What It Means to Modernize Data & Analytics

In a prior livestream, we explored modernization in the data & analytics space as it pertains to both business users and technology professionals.

Modernization, especially in the technology and business realm, is all around us these days. Whether it’s in physical forms such as phones, computers, entertainments systems, etc., or in less tangible ways like business processes, enterprise systems, and digital infrastructure.

At Smartbridge, we are all about modernizing the digital space of a business. That is why we wanted to share some of our insights into what that looks like for specifically data & analytics. The following is an overview from a livestream we did on the topic. If you’re a reader, keep on reading! If you’d rather watch the livestream, click here to visit the replay.

Modernizing Data & Analytics – Diving In!

What does it even mean to modernize data analytics?

Modernizing data analytics is a broad topic. Before diving into the technology, it’s essential to align data analytics with the business strategy. This alignment provides agility and a competitive advantage to the business.

How about from a technology standpoint?

Data analytics covers a wide range of areas—data itself, integrations, moving and processing data, housing, and reporting. A key aspect is understanding what makes a data platform modern. This includes handling various types of data: structured, unstructured, and semi-structured. The business needs to define the analytics required. Often, businesses need to move beyond tools like Excel to more advanced tools like Tableau, Power BI, or machine learning and data science capabilities. Additionally, agility is crucial. With the cloud, businesses can quickly spin up databases or virtual machines without lengthy approval processes, leading to faster decision-making.

For someone unfamiliar with modernizing data analytics, where does one even start?

Start with the principle: “Aim big but start small.” Data analytics is a broad field and can feel overwhelming. Focus on what can’t be done today and start by delivering quick insights to show progress. From there, assess the business needs, current data infrastructure, and capabilities. Address the people, process, and technology involved. For people, understand current skills and tools, and provide new tools or training where needed. For the process, streamline the flow of data, simplifying complex, time-consuming processes. After addressing these, move on to technology, enabling the business with the right tools to support the people and process.

What are some of the challenges companies face when modernizing data analytics?

Some challenges aren’t technical but rather psychological, such as the fear of losing control when moving from on-premise systems to the cloud. Overcoming this fear is key to driving business agility. Other common challenges include access to data, the speed of processing analytics, scaling data with growing volumes, and keeping things simple with a minimum viable product. It’s important not to overcomplicate the process—”aim big but start small.”

How do companies ensure success in this process?

One essential factor is governance. The adage “garbage in, garbage out” applies here. Good governance ensures that only quality data enters the system. This can start with an executive-level steering committee, data stewardship, and ensuring data is treated as a strategic asset. Aligning with the business’s strategic priorities is also crucial. Ensure that data analytics initiatives support the business’s goals. Additionally, it’s important to assess whether the team has the skills and capacity to handle new tools and technologies. Lastly, evaluate the architecture and tools, choosing solutions based on the business’s needs—whether it’s a data lake for data scientists or a simpler SQL database.

What are some best practices for modernizing data analytics?

Best practices include staying aligned with the business to keep them engaged, focusing on governance to maintain data quality, and ensuring that data cleansing and manipulation happen before pushing to the cloud. Another best practice is to start small and build up over time. This approach helps to prevent overwhelming the team and ensures the project’s success.

Why would someone choose Smartbridge to modernize their data and analytics?

Smartbridge is a full-service company offering more than just data analytics. We specialize in digital transformation and innovation, with practices that include enterprise solutions and automation (RPA). We partner with industry leaders like Microsoft Azure, Snowflake, and Power BI. Our approach focuses on aligning projects with business outcomes, ensuring that each project is tightly managed. With extensive expertise across industries like energy, retail, and construction, Smartbridge is well-positioned to help businesses with their data modernization journey.

Data & Analytics Services for the Enterprise

Our team of data & analytics experts are certified and backed by decades of experience implementing, integrating, and maintaining data infrastructures and analytics platforms. Smartbridge can help you unlock hidden opportunities and insights so you can build an analytics organization that can drive a true competitive advantage.

Here’s a list of data & analytics services we provide to help your organization succeed:

Looking for more on data & analytics?

Explore more insights and expertise at smartbridge.com/data

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