Do you feel overwhelmed trying to keep up with the latest technologies and tools in the data and analytics space? Are you investing in data initiatives that aren't delivering the results you desire?

It can be tempting to chase after the newest solutions in the market, but it's important to remember that the timeless principles and practices that drive success never change.

In this blog post, we'll explore the core principles essential for successful data and analytics projects, as well as the foundational practices we use with our clients to ensure success. By the end, you'll have an understanding of how to use data and analytics to drive business strategies and achieve tangible results without getting caught up in the constant influx of new technologies.

In today’s fast-paced business world, data and analytics are crucial for driving success. They can help inform strategic decision-making, optimize operations, and provide valuable insights that will help your organization achieve its business goals.

However, not all data initiatives are equal—just as not all approaches deliver successful outcomes. To truly succeed with data, it’s important to employ core principles that guide effective data and analytics initiatives, and to apply foundational practices that lead to successful outcomes.

When we work with our clients on their data and analytics challenges, we apply core principles and foundational practices to every project—enabling us to build data solutions that will support our clients’ initiatives and drive the engagement’s long-term success.

Core Data & Analytics Principles Necessary for Every Project

Every data initiative, no matter the size or scope, should be approached with a set of core principles in mind to ensure its success. These principles include:

Eight grey boxes arranged in two rows with four columns and black text in each box. Orange box with white text above that says "core data and analytics principles for data initiatives".

These eight core data and analytics principles will ensure you stay on task with building data solutions while also meeting short- and long-term business objectives. 

  • Keeping the big picture in mind: Understand the organization’s goals—as well as mission, vision, and values—and how your work aligns with them. This means listening to multiple voices and asking how your involvement will make them successful in the long run. How is what you’re doing helping to fulfill the mission of the organization?
  • Adding value from the start: While you should have and follow a long-term plan for your data initiatives, focus on delivering quick wins by implementing functional solutions that bring quick time-to-value and show initial positive results. This will build momentum and fuel continued focus on larger initiatives. Leverage rapid prototypes of solutions with end-users so that you can gather feedback from them early and often to avoid the risk of investing large amounts of time into a useless solution.
  • Ensuring adoption and usage: Track the adoption of your solutions and have honest conversations about whether people are using them. A lack of usage is a failure, even if you have the perfect technical solution in place. You should always be willing to go back to the drawing board with your clients to build the right solution for their needs when the data shows that they’re not using what you’ve built.
  • Prioritizing speed: Consider the end user experience by building solutions that are highly responsive. People are used to using technology that responds instantly to them —long gone are the days of the 56K dial-up modem and acceptable 20-minute load times. Your data solutions should follow suit.
  • Ensuring security and stability: Implement all data-related solutions with security, privacy, and quality best-practices to mitigate risks. You don’t want your organization to end up on the news in the latest data breach, and you don’t want people within the organization making misinformed decisions stemming from poor data quality.
  • Scaling effectively: Purposefully model and design solutions that expect to support the growth of the organization. This means avoid creating “data dead ends”, duplicate work, or technical debt. An attention to design ensures that the system can handle increasing amounts of data, increasing user demand, and inevitable changing requirements as the organization grows, enabling the business to achieve objectives and drive tangible results.
  • Paying attention to aesthetics and cleanliness: Pay attention to the little details that can make the biggest impact on your professional product. The marketing team has branding guidelines—make their day by asking them for it and then actually applying the concepts to any solution that will be used by humans—this includes proper hex codes for coloring, proper use of logos, consistent font usage, appropriate spacing.
  • Being able to explain your work: Create data solutions that the organization can sustain without your ongoing involvement. This means purposefully capturing knowledge about the requirements, the solution, and anticipated frequently asked questions in an accessible repository. Plan for your own eventual promotion by setting up your successor for success with proper and appropriate levels of documentation.

Applying Core Data and Analytics Principles in Practice

It’s not enough to have principles in place—you need to apply them in practice in all the ways you approach a project. “The key is to go into every project with an understanding of our client’s business objectives and long-term sustainability in mind,” says David Fussichen, CEO of Analytics8.

No matter how advanced technology gets and no matter how many platforms hit the market, you will always need people who have experience and expertise to make the connections between data and the business. The hard work comes in the form of data strategy, data modeling, and data accessibility. “You cannot get away from doing the hard work,” Fussichen says. “You cannot take shortcuts if you want sustainable results. There is no easy button for this work.”

The foundational practices that should be applied to every project include:

Orange cubed box that says, "foundational practices" lays above three grey boxes. Words in grey boxes: data strategy, data modeling, data accessibility. Blue check marks to their side. CEO quote below the grey boxes.

Foundational practices for every data initiative should include data strategy, data modeling, and data accessibility—it’s the hard work that needs to be done.

Data Strategy

Without a data strategy, all efforts in a project are based on guesswork. And it doesn’t matter what point of a project you’re at—asking the “why” behind any data initiative will better allow alignment with business objectives and the ability to connect to the needs of the business.

A data strategy is the foundation to all your data practices and aims to establish how an organization can utilize data to inform decision-making. Additionally, it allows you to create a data strategy roadmap that integrates the roles of people, processes, and technology and to use data to support and drive those business objectives.

Approaching a project strategically allows us to:

  • Consider the big picture and long-term goals
  • Ensure the work being done adds value to the business
  • Create a data solution that is feasible and can be implemented in a timely manner

“We do not start a project without knowing the why. It’s part of the process, but more importantly it is what allows us to build a data solution that not only meets our client’s expectations but takes it a few steps further with possibilities they didn’t consider. A data strategy is a foundational practice that allows us and our clients to explore what data can actually do for their business and build a plan that is focused on achieving those goals.”   – Christina Salmi, Managing Director

Data Modeling

If a data strategy is what answers the ‘why” and ensures your initiatives will meet business needs, data modeling is what defines the “how” by connecting the realities in an organization’s data with their ideal state of the solution design aimed at outcomes they want to achieve.

Data modeling is the hard work that is necessary to understand what data is available, how it relates to each other, how it needs to be transformed, and how it needs to be presented in order to make it useful for answering questions that solve real business problems.

Data modeling is foundational to every data project because it ensures that your data solution:

  • Produces correct information
  • Is oriented to the processes of your business to meet business demand
  • Is performant
  • Is extensible and as resilient as possible to changing requirements
  • Is easy to understand and use across the organization for various applications

“Data modeling is the foundation of any successful data project. It allows you to carefully plan and consider everything that is needed to achieve your desired outcome with the data solution—reducing costs and risks while increasing the chances of getting it right the first time. Without data modeling, you are doomed to repeat yourself and revisit your work when changing requirements inevitably occur.” – Tony Dahlager, Managing Director

Data Accessibility

People deserve to be informed, and not operate in information siloes. Data should be accessible to people in all roles in all organizations—appropriately and securely—but no person should be left wondering if they considered all the information available before making a decision.

Data accessibility involves four practices: Ensuring all core data assets are available and accessible for reporting; building effective mechanisms to make data insights accessible, usable, and engaging; developing data solutions that makes data actionable for users; and facilitating advanced analytics use cases for embedded, predictive, and prescriptive analytics.

Making data accessible is a foundational practice within every project ensures that analytics:

  • Align with the overall data strategy
  • Add value to the business while being fast, secure, and stable
  • Can scale as the company grows
  • Are aesthetically pleasing, ADA-compliant, and easy to understand

“Ensuring data accessibility is a timeless practice—it doesn’t matter what technology brings to the table, it’s an understanding of why it’s important and how to produce the outcome that will withstand the test of time. Without accessible data, the full potential of an organization’s most valuable asset cannot be realized, and the project may fail to meet its goals.” – Kevin Lobo, Managing Director


Talk to an expert about your data and analytics needs.

Unlocking the Secret to Success with Data and Analytics

Achieving success in the constantly evolving world of data and analytics can be a daunting task—but it’s not impossible. By applying the timeless principles and practices outlined in this blog, businesses can not only meet their current objectives and goals, but also navigate the chaos of the industry and build future-proof solutions. As Fussichen puts it, the real measure of success is being able to ask, “what else is possible?” and continually unlocking the full potential of data to drive innovation and growth.

Don’t let the constant influx of new technologies and tools distract you from the importance of building a solid foundation and creating a sustainable, data-driven culture within your organization. These principles and practices form the backbone of a successful data and analytics strategy and are crucial for driving long-term success and growth. With a strong foundation and a commitment to data-driven decision making, you can achieve your business objectives and reach new heights of success with data and analytics.

Sharon Rehana Sharon Rehana is the content manager at Analytics8 with experience in creating content across multiple industries. She found a home in data and analytics because that’s where storytelling always begins.
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