4 Issues in Data Migration from Legacy Systems to Avoid

The scales have finally tipped! According to a Flexera survey, 93% of organizations have a multi-cloud strategy and 53% are now operating with advanced cloud maturity. For those who are now behind the bell curve, it’s a reminder that keeping your data architecture in an on-premises solution is detrimental to remaining competitive. On-prem architecture restricts your performance and the overall growth and complexity of your analytics. Here are some of the setbacks of remaining on-prem and the benefits of data migration from legacy systems.

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Greater Decentralization

For most organizations, data architecture did not grow out of an intentional process. Many on-prem storage systems developed from a variety of events ranging from M&A activity and business expansion to vertical-specific database initiatives and rogue implementations. As a result, they’re often riddled with data silos that prevent comprehensive analysis from a single source of truth.

When organizations conduct reporting or analysis with these limitations, they are at best only able to find out what happened – not predict what will happen or narrow down what they should do. The predictive analytics and prescriptive analytics that organizations with high analytical maturity are able to conduct are only possible if there’s a consolidated and comprehensive data architecture.

Though you can create a single source of data with an on-prem setup, a cloud-based data storage platform is more likely to prevent future silos. When authorized users can access all of the data from a centralized cloud hub, either through a specific access layer or the whole repository, they are less likely to create offshoot data implementations.

Slower Query Performance

The insights from analytics are only useful if they are timely. Some reports are evergreen, so a few hours, days, or even a week doesn’t alter the actionability of the insight all that much. On the other hand, real-time analytics or streaming analytics requires the ability to process high-volume data at low latency, a difficult feat for on-prem data architecture to achieve without enterprise-level funding. Even mid-sized businesses are unable to justify the expense – even though they need the insight available through streaming analysis to keep from falling behind larger industry competitors.

Using cloud-based data architecture enables organizations to access much faster querying. The scalability of these resources allows organizations of all sizes to ask questions and receive answers at a faster rate, regardless of whether it’s real-time or a little less urgent.

Plus, those organizations that end up working with a data migration services partner can even take advantage of solution accelerators developed through proven methods and experience. Experienced partners are better at avoiding unnecessary pipeline or dashboard inefficiencies since they’ve developed effective frameworks for implementing these types of solutions.

More Expensive Server Costs

On-prem data architecture is far more expensive than cloud-based data solutions of equal capacity. When you opt for on-prem, you always need to prepare and pay for the maximum capacity. Even if the majority of your users are conducting nothing more complicated than sales or expense reporting, your organization still needs the storage and computational power to handle data science opportunities as they arise.

All of that unused server capacity is expensive to implement and maintain when the full payoff isn’t continually realized. Also, on-prem data architecture requires ongoing updates, maintenance, and integration to ensure that analytics programs will function to the fullest when they are initiated.

Cloud-based data architecture is far more scalable, and providers only charge you for the capacity you use during a given cycle. Plus, it’s their responsibility to optimize the performance of your data pipeline and data storage architecture – letting you reap the full benefits without all of the domain expertise and effort.

Hindered Business Continuity

There’s a renewed focus on business continuity. The recent pandemic has illuminated the actual level of continuity preparedness worldwide. Of the organizations that were ready to respond to equipment failure or damage to their physical buildings, few were ready to have their entire workforce telecommuting. Those with their data architecture already situated in the cloud fared much better and more seamlessly transitioned to conducting analytics remotely.

The aforementioned accessibility of cloud-based solutions gives organizations a greater advantage over traditional on-prem data architecture. There is limited latency when organizations need to adapt to property damage, natural disasters, pandemic outbreaks, or other watershed events. Plus, the centralized nature of this type of data analytics architecture prevents unplanned losses that might occur if data is stored in disparate systems on-site. Resiliency is at the heart of cloud-based analytics.

It’s time to embrace data migration from legacy systems in your business. 2nd Watch can help! We’re experienced with migration legacy implementations to Azure Data Factory and other cloud-based solutions.

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Private Equity Roll-Up Strategies – Accelerated Value Creation Through Data and Analytics

Private equity firms have a major challenge when executing roll-up strategies in their investment sectors. Rolling up multiple acquisitions creates an information and reporting nightmare. How do the PE firms and their operating C suite teams quickly get basic financial reporting from each acquisition and how can they get consolidated financial information across the newly built enterprise? And once basic financial reporting is in place, how do they accelerate financial and operating transformation finding cash flow enhancement and EBITDA improvement opportunities?

The Challenge: As your roll-up deals close, your financial data sources grow exponentially and producing accurate financial reports becomes extremely difficult and time-consuming.

As deals in the acquisition roll-up pipeline begin to close, there is typically a proliferation of information management systems from each of the newly acquired companies. As the number of information systems mounts, it becomes increasingly difficult to produce good financial reports. Typically, this results in a manual financial consolidation process which requires each acquisition to submit their statements and then the PE team must manually consolidate. A very difficult and time-consuming process at a point in time that is extremely critical for the deal.

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With this newly acquired “Tower of Financial Babel,” PE firms and their operators are looking to accelerate profit improvement and value creation. However, they are usually trying to make management and value transformation decisions while sorting through a legion of information management systems with different charts of accounts, conflicting formats, and diverse accounting policies. In order to deal with this mounting incremental complexity, the deal accounting team must try to reconcile these different statements, frequently requiring multiple conversations with each of the acquired company’s executive teams. In turn, this takes away valuable time needed by each of the executive leadership teams to manage their company and create value.

The reality is that the financial consolidation process for the typical PE roll-up becomes highly complex, very manual, and very slow. As more roll-up deals are completed and added on to the enterprise, this problem only gets worse and more fragmented. And given this difficult process, the consolidated statements are often delayed and even become suspect as the potential for reporting errors increases with process complexity.

Those are just the basics. From there, incremental value creation and financial transformation is extremely difficult. How can the PE firms quickly begin the financial and operating transformation that needs to take place? Specifically, how does the typical PE firm identify the EBITDA improvement opportunities as well as the cash flow harvesting that needs to take place across the new and expanding acquired enterprise?

The first thought might be to merge all of the new acquisitions onto one of the incumbent systems. However, these types of large-scale ERP migrations projects are very risky and prone to failure, more often than not running late and way over budget. And they are incredibly disruptive to location operating management who are also integrating into the new ownership structure as well as trying to improve operations and financial results. Therefore, it is easy to see why migrating each new acquisition from the deal pipeline onto a new ERP, EMR, or any other centralized management platform is difficult, complex, expensive, and disruptive to the new deal at a time when the new acquisition is vulnerable. PE firms want their operating teams focused on execution, operations, and financial performance – especially when the roll-up strategy is in the initial stages.

Worst of all, the PE and the executive teams could possibly be integrating onto an existing enterprise information management system that may be sub-optimal for the new larger, consolidated company. This type of process will most likely create short-, medium-, and long-term management issues which will surely be recognized in the future during the deal exit process.

Instead of migrating onto an incumbent system, why not take a long-term approach by developing a truely data-driven strategy? Take time to develop a holistic view of the new enterprise and be deliberate when devising and implementing a new enterprise management system. This enterprise view will allow the roll-up to find and implement the market-leading systems creating the ultimate strategic competitive advantage for the future state company. And this data strategy can be developed quickly – often in a matter of a few weeks.

Then how do the PE firm and the roll-up operating executive team quickly get the financial information needed to run and manage the company and begin the financial/operational transformation?

The best way to accomplish these goals with minimal operating company disruption on a relatively short timeline is to merge the portfolio companies on a data basis. Pull each of the portfolio company’s sources of data (ERP, CRM, EMR, auxiliary systems, social media, IoT, third-party, etc.) into a consolidated data warehouse. Then integrate the companies on a virtual basis designing and implementing a powerful data model to enable the consolidation.

Once the data warehouse and the consolidated data model are in place, standardized single-source-of-truth finance and operating statement dashboards will be in place. These dashboards will be on a near real-time basis for PE management, their lenders, the executive management team, local operating managers, and employees. These dashboard reports will enable self-service analytics allowing for a much deeper understanding of portfolio company performance and efficient operational reviews and conversations.

With the dashboards in place, the complete team can focus on value identification and creation with standardized KPIs (e.g., EBITDA, operating metrics, cash flow, etc.). Dashboards can be designed and built to benchmark across the portfolio companies and also up against best industry practices. Value trend lines can be measured as well as identification of best-case or lagging performers. Specific areas of performance can be targeted (e.g., A/R, inventory, fixed asset utilization, labor efficiency, etc.), again comparing performance and finding sources of cash flow improvement. With this tremendous analytics and insight, best practices can be identified and action plans can be created to implement across the new enterprise. Conversely, remediation action plans can put in place for lagging performers with improvement monitored and scorecarded.

Building and implementing an enterprise data warehouse and business intelligence dashboards creates an asset of true incremental value. With all enterprise data consolidated and modeled, this will open up opportunities for predictive analytics, data science, and deep value discovery. New marketing analytics strategies can be put in place. New products and services can be imagined and developed through data science ROI use cases. Optimization opportunities will be found, analyzed, and implemented. And so on as the operating teams become data-savvy.

In summary, the quickest and most effective way to affect a PE financial and operating transformation for a roll-up is to integrate at the data level. This data integration, consisting of a data warehouse and targeted financial and operating dashboards, will accelerate the value creation deal process. In turn, this will maximize the financial ROI of the deal generating the greatest return for the investment firm and their fund investors.

If you’re ready to accelerate value creation throughout your portfolio, contact us today to schedule a complimentary Advanced Analytics and Reporting Whiteboard Strategy Session.

Bylined by Jim Anfield, Principal at 2nd Watch

Ready to Migrate your Data to the Cloud? Answer these 4 Questions to find Out

Many companies are already storing their data in the cloud and even more are considering making the migration to the cloud. The cloud offers unique benefits for data access and consolidation, but some businesses choose to keep their data on-prem for various reasons. Data migration isn’t a one size fits all formula, so when developing your data strategy, think about your long-term needs and goals for optimal results.

We recommend evaluating these 4 questions before making the decision to migrate your data to the cloud:

1. Why do you Want to Migrate your Data to the Cloud?

Typically, there are two reasons businesses find themselves in a position of wanting to change their IT infrastructure. Either your legacy platform is reaching end of life (EOL) and you’re forced to make a change, or it’s time to modernize. If you’re faced with the latter – your business data expanded beyond the EOL platform – it’s a good indication migrating to the cloud is right for you. The benefits of cloud-based storage can drastically improve your business agility.

2. What is Important to You?

You need to know why you’re choosing the platform you are deploying and how it’s going to support your business goals better than other options. Three central arguments for cloud storage – that are industry and business agnostic – include:

  • Agility: If you need to move quickly (and what business doesn’t?), the cloud is for you. It’s easy to start, and you can spin up a cloud environment and have a solution deployed within minutes or hours. There’s no capital expense, no server deployment, and no need for an IT implementation team.
  • Pay as you go: If you like starting small, testing things before you go all in, and only paying for what you use, the cloud is for you. It’s a very attractive feature for businesses hesitant to move all their data at once. You get the freedom and flexibility to try it out, with minimal financial risk. If it’s not a good fit for your business, you’ve learned some things, and can use the experience going forward. But chances are, the benefits you’ll find once utilizing cloud features will more than prove their value.
  • Innovation: If you want to ride the technology wave, the cloud is for you. Companies release new software and features to improve the cloud every day, and there’s no long release cycles. Modernized technologies and applications are available as soon as they’re released to advance your business capabilities based on your data.

3. What is your Baseline?

The more you can plan for potential challenges in advance, the better. As you consider data migration to the cloud, think about what your data looks like today. If you have an on-prem solution, like a data warehouse, lift and shift is an attractive migration plan because it’s fairly easy.

Many businesses have a collection of application databases and haven’t yet consolidated their data. They need to pull the data out, stage it, and store it without interfering with the applications. The main cloud providers offer different, but similar options to get your data into a place where it can be used. AWS offers S3, Google Cloud has Cloud Storage, and Azure provides Blob storage. Later, you can pull the data into a data warehousing solution like AWS Redshift, Google BigQuery, Microsoft Synapse, or Snowflake.

4. How do you Plan to use your Data?

Always start with a business case and think strategically about how you’ll use your data. The technology should fit the business, not the other way around. Once you’ve determined that, garner the support and buy-in of sponsors and stakeholders to champion the proof of concept. Bring IT and business objectives together by defining the requirements and the success criteria. How do you know when the project is successful? How will the data prove its value in the cloud?

As you move forward with implementation, start small, establish a reasonable timeline, and take a conservative approach. Success is crucial for ongoing replication and investment. Once everyone agrees the project has met the success criteria, celebrate loudly! Demonstrate the new capabilities, and highlight overall business benefits and impact, to build and continue momentum.

Be Aware of your Limitations

When entering anything unknown, remember that you don’t know what you don’t know. You may have heard things about the cloud or on-prem environments anecdotally, but making the decision of when and how to migrate data is too important to do without a trusted partner. You risk missing out on big opportunities, or worse, wasting time, money, and resources without gaining any value.

2nd Watch is here to serve as your trusted cloud advisor, so when you’re ready to take the next step with your data, contact Us.

Learn more about 2nd Watch Data and Analytics services

-Sam Tawfik, Sr Product Marketing Manager, Data & Analytics

Migrating Data to Snowflake – An Overview

When considering migrating your data to the cloud, everyone’s familiar with the three major cloud providers – AWS, Google Cloud, and Microsoft Azure. But there are a few other players you should also take note of. Snowflake is a leading cloud data platform that offers exceptional design, scalability, simplicity, and return on investment (ROI).

What is Snowflake?

The Snowflake cloud data platform was born in the cloud for data warehousing. It’s built entirely to maximize cloud usage and designed for almost unlimited scalability. Users like the simplicity, and businesses gain significant ROI from the wide range of use cases Snowflake supports.

Out of the box, Snowflake is easy to interact with through its web interface. Without having to download any applications, users can connect with Snowflake and create additional user accounts for a fast and streamlined process. Additionally, Snowflake performs as a data platform, rather than just a data warehouse. Data ingestion is cloud native and existing tools enable effortless data migration.

Business Drivers

The decision to migrate data to a new cloud environment, or data warehousing solution, needs to be based on clearly defined value. Why are you making the transition? What’s your motivation? Maybe you need to scale up, or there’s some sort of division or business requirement for migration. Often times, companies have a particular implementation that needs to change, or they have specific needs that aren’t being met by their current data environment.

Take one of our clients, for instance. When the client’s company was acquired, they came to utilize a data warehouse shared by all the companies the acquiring company owned. When the client was eventually sold, they needed their own implementation and strategy for migrating data into the cloud. Together, we took the opportunity to evaluate some of the newer data platform tools, like Snowflake, for their specific business case and to migrate quickly to an independent data platform.

With Snowflake, set up was minimal and supported our client’s need for a large number of database users. Migrating from the shared data warehouse to Snowflake was relatively easy, and it gave all users access through a simple web interface. Snowflake also provided more support for unstructured data usage, which simplified querying things like JSON or nested data.


Migrating data to Snowflake is generally a smooth transition because Snowflake accepts data from your existing platform. For instance, if data is stored in Amazon S3, Google Cloud, or Azure, you can create Snowflake environments in each then ingest the data using SQL commands and configuration. Not only can you run all the same queries with minor tweaks and get the same output, but Snowflake also fits additional needs and requirements. If you’ve worked in SQL in any manner – on an application database, or in data warehousing – training is minimal.

Another advantage with Snowflake is its ability to scale either horizontally or vertically to pull in any amount of data. And since it is cloud native, Snowflake has embraced the movement toward ‘pay as you go’ – in fact, that’s their entire structure. You only pay for the ingestion time and when the data warehouse is running. After that, it shuts off, and so does your payment. Cost-effective implementation lets you experiment, compare, test, and iterate on the best way to migrate each piece of your data lifecycle.

Long Term Results

Snowflake has yielded successful data migrations with users because of its ease of use and absence of complications. Users also see performance improvements because they’re able to get their data faster than ever and they can grow with Snowflake, bringing in new and additional data sources and tools, taking advantage of artificial intelligence and machine learning, increasing automation, and experimenting and iterating.

From a security and governance perspective, Snowflake is strong. Snowflake enforces a multi-layer security structure, including user management. You can grant access to certain groups, organize them accordingly, integrate with your active directory, and have it run with those permissions. You assign an administrator to regulate specific accessibility for tables in specified areas. Snowflake also lets you choose your desired security level during implementation. You have the option of enterprise level, HIPAA compliance, and a maximum security level with a higher rate per second.

Do you want to explore data migration opportunities? Make the most of your data by partnering with trusted experts. We’re here to help you migrate, store, and utilize data to grow your business and streamline operations. If you’re ready to the next step in your data journey, Contact Us.

Learn more about 2nd Watch Data and Analytics services

-Sam Tawfik, Sr Product Marketing Manager, Data & Analytics