Data Modernization: What You Need to Know

From operations to performance to nurturing customer relationships, exceptional data use drives exceptional outcomes. Once the purview of only the largest, most well-funded enterprises, all organizations today need effective data use to thrive and keep pace with change.

Everyone has data, but outmoded systems and processes will prevent your organization from utilizing it to its full potential. Data modernization can provide needed insights while cutting costs, but those benefits won’t come without first getting clear about strategy and objectives.

A recent Forrester survey revealed that data-driven companies are 58% more likely to beat revenue goals than those not focused on data. A BARC research report showed that businesses using big data realize a 10% reduction in costs and at least 8% increase in profits.

Data modernization means making data more accessible and easier to work with so organizations can take advantage of evolving technologies like visual analytics, machine learning (ML), and artificial intelligence (AI). It starts with strategy and depends upon updating infrastructure, because without infrastructure architected specifically to accommodate new approaches—both existing and those yet to be developed—none of the rest is possible. From there, much more becomes possible, including insight that helps your organization thrive.

The big problem with old data infrastructure is that it’s hard to maintain. Organizations that still use it are constantly asking for enhancements that become more and more difficult to execute; adding anything to these systems is challenging and the opposite of timely.

Traditional data warehouses are also not well documented, which prevents an accurate data model. They’re usually on-prem, which imposes capacity and performance speed limitations by the physical machine.

Often, people with these warehouses don’t know how bad they’ve got it. They’ve reconciled themselves to the limitations of speed, space, and power without realizing there’s a better way. We can help them improve in ways they didn’t know were possible.

- Michael Tantrum, National Sales Director, Resultant

The evolution to a modern data infrastructure can produce significant return on investment, including:

• Fewer staff hours devoted to prepping and moving data and maintaining data environments
• Reduced cost of ownership—significantly lower CAPEX
• Improved analytics
• Improved access to data across the team
• Faster time to insight
• Data democratization

Some of the most important benefits are realized from making your data available to the people who need it most at the times they need it most—producing insights that were previously not possible with older, slower, more cumbersome infrastructure.

The modern data pipeline loads, integrates, and cleans your data. It’s flexible, expandable, and versatile. It is the backbone of how you move the data. And there isn’t anything like this modern data pipeline in legacy systems. Cloud-based modern data warehouses are almost infinitely scalable, and since you only pay for what you use, they’re practically a no-brainer.

With modern infrastructure in place, a whole world of process automation opens up, freeing valuable team members to focus their efforts where they will make the greatest impact. No longer do they need to devote time to tasks that technology can execute automatically. Processes in development, documentation, CI/CD, testing, and data governance can all be automated.

The absence of a modern data infrastructure is very limiting to a company wanting to make better use of its data and prevents moving and processing data efficiently. Organizations tend not to think about data modernization until they find themselves needing to add capacity or add or renew licenses and come up against surprising associated costs. Here’s how modernization helps.

Distributed Computing

Processing data can happen in parallel and is seamless. You’re not limited by the capacity of your physical on-prem servers, in either space or performance speed.

Automation

There’s no such thing as the server being down. Multiple redundancies are in place to prevent such a crisis. Other automated processes take the load off team members so they can focus on work that has real impact. The system needs fewer separate infrastructure components and requires fewer technical skills to operate.

Flexibility

Processes can batch different types of data in real time. Whereas in the past, maintenance increased along with the footprint of the code, that’s no longer the case. You don’t have to keep modifying your system when adding new data sources anymore, and you can address multiple use cases without having to build different solutions for each one.

Cost Savings

Physical hardware is expensive. Organizations formerly had to guess at needed compute power and capacity with onprem servers and buy for the maximum they’d ever need (which always eventually increases). When they weren’t using the full capabilities of the hardware, it was just sitting there dormant. Compute abilities are still the expensive part of cloud infrastructure, but you can separate the compute functions from the data development functions. Since you only pay for the compute capacity you use—when you use it—it’s a big savings.

To make data-driven decisions, it’s essential to trust that your data is moving from source systems through the pipeline as designed, and that what you receive at the data warehouse is what’s expected. That’s data quality.

Poor data quality can have a devastating impact across your organization, resulting in:

• Stakeholders not trusting data
• Business leaders making poor decisions based on bad data
• Extra expenditure of time and money to troubleshoot and correct data issues
• Increased data security and privacy issues

A sustainable data governance strategy as part of your data modernization project ensures data quality, helps break down data silos, increases data trust, bolsters security, and turns information into actionable insights that support your goals.

Data modernization allows users to combine all types of data, at any scale, more easily than in traditional systems. Data sources used to be limited to files and relational databases. Today companies use various data sources of disparate types like CRMs, marketing platforms, email automation tools, and business intelligence applications, usually connected via a range of APIs.

Your data modernization project is most successful when you lay the groundwork to help your teams thoroughly adopt it. Organizational change management builds an organizational culture that embraces data as an asset. When the value of data is integrated into each role, your team utilizes and maintains trusted, reliable data to reach organizational goals more effectively and efficiently.

Modernization enables much deeper business analytics and intelligence for a broader base of users at a lower cost. It also usually includes moving from an on-prem data center to the cloud, with a data platform optimized for cloud-based analytics.

While the benefits are compelling, it’s critical to implement your modernization project correctly to avoid missteps. According to recent research from Boston Consulting Group, over 70% of digital transformation projects fail. The top reasons for failure include lack of executive support, a culture that can’t or won’t adopt broader collaboration, and failure to bring in talent that has experience executing a data transformation.

Enlisting the help of people who have taken this journey before will significantly increase your likelihood of success. “People trust people, not data,” says Tantrum. “They don’t adopt new technology readily without a human motivation.”

In the past, data systems carried considerable cost and maintenance needs. They were hardware-based and performance limited, and any changes or added capacities took a long time to implement.

Modern data infrastructure saves costs and requires less maintenance and less technical expertise onsite to operate. Many tools take care of the management and maintenance for you, and the time to implementation is very fast.

- Bernardo Unzueta, Director of Data Engineering and Architecture, Resultant

Numerous tools for data transformation are on the market, each with their own strengths and specifics. Choosing the right tool is an area of the project where using a consultant can make a big difference to your outcomes.

To select the best warehouse/pipeline tool for your modernization project, start by answering these questions.

• How many different sources of data do you have?
• What types of data sources are you working with?
• Where does your data live today?
• How much transformation is required to convert raw data into a form ready for analysis?
• Who are the resources who will build your data pipelines, and what are their skillsets?
• Are you building a tactical, business unit-focused solution, or an enterprise-class solution?

“Create use cases to select the best tool for the modernization project,” says Unzueta. “The volume of data your organization has and use case specifics (some may need data streaming for near-real time updates), are important considerations.