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Data management is critical to every organization's success, and many companies call themselves data-driven. Yet too often, their data and the teams that manage it are siloed from the rest of the organization, and the quality of the solutions they implement suffers as a result. Instead of being able to focus on long-term initiatives that would help refine and optimize interdepartmental processes, data professionals are stuck addressing problems as they arise with stop-gap fixes. These ad-hoc data tools may suffice in the early stages of an organization's growth, but they lack the scalability that an enterprise requires to remain competitive and generate ongoing value to stakeholders.

The solutions that scale more effectively involve more integrated data teams coupled with an information-driven approach to data products. The first step is developing a centralized data center to which each department is connected through dedicated data business partners, followed by outlining strong data governance and standardization principles that will help align the entire organization's data initiatives. This level of centralization ensures data is more accessible, accurate, and contextual so that data teams have a more informative picture of each problem before development begins.

Many growing enterprises are held back by data teams that are understaffed and under-resourced, where just starting development on multifunctional data products is impossible. But the problem isn't just about funding or talent shortages; across industries, data teams are swamped. They are so busy reacting to the latest urgent demands for dashboards and charts that deeper initiatives never get off the ground, even if there are skilled employees to take them on.

This has led more organizations to shift their data management approach, not only providing more funding and recruitment resources to their data teams, but also building new processes that allow data tools to be built and deployed more effectively. Data products come with their own life cycles, development stages, and value-driving opportunities, and data teams are being repositioned to ensure that these products are managed just as meticulously as any other product offering would be.

Defining the value of enterprise data products

In order to implement a robust and scalable data product management process, there needs to be organizational understanding that building good solutions takes time. This means discouraging departments from flooding their data teams with requests for tools that only serve short-term goals, and instead encouraging deeper conversations about the overall problems at play in a particular business domain. This enables data teams to develop data products that create lasting improvements.

At this stage, data product managers serve a critical gatekeeping role to ensure that each new data product targets a specific user segment and comes with a unique value proposition. When teams pitch products, it should be clear what goals the products are meant to achieve, who they must deliver for, and why building them from scratch will be worth it. Not every problem that requires a data solution is expansive enough to warrant building entirely new data products. For some, improving upon existing products or using third-party vendors will offer a better return on investment.

When data teams have been operating on a reactionary basis for years and attempt to implement a more proactive approach, they can encounter departmental pushback—especially from teams that frequently send in urgent requests for data tools. It would be unrealistic to expect these requests to stop overnight, and not all of them can be ignored. With a fully resourced data team, however, some data analysts can be designated to act as real-time support representatives while other initiatives build momentum. Because these team members are already on the ground listening to individual employee concerns, they're positioned to become advocates for deeper data projects that can help each department work more efficiently.

Ensuring data product integrity

For organizations managing multiple data teams or trying to release multiple data products simultaneously, data quality is paramount. A study by SnapLogic in 2020 found that "77% of IT decision makers don't completely trust the data in their organization for accurate or timely decision making." Solutions built on this bad data end up being resource-wasters for an organization, and even the best data governance and cleaning methods are not enough to transform bad data into verifiable, trustworthy insights. This requires a robust data enrichment process that can evolve and scale at the enterprise level. Tools that offer ML-based enrichment functions like sentiment analyses, keyword extractions, knowledge graphing, and language detection can all help verify data through added context.

Data engineers are the experts in fielding these quality questions, but when organizations make cross-training a priority, they can ensure that their engineers also understand the data coming in through different business domains. This allows teams to transform datasets faster, recognize gaps or errors, and make targeted requests when additional data is needed. Data UX researchers are another important data team resource that can be deployed to spearhead data literacy initiatives throughout the organization. Regardless of whether a data product is customer-facing by traditional definitions, ensuring that all employees are educated on how to read, interpret, and interface with the organization's data will increase teams' capacity to close quality gaps themselves.

Modeling and evaluating a data MVP

If MVPs are essential for the agile development of traditional products, then they should be regarded as essential for data products as well. Given the sheer amount of data that is available to enterprises, it's easy for data teams to get carried away with multiple possibilities and lose focus on the main objective. Enforcing an MVP approach to data product management keeps that focus and allows more effective data solutions to reach end users faster.

Organizations focusing on customer-facing data products rely on this faster time to market in order to leverage timely user insights and stay competitive with their offerings. While competition may not be an issue for internal data products, these products are also very fruitful places to implement the MVP model because the target market—the internal workforce—is able to easily give real-time feedback without much risk to the customer-facing operations of the business. When these internal end users are not given opportunities to provide insights on an MVP, the potential usefulness and efficiency of the final data product suffers. Over time, as suboptimal data products are deployed across departments, their inefficiency can slow down internal operations and damage the organization's competitive advantage in the market as well.

Instead of sacrificing modeling and evaluation in the name of delivering solutions faster, thriving organizations drive a culture of continuous assessment. Models can reveal the potential MVPs of every data product that the organization is working on, making it possible to narrow down which iteration of the product's design drives the most value and the best ROI. When a data team has UX researchers ready to interact with internal users, it's able to gauge how well those models are delivering on their promise of relevant, useful, and timely insights.

Launching data products: an org-wide effort

Commonly, data teams are separated from many core enterprise functions, but the data product management approach demands cross-department collaboration to bring valuable data solutions to market. Customer-facing data products often have the most direct impact on an organization's bottom line, making them appear to be the most urgent areas for collaboration. However, internal data products also require input from multiple departments, from design and web development to finance and HR, in order to deliver optimal results. These products' stakeholders include not only the internal users who will actually be receiving the product, but also any adjacent teams that might benefit from the infrastructure used for the product. The onus on leadership, rather than relying on these disparate parts of the organization to incorporate data teams themselves and participate in the data product process, is to ensure that data teams are integrated throughout the organization and positioned to both collect from and deliver to the company's rank and file.

Increasingly, enterprises have been creating space for a new C-suite role to facilitate this integration: the Chief Data Officer, or CDO. Among other areas like data governance and analytics, CDOs are focused on creating data strategies that prioritize innovation, connect more deeply with the core operations of a business, and boost overall revenue. This is just one sign that data product management processes are set to grow in scale and popularity in coming years. To capitalize on this growth, enterprises must embrace the product development model for their data and empower data teams with a product management mindset. Only when these teams are delivering internal data products that drive value as consistently as external offerings can enterprises truly call themselves data-driven.

Zoho offers a suite of intelligent enterprise business software, including an award-winning CRM suite, the industry's only comprehensive analytics and BI platform, and a powerful low-code development ecosystem.