” Metaverse is where the real world and virtual world collide, a mashup of technologies like augmented reality (AR), virtual reality (VR), blockchain, artificial intelligence (AI), and machine learning (ML), creating a digital experience that mirrors real life. It’s essentially the next evolution of the internet, with avatars and 3D, connected experiences.

When it comes to financial services and the metaverse, the time is now to prepare for adoption in this industry.

Metaverse is gathering steam

The global health pandemic accelerated the adoption and advancement of technologies such as blockchain, cryptocurrencies and NFTs, and AR/VR, which are crucial metaverse building blocks.

Gaming, in particular, has served as a gateway to the metaverse, opening new doors and experiences and revealing exciting possibilities. For example, Epic Games found that Fortnite players often socialize with their friends inside the gaming platform even after a game ends, providing opportunities for virtual events, such as when Ariana Grande stepped into Fortnite to deliver a virtual concert in 2021.

Major brands are also betting big on the metaverse. Most notably, Facebook rebranded itself as Meta and unveiled plans to build a metaverse. Other brands like Samsung and Adidas are purchasing “real estate” in metaverse platforms such as Sandbox and Decentraland, paying up to $2.4 million for digital property and renting land for up to $60,000 a month.

These brands are building experiences to engage with the estimated 2.4 billion people who will visit the metaverse by 2026. The global metaverse market is expected to balloon to $947 billion by 2030.

Use cases for financial services and the metaverse

But what does the metaverse mean for the financial services industry? Below are some compelling use cases awaiting financial services companies in the metaverse.

Transactions

Enabling digital transactions is the most obvious use case. For example, American Express is already investing in the metaverse, with plans to allow customers to use AmEx payments in virtual worlds.

Furthermore, cryptocurrencies are on the rise, with an increasing number of countries recognizing crypto as a legal tender. Simple and secure crypto transactions will likely form the foundation of the metaverse economy. In fact, AmEx is also eyeing services like crypto management and trading.

Consumer banking experiences

While consumer banking used to be primarily in person, these days, many consumers never set foot in their bank. While convenient, these online experiences can be highly impersonal.

The metaverse provides an opportunity to humanize consumer banking, offering a virtual destination where consumers can interact and ask questions, whether they’re applying for loans or opening accounts. The metaverse also provides the opportunity to expand products and services. Already, some digital-only banks connect customers to third-party services and cross-industry marketplaces.

Robo-advisory investment banking

Robo-advisors have been around for a while. But in the metaverse, robo-advisory services will be up-leveled with more personalized, interactive experiences. Robo-advisors in the metaverse will be able to talk to consumers and answer questions, providing a more human-like experience.

Virtual meetings and onboarding

The metaverse promises new shared spaces and immersive experiences, changing how people interact. Virtual meetings and events will become more immersive, enabling people to move freely and interact in any language. The metaverse also impacts onboarding, providing more personalized, engaging learning experiences that improve knowledge retention.

Preparing for a metaverse future

The metaverse is a journey. While no one knows exactly what the destination looks like or when we’ll arrive, we know that the metaverse is our future.

The first step in preparing for the metaverse is digital transformation. Financial institutions must accelerate digital transformation initiatives and identify use cases across the different layers of the metaverse framework, including experience (dematerialization of physical objects), discovery (inquiring for information), the marketplace and meta-economy, spatial computing (bringing life to metaverse), and the human interface to the virtual world.

We already know many of the building blocks needed to realize this future, and it behooves financial institutions to build foundational competency in AI, ML, blockchain, cryptocurrencies and NFTs, and AR/VR technologies.

Financial institutions must also begin thinking about how to design personalized and relevant experiences and reimagine their products and services for a metaverse future.

Beyondsoft helps customers around the world accelerate digital transformation and prepare for success in the metaverse. We work with customers to perform initial discovery and design, identify required technologies, and build a technology roadmap. To learn more about how we can help your financial institution prepare for the metaverse, reach out to our experts.

How a data mesh architecture can help your organization

More than ever, accurate data is vital to business success, and organizations worldwide are racing toward budget-friendly, cloud-based analytics solutions that scale dynamically. Centralized analytics and data management solutions can assist an organization in many ways. For example, filtering and delivering the most applicable data to each internal department–like a company’s sales and finance teams–allows each group to operate more independently and effectively to support the larger organization.

When relevant data is easily accessible to the employees who need it, businesses realize enhanced productivity, streamlined processes, and innovations that provide additional value to customers. Also, by giving “ownership” of data to functional groups, companies can more easily adhere to and govern data privacy policies throughout their lines of business.

The convergence of all these factors drives the increasing importance of a data mesh architecture in today’s marketplace.

Data as the new currency

Before I talk more about data mesh, a short backstory is helpful. Many years ago, the value of data as the “new currency” became clear for organizations of all sizes. Today, most organizations make significant investments to capture as much information as possible and store it in data lakes.

However, storing enormous volumes of data without a way to extract relevant meaning isn’t helpful. Organizations must analyze and convert data into insights that drive better development and business practices.

The DevOps methodology chips away at many challenges by aligning operations and development teams to streamline coding processes. In data engineering parlance, DataOps takes everything a step further by ensuring data pipeline observability, data reconciliation, and streamlined workflows.

The role of data mesh

Large organizations have a highly complex data environment. They need more effective ways to combine available information to harvest its potential benefits. Data mesh builds upon the foundation provided by cloud native data technologies and the DataOps model to manage it.

In the simplest view, data mesh architecture extends data management practices. It eases the burden on data analysts by helping tease out the most relevant data for each functional group within a company. With data mesh, each domain’s front-end applications receive a uniquely appropriate feed from the data pipeline. In turn, employees in those departments gain the deeper insights they need to do their jobs more effectively.

Benefits of a data mesh architecture

Beyondsoft’s clients implementing a back-end data mesh architecture gain four key benefits:

Maximizing data as a product: Reliable data guides modern businesses, so using available information is vital for effective and streamlined work.
Domain-driven data ownership and architecture: Data needed by one corporate department filters into a different “bucket” than data required by other departments.
A self-serve data platform: With data mesh in place, team members can get the most from domain-focused, automated front-end apps.
Federated governance: Since each functional group “owns” its data, it can also manage the data and its associated usage rules.

Data mesh has use cases across verticals

In the finance vertical, data mesh architecture can help a bank’s loan department secure customer-confidential data required to process and deliver customer loans as rapidly as possible. Simultaneously, data mesh can support other employees in the bank’s business planning team who need more general information to monitor long-term income derived from interest on those loans and anticipate corporate profitability.

In the insurance vertical, a claims department needs specific information about customers’ insurance policies and the nature of their claims to process payments quickly. However, the insurance company’s actuarial group may require a completely different, anonymized data set to analyze various demographics and determine policy rates. A data mesh architecture supports individual groups’ needs.

Get started with help from Beyondsoft

As these examples illustrate, adopting a data mesh architecture can streamline operations, assist with federated governance, and empower various corporate departments with near real-time information specific to their role. In today’s fast-moving marketplace, functional differentiators like data mesh architecture can help organizations be more agile and advance their business at a brisker pace.

Reach out to our team at Beyondsoft to discuss how data mesh can benefit your organization and to learn how we’ve helped our other customers.

11 top-rated tech podcasts to geek out on for #NationalPodcastDay

As rapid digital transformation reinvents how business gets done, keeping up with the latest and greatest technologies can be challenging. Thankfully, you can learn on the go through the magic of the podcast. In celebration of National Podcast Day, we’ve curated a list of top-rated tech podcasts to add to your playlist.

Tech news and culture podcasts

TechMeme Ride Home: Got 20 minutes or less? Just want the daily essentials? Get a quick, no-fluff update on the tech industry’s current news and trends.

TechStuff: As indicated by the title, this show is all about technology: how it works, the people and companies behind it, and its impact on our world.

Your Undivided Attention: Brought to you by the Center for Humane Technology, this podcast explores the power of tech and how we can use it to drive a more humane future.

Cybersecurity podcasts

Security Now: In this weekly series hosted by Steve Gibson and Leo Laporte, delve into today’s hottest information security news and topics.

Defensive Security: Get insight into high-profile security and breaches, malware infections, and intrusions for learnings and takeaways to improve security.

Data and AI podcasts

The AI in Business Podcast: In this series, join top AI executives from companies like Microsoft, Rolls-Royce, and Stryker, who dive into emerging trends, industry use cases, and best practices.

The TWIML AI Podcast: Join host Sam Charrington and top minds from the world of AI, ML, and data science to unpack topics related to deep learning, natural language processing, robotics, and more.

Developer podcasts

DevOps Paradox: Are you a fan of DevOps? So are we! In this podcast, explore ways to modernize legacy applications, reduce complexity, manage technical debt, and implement CI/CD.

Developer Tea: Designed for the busy developer who wants to excel, these bite-sized episodes tackle topics like decision-making, productivity, feedback loops, and more.

IoT podcasts

Internet of Things Podcast – Stacey on IoT: Get the latest on everything IoT from Stacey Higginbotham and Kevin Tofel. This podcast covers a breadth of topics from smart home to IoT security to industrial use cases.

IoT for All: Hear from the industry’s top experts as they explore the applications, benefits, and inner workings of IoT solutions. Topics range from supply chain visibility to asset tracking to smart cities to 5G and LTE.

About Beyondsoft

As a global technology provider with more than 26,000 experts across five continents, Beyondsoft has become the partner of choice for Fortune 10-2000 companies for more than 27 years. We offer onshore, offshore, and nearshore delivery. Explore our service offerings or contact us today to learn more about how Beyondsoft can help you with your digital transformation needs. Plus, check out our insights page, with links to the latest blog posts and thought leadership to help you accelerate business.

Struggling to scale analytics and AI? Data quality may be the culprit

If your organization is struggling to scale analytics and AI, you’re not alone. We’ve come to the aid of many clients challenged with realizing their investments in advanced data analytics and AI. According to a 2020 Forrester survey, 90% of firms have difficulty scaling data analytics and AI across their enterprises. And data quality is the top culprit, followed closely by data integrations and data access. In fact, the report goes on to note that:

58% lack confidence in the quality of their data sources
54% lack integration with analytics, data science, and AI platforms
53% lack data access and democratization
40% have low confidence in data governance issues
37% lack confidence in connecting data sources

Without properly curated data, AI and analytics initiatives fall short, leading to increased costs, missed deadlines, and regulatory risks. But to compete and thrive in this new digital age, corporations must leave legacy technology behind and embrace automation, predictive analytics, and the myriad benefits that advanced data analytics and AI offer.

The solution? Look to external experts like Beyondsoft, with proven skills, expertise, and experience in helping enterprises overcome data obstacles.

A best-practice approach

When we partner with clients struggling with data-centric issues, we work to understand your business first and then deliver the right technology services to propel your business forward. Every engagement begins with an effort to understand your organization’s pain points and business needs. We construct a data and analytics roadmap and plan out a data strategy and data ecosystem. We then implement a data architecture and integrate data foundations and platforms, structuring data through data labeling, platforms and engineering, and data recognition.

Once your data structure is established, and you’ve got quality, integrated data, we set up everything you need to derive actionable insights, including business intelligence and visualizations through intelligent dashboards, data analytics, and self-serve reporting. At this point, you’re ready to innovate solutions, leveraging data science and automation through AI and machine learning.

Every Beyondsoft engagement is built on three essential best practices:

Data governance (data quality): The process, roles, policies, and standards to ensure your data is high quality, meaningful, available, usable, timely, accurate, and secure.
Data fabric/data mesh (data integrations): Technical architectures that facilitate the end-to-end integration of your various data pipelines, data stores, and cloud environments through intelligent and automated systems.
Data democratization (data access): Making digital information accessible for your people, processes, and technical platforms, building the foundation for self-serve analytics, predictive analytics, and data-driven decisions across the data ecosystem.

These best practices help ensure data quality and consistency across your organization, instilling confidence and producing better, more actionable insights to inform better business decisions. They also help to establish transparent policies and platforms to support enterprise-wide data infrastructure, allowing your organization to be more agile. Finally, these best practices enable seamless growth and scalability, regardless of the exponential increase in data volume.

The Beyondsoft engagement model

We tailor our engagement model to suit your unique technical environment, project oversight models, product ownership matrix, and distributed / remote team structure. With these engagement models, you can:

Extend your in-house capacity
Increase specific capabilities by adding thought leadership to your team
Launch a new support or R&D team
Outsource operational management of specific processes
Decrease labor costs through nearshore / offshore

If your organization is struggling to scale analytics and AI or if you’re exploring the idea, contact us today for a consultation.

How a upsertable data lakes can simplify the data lake journey

Migrating data from traditional SQL databases to a data lake presents multiple advantages for organizations.

Data lakes offer the flexibility and scalability to manage growing volumes of data. Data lakes enable organizations to take advantage of disconnected, structured and unstructured data streams such as customer data, IoT sensors, and click streams. Organizations can more cost-effectively store unused data and leverage it down the road for advanced analytics as opportunities arise and as new technologies become available. Furthermore, because they separate storage from compute, data lakes offer far greater flexibility and scalability.

At the same time, migrating to a data lake architecture can be daunting for organizations whose database administrators (DBAs) are accustomed to SQL databases and transactions characterized by atomicity, consistency, isolation, and durability (ACID). Because data lakes are not ACID-compliant, simple DBA tasks such as updating or deleting records are not as straightforward, since data lake files cannot be changed or updated directly. Instead, these tasks require complex scripting and file handling.

The good news is that traditional DBAs can perform these operations with a similar level of skill and effort using what we’re calling “upsertable data lakes”—essentially ACID-compliant, modern table formats that combine the best features of data lakes and data warehouses. In this article, we’ll explore how upsertable data lakes such as Delta Lake and Hudi can ease your organization’s transition to a data lake architecture, all while leveraging the experience and knowledge of your existing DBAs.

Some challenges of data lakes

Managing a data lake is different from managing a relational database and requires different skillsets. Data lakes require data validations in multiple places, and preventing failures necessitates the handling of files and complex scripting that is unfamiliar to traditional, SQL-centric DBAs.

Then there are the challenges of cleansing or deleting data to comply with data retention and privacy laws such as GDPR. In a relational database, these tasks can be accomplished easily through SQL commands. But in a data lake, it can be a complex endeavor, requiring skillsets that fall outside a traditional DBA’s wheelhouse. And because files are immutable, there is some risk of instability during transitions if transactions are not ACID compliant.

How upsertable data lakes reduce complexity

Because they fuse the best of data lakes and data warehouses, upsertable data lakes such as Delta Lake and Hudi provide a more familiar interface for DBAs. Upsertable data lakes take what DBAs love about data warehouses and apply them to a data lake setting, which can make the transition to a data lake architecture easier. Thanks to a rapidly closing feature gap between upsertable data lakes and relational databases, the data lake transition is a much more familiar journey.

To illustrate this feature familiarity, let’s say a DBA needs to delete or update a record. Performing this simple task in a traditional data lake can require the creation of complex scripts. But with an upsertable data lake, it’s a simple matter of running a delete statement or an update statement, as would happen in a relational database.

Furthermore, due to the immutable nature of data lakes files, changing the record requires the creation of a new file with the change, and the deletion of the old one. During this process, oftentimes the data will be in an unstable state. This is where an upsertable data lake comes in like a hero, bringing ACID compliance to transactions to guarantee data validity. Thanks to ACID compliance, an upsertable data lake abstracts all the file handling underneath the hood, which means DBAs no longer have to think about the file handling. The data is never in an unstable state.

Contact Beyondsoft for a data architecture health check

Where are you on your data lake journey? Beyondsoft has performed hundreds of data migrations and big data projects for large enterprise customers. Our certified practitioners have hands-on, best-practice knowledge of all the major platforms, including AWS and Azure.

We can partner with you to analyze your data architecture and business requirements to help you determine if an upsertable data lake is the right fit and determine the best migration strategy. To learn more about how Beyondsoft can help you with your data lake journey, contact us today.

Choosing the right data migration strategy

Migrating to the cloud offers multiple benefits. Organizations looking to modernize can reap enormous advantages that range from decreased licensing and storage costs to increased performance and scalability. Migrating to the cloud enables organizations to advance their data analytics and even drive new revenue streams.

Of course, detailed planning and preparation are a must if you want to increase the success of your data migration. And choosing the right data migration strategy is a key part of this process.

I’ve participated in countless data migrations. Below, I’ll overview the four approaches that I most often see, as well as their advantages and disadvantages. For additional help choosing the right strategy, be sure to check into a data migration assessment.

1. Lift and shift approach

The “lift and shift” is the most basic data migration strategy. Organizations use this approach when taking the same database technology that they’re using on premise—say, an Oracle database—and shifting it to the cloud.

The main advantage of this approach is its simplicity and speed and the availability of tools that can facilitate the migration. But keep in mind that when you lift and shift an existing database, you’re also moving all of its hidden problems, hacks, and band-aids to the cloud. The technical debt you had on premise will still be an issue in the cloud—sort of like moving out of a roach-infested house and taking the roaches with you to your new home.

2. Data lake approach

The data lake approach involves removing data from an on-premise database and storing it in a solution such as Amazon S3. For this approach, you can employ a variety of available technologies to access, analyze, and query the data. This approach also gives you the ability to load the data into your database of choice, such as Redshift. Plus, because there’s a middle ground where the data sits, you can load it into the target database in parallel.

This approach offers the flexibility to use any database. While it does require redesigning your database to some degree, you can make changes to the data along the way and increase the performance in your target database. Additionally, because you are building something new and modernizing your database application, you can exterminate those roaches.

Additionally, this approach enables you to separate storage from compute, so you can take advantage of newer compute technologies as they emerge. There are also cost advantages of separating data that is accessed seasonally from data that is accessed regularly. A data lake allows you use compute power to handle those spikes and shut if off when the data isn’t being used.

Of course, there are some tradeoffs. Direct queries against a data lake are typically slower than queries against a database. In situations where you have a higher performance requirement, it makes sense to load that data into a higher performing database. But you can still load your data from the data lake into a tool that allows that high performance. Another tradeoff is that data lakes introduce a new set of technologies to engineers and DBAs who must contend with a steep learning curve. This new domain of knowledge requires considerable ramp up.

3. Combo approach: Lift and shift + data lake

Sometimes, there is a pressing business need to migrate—such as a datacenter that is shutting down. The timeline is short, and the organization needs to get the database off premise quickly—before migrating to a new database solution.

In this hybrid approach, you can combine the above-mentioned strategies, first using the lift and shift approach to migrate the existing database to the cloud, then, taking the time to migrate to a new database such as Redshift or Aurora.

In addition to getting to the cloud quickly, this approach gives you breathing room to determine the best database to be used for your data. The disadvantage is that in some cases, it ends up creating more work in the long run, since you have to make your database work in the cloud. And then you have to migrate to a new database technology.

4. Hybrid streaming approach

In some cases, an organization wants the benefits of a data lake in the cloud, but for business reasons must keep an existing transactional database on premise. Using change data capture (CDC), you can enable changes on premise to stream into the cloud in near real-time. By taking a streaming approach to getting data into the cloud, you can get benefits from the cloud without moving the database. On the downside, replication from the on-premise database is more prone to failure since typically the same redundancies aren’t in place.

Choose the right strategy with a data migration assessment

Choosing the right data migration strategy is critical to your overall success, and depends upon business requirements and drivers, pain points, performance needs, short-term plans, future goals, and multiple other factors.

A data migration assessment from Beyondsoft applies a holistic approach to preparing for a data migration and provides a sound foundation and blueprint for completing your database and application migration. Our proven execution model addresses areas such as business applications and workloads, technical operations, production support, and training to determine the best migration architecture and strategy. Our experts work with you to perform a detailed analysis of your existing database and application architecture and create a roadmap for your migration that includes business, functional, technical, and testing requirements as well as recommendations for addressing gaps. We also create an ROI model that demonstrates performance measures and the time it will take to recoup the cost of your migration.

Here at Beyondsoft, we’ve performed countless database migrations and big data projects for large enterprise customers around the globe leveraging technologies such as PostgreSQL, Amazon RDS, Amazon Redshift, Vertica, Presto, ConvergDB, and more. A data migration assessment is a worthwhile investment that will save you time and money down the road. To learn more, talk to one of our data migration experts.

How to increase the success of your data migration

Data migrations are a popular undertaking these days. In fact, according to the IDC, data migrations comprise around 60% of all large enterprise IT projects. Although the reasons can vary, data migrations are typically motivated by cost reduction, performance, or a combination of the two.

From a cost perspective, large databases and data warehouses can get expensive and licensing fees can escalate out of control. Meanwhile, migrating to less expensive cloud-based solutions can save substantial money.

From a performance standpoint, legacy platforms that are approaching end of life can be a forcing factor. Decoupling data from compute and leveraging cloud capabilities can increase agility and performance. Finally, some enterprise organizations are looking to monetize data and expand market share by taking advantage of more modern platforms.

Ultimately, enterprises that want to compete effectively and drive business growth must embrace modern, more agile solutions. Yet, for all its benefits, a data migration can be a complex and time-consuming undertaking, fraught with the unexpected. Plus, for the uninitiated, it can be hard to know where to start.

The good news is that the proper planning and preparation, as well as choosing the right migration tools, can reduce risk, streamline your data migration, and optimize performance and return on investment (ROI). In this article, I want to explore a few common data migration obstacles and pitfalls and outline steps you can take to minimize risk and increase the success and speed of your data migration.

Common obstacles

One key obstacle to moving forward with a data migration is lack of internal expertise. Most organizations simply don’t have the skillsets to perform a migration in-house. Hiring dedicated internal experts can be cost-prohibitive. Furthermore, even if an organization has the in-house talent, they’re likely consumed with day-to-day, business critical tasks and don’t have the time to devote to a migration.

Many organizations don’t have methods to obtain a clear data migration path and ROI, which means they’re mostly focused on the price tag instead of increased performance and future cost savings. Additionally, many organizations haven’t pinpointed the desired outcomes and performance expectations of their data migration, which can set them up for headaches down the road.

Finally, many organizations simply haven’t had exposure to multiple platforms, making it difficult to choose the best solution. We’ve seen enterprise customers default to the platform that they’re most familiar with only to be disappointed with the results and forced to consider a second migration to a more suitable platform. In this case it is better to understand future business requirements before solutioning.

Common pitfalls

Without the proper planning and expertise, many things can go wrong. From a business standpoint, there is tremendous pressure on the product owner who can face financial and reputational risks caused by data losses, frequent or extended downtimes, and security issues. To minimize these kinds of issues, it’s critical to establish clear expectations and success criteria around delivery timeframes, expected downtimes, data validation, cost optimization, and long-term post-migration support.

From an ROI standpoint, many organizations fail to prioritize properly, leading to disappointing time to value. It’s important to remember that database migrations also have applications attached, and when there are a large number, the entire effort can be very time consuming, not to mention costly. Choosing the most profitable application sets as top-priority migration candidates can deliver short-run profit objectives and long-term benefits with cloud innovations.

Additionally, even with thorough planning, there will always be unknowns. When I talk about performing a data migration, I often use the metaphor of remodeling a house. While much of the remodeling work is obvious, it’s what’s behind the walls or under the floor joists that can be most challenging. Whether it’s faulty wiring, mold, or structural issues, once you begin tearing down walls, you’re sure to come across some unknowns that cost time and money to correct.

The same is true for a data migration. For example, ETL and the way in which data has been blended from multiple sources can be difficult to contend with. You may find stored procedures and business logic that is tied into the database that you weren’t expecting. Depending upon the age of the database, you may have had multiple DBAs doing the work, and you may find yourself unraveling what they’ve done, without any historical knowledge to understand the full impacts.

Finally, when migrating from one database or data warehouse to another, it’s critical to understand the business logic and stored procedures that exist between the two. There are tools-based solutions available that can identify these migration patterns for you and automate a substantial portion of the migration process. These tools can help speed up the migration, increase accuracy, and minimize risk of human error. However, without in-depth experience and knowledge of data migrations, it can be hard to know which tools to use and how to use them. And not having the right tool can add several months to a migration.

Why you need a data migration assessment

These obstacles and pitfalls can impact not only your budget and timeline, they can also impact the overall outcome. Which is why the first thing that any enterprise organization should do is to engage with an industry expert like Beyondsoft to perform a thorough data migration assessment.

A data migration assessment from Beyondsoft provides a solid foundation for completing your data, ETL workflow, reports, and applications migration. Our proven execution model addresses areas such as business applications and workloads, technical operations, production support, and training to determine the best migration architecture and approach—including the selection of tools to accelerate your migration.

Our experts work with you to understand your business needs and objectives and develop a clear business case for initiating a migration as well as expected outcomes. We work with you to define specific success criteria to enable on-time delivery, minimize customer disruptions, ensure data accuracy, optimize costs, and smooth the post-migration transition through support and training.

As part of our assessment, we perform a detailed analysis of your existing database and application architecture, including schema, objects, code, and data volume and create a clear roadmap for your migration that includes business, functional, technical, and testing requirements as well as recommendations for addressing identified gaps. Because we have experience with multiple migration patterns, we can quickly identify stored procedures and business logic and pinpoint the right tools-based solution to accelerate your migration.

We also work with you to prioritize migration candidates using a proven, best practice approach. We examine factors including application size, complexity, cost, savings, and overall effort to determine the candidates that deliver an ROI model that maximizes migration time to value while also setting you up for long term success.

As a trusted enterprise business partner, Beyondsoft has successfully delivered on countless database migrations and big data projects. Our experienced, global experts are deeply familiar with technologies such as PostgreSQL, Amazon RDS, Amazon Redshift, Vertica, Presto, ConvergDB, and more.

A data migration assessment is a worthwhile investment that will save you time, money, and frustration down the road. To learn more, talk to one of our experts.