Data science is increasingly revolutionizing the business world. Companies across industries are leveraging data for strategic decision-making. But it’s not enough to collect vast amounts of data; organizations also need to put themselves in the best position to interpret it intelligently. That is where data products come in.

Rapidly changing customer behavior has intensified companies’ need for high-quality data. The 2021 Global Data Management Benchmark Report from Experian found that 84% of data professionals and business leaders saw a growing demand for data insights in their organizations due to COVID-19. The rapid shift towards digital transformation during the pandemic also made 72% of professionals say they are increasingly data-dependent.

Data and data products play a larger role in corporations’ long-term decision-making than ever before. Still, any plan to overhaul an organization’s culture to become more data-driven should be carefully implemented. Being able to leverage data is not just a question of having the technical infrastructure. The company also needs to have a “data culture” that will embrace these more analytical processes.

Ensuring Buy-In of New Data Products

Data products can break down information silos throughout your organization, allowing quick and easy access to insights. This is a key step in improving overall data quality, as it becomes easier to collect, analyze, and find contradictions in strategic information.

However, to implement data products, you need a willingness throughout the organization to transform. Before you begin development, ask colleagues in each department about their goals and figure out how advanced analytic solutions can uniquely benefit them. That way, you can ensure that the data product will prove useful for these professionals.

Building user-intuitive solutions that target the needs of your organization is crucial for a high ROI and buy-in throughout the company. To do this, your team will need to shift from a data project to a data product mindset.

Project Vs. Product Mindset

What is the difference between a project and a product? A project is a temporary endeavor with a clear beginning, middle, and end. There is a singular goal in mind, and once achieved, the team goes on to work on other projects.

Meanwhile, a product is an ongoing physical or virtual item that continually meets the needs of its users. As your customers’ or internal staff’s needs change, teams will add new features to a product or adapt it into new forms. Unlike a project, a product is always adapting to emerging circumstances.

Projects and products require two very different mindsets. When working on a project, a team’s approach is to meet previously agreed-upon benchmarks. By the time a team finishes work on a project, the company’s needs have often changed so much that the original goals make little sense.

A product mindset is more fluid and pragmatic, always adapting to changes in the industry, company, and users’ demands. A team working on a product knows what will show value in the short term. But they also know that continuous modernization will add more long-term benefits to the company’s bottom line, brand reputation, and efficiency in the years to come.

Benefits of Data Products

Data products give organizations the insights they need to target inefficiencies in their operations and better captivate potential customers. Teams equipped with advanced analytics can pivot with agility to meet changing market needs.

Let’s look at an example: a retail company has an online platform that serves as a catalog, order management, payment, and fulfillment hub. If the retail company does not have the data products necessary to dynamically collect, track, and address feedback about the platform, the user experience will devolve. Customers will never see an improved experience, and the application will never run as efficiently as possible, increasing the total cost of ownership.

On the other hand, the retail company could have a real-time tracking system that quickly structures raw data from customers. Then, the system can send all applicable feedback to the right teams to fix and modernize the retail platform. That way, customers see an enhanced experience quickly, and the total cost of ownership for the application gradually decreases.

Data Products and Preventing Brain Drain

In 2021, 64% of IT leaders said staffing shortages were the main obstacle preventing them from implementing new technologies. The Great Resignation has made employee retention difficult and company brain drain almost inevitable.\

Luckily, data products can also help reduce the number of lost insights when an employee leaves the team. By centralizing the company’s shared information in a repository, no single employee or team has exclusive access to key data points.

Instead of having siloed data, this proactive approach allows knowledge to spread throughout the company. Better yet, data products enable teams to collaborate more effectively together, improving the day-to-day experience for employees and possibly lowering the turnover rate.

Preparing to Build a Data Product

Advanced analytics solutions need to be paired with a data-driven culture to capture the most vital insights. Here are some steps your organization can take to get the most out of data products.

Step 1: Locate and Collect All Data
The first step is to find and store all company and customer information. Your organization will use this wide range of data, from customer contact information to sales and market research, for future analysis.

Step 2: Set Goals
Establish your organization’s priorities and which KPIs will most accurately track your progress. Then, let your teams know what these KPIs are, being sure to demonstrate how these data points offer a more substantive look at the company’s bottom line.

Step 3: Remove Unneeded Data Points
Irrelevant information can compromise the efficiency of your data products. Now that you know your company’s KPIs, filter out the information you know is not essential to business decisions. You can even leverage AI to proactively remove this information in the future from raw data.

Step 4: Employee Training
Invest in training that not only teaches employees how to use certain tools in data systems but also how to interpret the data and leverage it in decision-making.

Step 5: Promote Collaboration

Every department and employee in your organization will benefit from being more data-driven, not just technology teams. Training everyone in new data analytics methods encourages collaboration and accelerates your transformation into a data-savvy company. This also challenges data scientists and business analysts to present information in the most accessible and visually intuitive way possible.

Step 6: Take an Iterative Approach to Building Data Products

As we’ve discussed, the data analytics journey is a continuous process. No organization will ever have a perfect system, but your company can continually target new functionalities that will make you more efficient and prolific.

Final Thoughts

Organizations always want to be one step ahead of consumer demands and identify market inefficiencies that are slowing down their competitors. To get the necessary insights, companies need data products and a data-driven company culture from top to bottom. No matter where you are on your data analytics journey, you may benefit from consulting with a tech company with 30+ years of experience in making clients more insights rich. Contact us today to begin working with our experts.