Data product development anti-fail guide: 10 big NOs to watch out for

Dr. Gleb Basalyga

PhD, Senior Machine Learning Engineer at Modsen

The recent mindboggling AI-powered changes turned the collocation “data product” into a major buzzword, and the data industry – into one of the fastest-growing domains, getting a 62% market value boost in just 6 years. Users seek data-driven products, businesses invest heavily in building them. But are there any clouds on the horizon for companies embarking on the process of developing data solutions? Uncovering the major don’ts of the data product development journey here in this article.

What are data products?

Off the top of one’s head, the answer might be “all products using data to some extent”. However, that’s not quite true. Data products are applications, tools, or systems that leverage data to deliver insights, functionalities, or services. These products are designed to collect, process, analyze, and present data in a way that creates value for users. Non-data products, on the other hand, are those, whose primary function is not directly related to data, although they may use data as a component of their functionality, and they would still be valuable without data components.

10 data product development mistakes to consider

Once you’ve detected the need for a data software product, there comes a long list of decisions to make. Where to start? Whom to entrust the project? What should be put into focus? To help you navigate through these and other questions, here is a 10-step checklist of data product-related pitfalls to avoid during the journey ahead.

#1 Stating vague product objectives

The trend for data-powered software often makes business owners feel they are in a race to build something with “data” in it and make it fast. The lack of clearly defined objectives hinders the whole subsequent process as product goals are pillars for both strategic and situational decision-making.

Implications

A data product without precise objectives is destined to fail at one point or another. When a project becomes directionless, it’s difficult to measure success or make decisions that align with business needs.

Our solution

At Modsen, we don’t proceed with development if at the stage of requirements gathering and analysis there are any issues, and vague product objectives are one of them. In such a case, we help business owners state a problem and determine a set of product goals we could work with, otherwise months-long efforts of the team and client resources would be spent in vain.

#2 Overlooking data quality

When it comes to data products, the quality of the data you use accounts for 100% of your project success. Imagine you were to cook a meal for a dinner party, but your kitchen is a mess: the ingredients are scattered all over the place, vegetables are unwashed, and there are expired items mixed in with the fresh ones. Even the best chef wouldn’t be able to prepare a gourmet dish given such initial conditions. Same with data products. No clean quality data – no successful project whatsoever.

Implications

The key value of data products lies in deriving insights and helping users with reasoning and decision-making. Unless the information within the system is correct, no output is suitable for use. Even if a faulty data product is launched, it won’t last, as users will lose their trust in it after a session or two.

Our solution

Depending on the complexity and scale of a data project under development, data cleanup could take months. But that’s the only way it can be done. Data products are unique and so are the processes behind their building. To ensure top-notch data quality, our in-house data experts implement checks and validation, clean and preprocess data thoroughly to ensure its accuracy and reliability before it is used for analysis or integrated into the data product.

#3 Not putting yourself in the user’s shoes

Every product out there is built for the user, hence user’s needs and convenience come first in the list of priorities. Focusing on technical aspects while neglecting to consider the end-user’s requirements will adversely affect the data product, whatever sophisticated it may be.

Implications

The data product may be technically sound but fail to deliver value or be user-friendly, leading to low adoption and dissatisfaction, which will inevitably result in a business failure unless user-centric adjustments are made.

Our solution

At Modsen, we root for engaging with users throughout the development process through internal testings or beta testing groups. Our in-house Project Managers and UI/UX experts gather feedback, conduct research and analysis, and iterate on the product design to ensure it meets user needs and expectations.

Got any questions about data product development?

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Dr. Gleb Basalyga, PhD, Senior Machine Learning Engineer at Modsen

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#4 Not prioritizing data privacy and security enough

The data security issue is twofold. On the one hand, you must ensure there’s no chance for sensitive user data to leak, on the other hand, a data product itself should convey a strong idea of being safe and trustworthy.

Implications

Poor data security measures result in data breaches and unauthorized access, compromising user privacy, and leading to legal consequences and damage to the company’s reputation.

Our solution

Stringent, regularly updated security policy and breach-proof in-house software development processes of our own, allow us to share the best data protection practices with our partners. When it comes to the security of the data products we build, encryption, access controls, regular security audits, and compliance with data protection laws are in place to avert any unauthorized access attempts.

Modsen Design Studio team makes sure to convey the manifestations of internal product security and stability through navigable, solid, and clear-cut UI/UX.

#5 Aiming over your head

In business, ambition is important, but realistic ambition is vital. The range of data products is vast, from dashboards to complex machine learning predictive systems, and if you’re relying on your in-house team for data project development, setting your sight too high might be a bad idea. Start with something smaller and simpler, especially if you’re pressed on time. A properly built simple data product can be scaled into a more complex solution while a huge malfunctioning system crafted under time pressure and with expertise gaps will lead you nowhere.

Implications

Solution overcomplication results in overly increased development time and costs, product security breaches, and a lack of focus on user experience which leads to low adoption rates.

Our solution

We’ve gathered seasoned data experts under our roof so that any of your ambitious data product ideas could be implemented properly and drive tangible business results. Whether it’s a complex ML/AI data solution or a smaller data-powered product, we keep the design simple and focus on core functionalities, prioritizing usability and maintainability, avoiding unnecessary features that complicate the user experience.

#6 Not iterating on user feedback

A rare product can be built and maintained great without listening to end-user feedback and making the necessary adjustments. Developing a data product in isolation, without regularly incorporating user and stakeholder feedback is a ticking bomb.

Implications

The absence of iterative data product development and updates will at some point result in a lack of critical features and solution outdating, which is unacceptable in the case of data-powered applications.

Our solution

Agile project development approach we adhere to includes regular feedback loops, allowing us to collect and analyse user feedback and performance data to continuously improve the product.

#7 Ignoring proper data governance measures

When building data products, data management aspects might seem of secondary importance, while in fact, they are central. Neglecting to implement policies and practices for managing data used within the system is a potentially costly mistake.

Implications

Toughening of data regulations and failure to focus on them can lead to data privacy issues, security breaches, and deregulations, resulting in legal and financial penalties, once they are discovered.

Our solution

At Modsen, we prioritize establishing strong data governance frameworks and consult our partners on managing them. Our data and security experts implement data access controls, compliance checks, and regular audits to ensure that all data handling practices adhere to relevant regulations and standards.

#8 Underestimating data integration challenges

At the beginning of this article, we’ve mentioned that data is a mess. 147 zettabytes of globally created data present a tangle of structured, unstructured, meta, master, machine data, and other information types that don’t merge smoothly with one another.

Implications

Not deep enough understanding of data integration issues result in data inconsistencies, duplications, and errors, which can undermine the reliability of the data product.

Our solution

Working with data products, we utilize robust data integration tools and plan for the complexities and pitfalls involved. Ensuring data compatibility and establishing protocols for handling discrepancies is our approach to mitigating data integration issues.

#9 Not thinking about scalability in advance

329 million terabytes of data are generated daily, which means that data-powered products have to be built at scale to be able to handle the ever-growing information volumes and user numbers.

Implications

As the data product grows while having trouble scaling, performance issues arise, including slow response times and system crashes, hindering the product’s usability and reliability.

Our solution

All software products we build are designed with agility in mind. Flexible and scalable architecture of data solutions allows our partners to grow seamlessly, being sure that performance metrics will remain stable under increased loads.

#10 Starting off with wrong steps

Data products need to be developed differently. With non-data solutions, you can begin with product modeling, feature engineering, or infrastructure construction. When approaching data products, you have to think of performance evaluation tools first.

Implications

Without performance evaluation frameworks ready from the outset, the objectives of the data product may become misaligned with business goals, leading to a product that does not effectively solve the intended problem, causing delays and increased project development costs.

Our solution

Having built several successfully operating data products, we know exactly what to start with. Before launching the development process, together with stakeholders we establish clear metrics for performance evaluation and determine what success looks like for the data product in measurable terms and what tools are required to evaluate them.

The final thought

Quality data products are the driving force behind effective digital business transformation. Striving to build leading-edge data solutions, stakeholders often overlook or underestimate the obstacles associated with the endeavor, resulting in failed or semi-efficient data products and wasted resources. Entrust complex data software engineering tasks to seasoned professionals who won’t let any process pitfalls stand in the way of your success.