Data modernization initiative: 4 tips to get you started

When you first implemented your modern data stack, it probably worked great. Complete, accurate information enabled fast, confident decisions.

After a while, though, a data stack, like any technology, will start to underperform. You might notice more incomplete data in the system. Quality degrades and accuracy fires start cropping up faster than you can put them out.

And once you’ve lost trust in the data, decision-making slows down – bottlenecked by a feeling data should be manually checked before it’s acted on.

This is your (worst case) signal that it’s time to optimize and modernize.

Data modernization updates your data stack to keep up with not just new technology, but with the changing needs of your business.

The Benefits of Data Modernization

When your iPhone slows down, it usually means it’s time to upgrade to a newer model. But let’s face it, not everyone does. As long as it lets them call, text, and maybe watch the occasional TikTok, they just deal with decreased performance.

Likewise, you could just deal with an underperforming data stack. But unlike the iPhone, it’s not just your convenience at stake – it’s the profitability of your business.

The advantages of data modernization include:

• Higher data quality
• Improved data accuracy
• A flexible data stack with fewer data silos that can scale up or down as needed
• Real-time insights for better business decisions
• Higher efficiency
• Lower costs
• A better customer experience

Modernizing the Data Stack for Better Quality

It’s hard (if not impossible) to get high-quality data out of low-quality data systems. Modernizing your data stack improves its ability to clean, standardize, and deduplicate data. 

The data you pull out of a modern data stack is more complete and more accurate than what you get from an outdated infrastructure. That complete, accurate, and, most importantly, reliable data enables leaders to make confident, timely, data-backed decisions to drive the business.

Modernizing the Data Stack for Flexibility

Modern, cloud-based data tools offer on-demand flexibility and scalability for your data stack.

A 2022 survey by Matillion and IDG found businesses were drawing data into their warehouses from 400 or more different sources, and that for many, data volumes were increasing by 60% or more each month.

Modern data tools integrate with a number of data sources and apps, eliminating the need to build and maintain hundreds of custom APIs. Modernization will also help you handle large data volumes without the security risks of an outdated data tool. So you can handle more data with better data governance. Win-win.

Making Better Decisions with Real-Time Data

Data has leveled the playing field in business. A young founder armed with accurate, real-time market data can make fast decisions better than a seasoned veteran relying on gut instincts.

Modern businesses move fast. Clean, real-time information leads to actionable insights that enable stakeholders to make confident decisions that drive the business forward.

Optimizing Spend with a Modern Data Stack

Data engineering is not cheap. Modernizing data is one way you can get the best return on every data dollar you spend.

A “good enough” data architecture is often highly inefficient. Using cheap analytics tools or building manual integrations in-house might seem frugal, but in the long run, what you save in initial tooling costs you in lost productivity.

A modern data stack minimizes the time your data team spends on rote, manual activities. That time can then be spent on innovations, upgrades, and other activities that drive revenue. At the end of the day, every dollar spent on modernizing data infrastructure works harder than the dollars spent making do.

Data Modernization Strategy

Data modernization starts by evaluating your data assets and infrastructure to find the sticking points. It ends with implementing or upgrading tools to improve data quality, scalability, and real-time insights.

Which tools you upgrade or replace, and what criteria you use to evaluate them, depends on the specific pain point you need to solve.

This is the four-step process I recommend for any data modernization initiative.

‍Step 1: Identify the Pain Points in Your Current Data Stack

The good news is that your data stack probably needs a tuneup, not a complete overhaul. So before you start replacing tools and rebuilding connectors, take some time to zero in on where the stack is underperforming.

• Is processing time running slow?
• Are manual integrations devouring engineering hours?
• Does the modeling process feel like reinventing the wheel?
• Is the business team frustrated by a lack of real-time insights?
• Is poor quality affecting your ability to trust your data?

Once you’ve identified and prioritized the issues, you can map them to the layers of the stack most in need of an update. Then you can evaluate all the new and improved technologies to modernize your data and resolve the pain.

Step 2: Evaluate Modern Data Tools

As your business grows, it’s natural to outgrow your original data stack. The tools you chose might have been great to start with, but if they don’t serve your needs now, there’s no way they will serve you well in the future as you continue to scale.

Modernizing your data stack might be as simple as upgrading your plan with a current vendor to a premium tier. Or you may have outgrown that product completely and need to replace it with a faster, more robust ecosystem competitor.

Besides upgrades, you will also want to look at expanding your data stack. Adding tools like reverse ETL or data observability can enhance the performance of tools throughout the stack.

As you evaluate tools, you’ll notice many are cloud platform solutions. Moving from on-premise solutions to the cloud can open up opportunities for flexibility and scalability while reducing overall data storage costs.

Step 3: Make Your Data Modernization Plan

Once you’ve identified your data stack problems and evaluated possible solutions, you’re ready to plan the update.

Your data modernization roadmap should include:

• A prioritized list of issues to address
• A prioritized list of tools and technologies
• A timeline for when each new technology will be implemented
• The tasks involved in each step of the implementation
• Who is responsible for each task

Step 4: Implement Tools and Automate Your Data Pipeline

Finally, you’re ready to start the data modernization process. Follow your plan to address the highest priority issues first.

Keep your modern data stack running smoothly by automating pipelines as you go. Manual data integration is error-prone and takes a lot of time. Automated pipelines create seamless data integration while freeing up engineering hours to work on higher-impact activities.

Data Modernization Challenges

There’s no question that data modernization helps companies get better use of their data.

However, that’s not to say modernizing a data stack is easy. Like anything worth doing, it has its challenges.

You can generally put these challenges into three categories: people challenges (i.e., skills), scope challenges (i.e., priorities), and timeline challenges (i.e., avoiding business disruptions).

Some of the most common challenges of data modernization include:

• Resistance to change
• A shortage of skilled data professionals to implement and work in the new stack
• Security and compliance concerns around data migration and handling
• Integration issues with existing tech tools
• High up-front costs

Overcoming Resistance to Change

Tools are great, but at the end of the day, it’s the people using the tools who determine a company’s success.

The first “people challenge” is resistance to change. This might come from employees who are comfortable with existing tools, even if they’re not very efficient. Or, it might come from leaders who balk at investing in new tools.

To get past employee resistance, involve them in the evaluation process. Investigate the pain points they experience while doing their job. When proposing a modern tool, emphasize how the change will address the issues they identified.

Leadership resistance is a little different. Convincing leaders it’s time to modernize the data stack usually comes down to numbers. Show them where the current infrastructure is causing bottlenecks and inefficiency, and highlight the potential ROI of a solution. How can they tangibly benefit from data-driven decision-making?

Overcoming a Skills Gap

If you have great technology, but you don’t have a team of professionals skilled in using and maintaining it, you don’t have great technology. Or at least, you can’t use it.

When identifying the pain points in your current data infrastructure, look past the tools at the teams. Do you have enough people in the right roles to achieve data team goals? If not, you may need to modernize your data team before or in conjunction with your data stack.

Skilled data talent is at a premium. Before launching a long and expensive recruitment cycle, consider whether any of your current team members could upskill to take on new responsibilities. You could also look at recruiting talented people from internal departments like finance and IT and training them to join the data team.

Overcoming Security and Compliance Concerns

Data security is a top concern for businesses, and for good reason. In 2023, more than 3,000 U.S.-based data breaches were recorded, impacting more than 353 million people.

In an effort to protect individuals’ data privacy, regulators have implemented a patchwork quilt of data security regulations. Requirements vary from country to country and even from state to state, keeping businesses on their toes as they try to comply.

It’s no surprise, then, that some leaders are afraid tampering with a “good enough” data stack will open a Pandora’s box of privacy and regulatory headaches.

To overcome these fears, during the evaluation stage, make sure any vendors or tools you are considering have substantial data security safeguards in place.

Overcoming Integration Challenges

Each element in your data stack needs to work seamlessly with the other elements. Otherwise, you risk incomplete data and broken pipelines.

While it’s possible to build custom connections between tools, this is usually an expensive solution. In the long run, it’s easier and less expensive to simply look for tools that offer the integrations you need.

The Cost of Data Modernization

When planning your modernization project, take careful stock of the costs of new or upgraded tools. Look at:

• Up-front purchase and implementation costs
• Ongoing maintenance fees
• Additional charges for storage or compute
• Cost of training your staff
• Functional costs, such as buying a separate iPaaS tool for integration

Also, question whether you have enough skilled staff, and if they’re in the right positions to use and maintain the modernized stack.

Next, look at the potential ROI of the tools. This might include metrics like:

• Engineering time saved
• Reduction in lost opportunities
• Greater security, reducing the risk of fines for noncompliance

At the end of the day, no matter how amazing a tool’s capabilities are, the math has to make sense before you invest.

Trends in Data Modernization

Trends come and go in data just like they do in any industry. These trends in data modernization have the staying power to make a long-term impact.

Adoption of Cloud Data Platforms

Cloud data platforms cost significantly less than on-premise data solutions, while offering significantly more data durability.

Most cloud service providers store redundant copies of your data distributed across various server locations – so if the San Francisco data center is lost to a wildfire, you can still access your data from servers in Detroit.

Cloud data platforms are also more flexible and more scalable than on-premise servers, and provide real-time data processing. A modernized data stack is likely to be largely stored in the cloud.

Focus on Data Protection, Privacy and Governance

Ensuring data privacy is vital to building and maintaining customer trust. It’s also crucial to remain compliant with various industry and government regulations, such as GDPR, GLBA, and CCPA.

As data security continues to gain prominence in the cultural conversation, and regulations continue to proliferate, the modern data stack is evolving to incorporate tighter access controls and governance policy restrictions.

Automated Data Processing with AI and ML

Artificial intelligence and machine learning have an exciting role in data modernization. These robot assistants can quickly process and analyze entire data lakes/data warehouses, then provide real-time insights and predictions.

As technology advances, AI will also play a greater role in troubleshooting data quality, combing through queries to find errors in minutes that had human analysts searching for hours.

Real-Time Data Processing

The faster you can get insights from your data, the faster you can make decisions that take advantage of opportunities and avoid threats. A modernized data stack processes and analyzes data in real time, improving business outcomes and overall agility.

For example, let’s imagine a clothing store has a hard time keeping up with inventory management. Poor customer data resulted in ordering too few high-demand sweaters, while there’s excess inventory of socks.

After modernizing your data infrastructure, you can process sales data in real time, allowing you to spot trends and take advantage of them before they’re old news. The upshot of this change is that your customers are happier, your inventory is leaner, and you don’t miss out on sales opportunities because of insufficient stock.

‍Modernizing Data Infrastructure Matters

Your company can’t gain a competitive advantage unless you’re using fresh, accurate, trustworthy data.

Data modernization is an ongoing process. As technology evolves, the bar for performance is constantly moving higher. At the same time, as your business grows and scales, its data management needs and usage evolve.

By identifying points of friction, evaluating new technologies, planning modernization projects, and implementing upgrades, you can ensure your data stack keeps up with these changing priorities.

Metaplane provides automated incident detection, impact analysis, and root cause diagnosis through end-to-end data observability, so you can be confident in your data’s accuracy and reliability.

Get started for free, or book a demo to see how it works.