A Complete Guide to Data Products
This essential guide to data products provides valuable insights into: what they are, the key capabilities, examples, and key requirements. We've also included a handy data-product checklist and highlighted the advantages of leveraging data products.
At the core of any successful business is the utilization of good data. It’s the factual insights that can be drawn from this digitally stored information which, allow a company to make measured and accurate decisions. In order to be successful in this pursuit, it’s pivotal that the way in which you interact with data is cohesive, reusable, and ensures quality and accuracy at every touchpoint.
In order for this to be achieved, many enterprises turn to data products, which have been carefully crafted with their exact needs in mind. But what exactly is a data product? In this guide, we’ll assess what constitutes a successful data product, the key capabilities they possess, the data product lifecycle, advantages of using one, and how to create one.
What is a data product?
A data product is a packaged solution, which has been created with the end goal of meeting the needs of its users. It can serve a multitude of purposes, with the core values of turning raw data into actionable and insightful information that an enterprise can use to their advantage. A strong data product makes processing data simple, while also offering a discoverable, interoperable, and clearly-owned approach to managing and utilizing data.
It’s important to note that a data product is different from data as a product. The former is a rounded solution, which is used to make the processing of data easier. The latter is that same solution but sold to an external third party for use within their own network. The easy way to remember the difference is that:
A data product
is owned by a company
Data as a product
has been purchased by a third-party
Any good data product will be:
Discoverable and interoperable – using machine interfaces to support real-time operations
Comprehensive and consistent – aligned to a domain-specific universal schema, with enriched external data sources
Clean, accurate, and reliable – utilizing strong data control and monitoring systems, with data validated and cleaned based on global best practice standards
Continuously updated – to ensure a flexible and evergreen approach to data management
What are the key capabilities of a data product?
Data products can exist for a variety of reasons. Some may even offer more than one function as part of their overall capabilities. Some of the most common functions of a data product are:
Discovering insights
Data serves to drive a company forward and make tangible financial changes to your bottom line. A data product facilitates the ability to do this with the use of an internal catalog or dataset that can be drawn upon at any time. This includes factors like predictive analytics or seasonal trends.
Preserving data quality
A data product centralizes information from a variety of sources and harmonizes it to ensure data quality is at the forefront of everything that a company does. Most modern data products are able to detect and fix these anomalies at their source, ensuring that all information being utilized is as effective as possible for growth.
Security procedures
Data privacy standards need to be adhered to in order to ensure the safeguarding of sensitive personal information. A data product is able to ensure that with the use of a two-fold approach to data security. The key is ensuring that access to this sensitive information is only granted to the right people, while also employing dynamic access. The latter involves an administrator or data officer guaranteeing that permissions are in place to limit both who can access data and how they are able to.
Automation
In the modern digital age, artificial intelligence (AI) is continuing to play a role in how companies operate. An agile or flexible approach to data utilization is made possible with data products that rely on automated support to flag anomalies, or even execute data-driven decisions independently.
Organizational requirements for a good data product
In order for a data product to be crafted and executed to its full potential, an organization needs to be in a position to effectively manage and monitor its use – as well as being able to advertise their product to data consumers. It’s important to utilize change management at a procedural level to ensure a business is best positioned for managing the requirements of a data product lifecycle.
Some changes that could be implemented include:
Introduce a dedicated team
As any successful leader of business will tell you, it’s vital to have the right people in the right positions in order to flourish. The creation, promotion, and ongoing management of any solid data product should be spearheaded by a team of skilled professionals who are experts in their field.
This team need to possess diverse skillsets. While accruing a squad of data experts might sound like a good strategy, it could inadvertently neglect other key factors such as business domain knowledge or product delivery skills. That said, technical knowledge (such as data analytics and engineering) should definitely be at the core of any team you assemble.
A data product delivery platform
Your data product won’t have the widespread impact you want if nobody is able to readily access it. A delivery platform enables data consumers to browse for products you might be offering, as well as request access to them. This platform needs to be user-friendly, and should include vital information such as data privacy standards, key data quality procedures, product-limitations, and any other relevant documentation.
Clearly established data governance
It’s imperative that an organization can safely and securely protect the personal data of both themselves and their consumers. Most commonly, this approach will be automated via data masking, encryption, dynamic access, cleansing, profiling, validation, and even audits.
Data governance should be built into any product. And while automated security is effective to a point, it’s important to have dedicated team members to work alongside this. Good data governance begins with your own internal team. Ideally, someone within your organization should be in charge of this role.
Begin with small-scale case studies
Championing the use of data products might seem obvious to you, but it could be a challenge convincing stakeholders to allow this kind of investment. If you find yourself in this situation, begin by targeting high-impact use cases within your own organization.
Emphasizing the value to a business of introducing a tailored data product encourages ongoing collaboration between you and the stakeholders. Most often, this kind of experimental and measured approach to data product development highlights unexpected understandings and requirements for how a business might progress in the future.
Gain control of your data
Examples of data products
In order to best appreciate the effectiveness and purpose of a data product, it might help to understand what they look like in action. While these packaged solutions can take many forms, use these basic examples as guidance for bettering your understanding:
Data APIs
These products allow for a seamless interchange of data between different applications and systems. A common example of this is Google Maps. Google collects and stores data on the world’s roads, then allows developers to access this data to create their own maps and applications. Utilizing a single view of data is also a powerful tool here. An API makes it possible to pull in data from a variety of sources, consolidating them and making it possible to view a singular representation for each individual customer.
Recommendation engines
These platforms provide personal recommendations based on user behavior and preferences. This is done via a system of algorithms, with the aim of providing a streamlined and efficient service for the user. These draw on a number of datasets to present a tailored recommendation for a user. Tripadvisor does this when recommending hotels or restaurants.
Real-time dashboards
These visual tools provide real-time updates for companies looking to make measured decisions. Common features of these kinds of dashboards include things like customer segmentation tools, predictive models, recommendation systems, fraud detection algorithms, and chatbots or virtual assistants.
Predictive analytics tools
An incredibly valuable asset for any company, predictive analytics allow you to forecast and make actionable decisions for the future. Trends can be identified early, maximizing business processes and ensuring the right steps are taken to manage customer behavior.
Personal finance insights
These data products ensure a business makes smart decisions about the management of their money. A good example here is YNAB (You Need a Budget). This system allows users to track income and expenses, assigning a budget. Other ways you can gain financial insight with a data product include feeding data into predictive algorithms, having it automatically identify data patterns, and using dashboards to easily visualize data.
The data product life cycle – how to create a data product
For enterprises interested in creating a data product – or wanting to work with a trusted professional to do so – it’s important to understand the life cycle of these packaged solutions. While each individual product will pose unique opportunities and solutions, the general beat of a product’s life cycle can be understood as follows:
- Problem definition.
A data product is likely to only be created if there’s a genuine need for it. If you’ve noticed that there are areas of your enterprise you want to improve, this should serve as the core goal of a product. This could be increased risk awareness for preventing fraud, or something like more efficient customer behavior analysis.
- Target audience identification.
Once you know what the purpose of your product is, the next step is identifying who you’ll target with it. This could be customers if you’re looking to increase revenue, other businesses if you want to sell your product to them, or even stakeholders. Knowing who you’re targeting helps to focus your efforts.
- Accurate data selection.
As with most business decisions, the quality and accuracy of your data will heavily impact your product. Your dataset needs to be large enough to train your model, but not so big that it leads to delays. Perhaps most importantly, it needs to be relevant to what you’re trying to achieve.
- Cleaning and preparing data.
Once your data is selected, it needs to be polished. You can clean and prepare data by removing duplicate or irrelevant data, as well as adding anything that’s missing. Once this has been done it can be sorted into training and testing sets.
- Model design and build.
This stage will be determined by what you’re looking to achieve with your product. You could build artificial neural networks, decision trees, or support vector machines as part of your design stage. It all comes down to what you want to achieve with your data product.
- Training and evaluation.
Once the model is built you’ll need to test and train it using the data you put aside for this. This will help to automatically update the model’s parameters in order to ensure it makes accurate predictions. Compare the predictions made by the model to the testing data to assess if it’s successful or not.
- Deployment.
If you find that your product meets the standards required, it’s time to release it to the wider world. That means integrating it with a web application, mobile app, or other digital system. User experience should be a priority at this point, with a focus on ensuring it’s easy for your target audience to interact with.
- Ongoing monitoring.
Once live, a data product shouldn’t be forgotten about. Ongoing monitoring and maintenance needs to be carried out to ensure that it continues to perform at the level required. That means continued accuracy and valuable insight generation.
A data product checklist
If you want to optimize your data product production there are a handful of key questions and considerations to make during the creation stage. A successful product will be achieved when the following factors are kept in mind.
What is the purpose of the data product?
A product needs a clearly defined goal. Having this allows for a targeted and streamlined approach, which in turn makes the chances of success considerably higher. It’s also important to know exactly who your target audience is (customers, internal employees, other businesses, stakeholders, etc.), as well as what their purpose for using the data product will be.
What data will be included as part of your product?
Knowing what will be included is also of utmost importance. In this instance, this refers to the type of data you’ll be using, the source of this data, and how it’s going to be cleaned to be both accurate and relevant. It’s important to have your datasets prepared when tackling a project like this, as this will be the crux of the entire product.
How is data going to be visualized within the product?
The way in which someone draws valuable insight from a product also needs to be taken into account. This doesn’t only need to take aesthetic value into account, but also user experience. For someone to be able to interact with data successfully, they need to be able to understand what it’s telling them. One way to ensure this aim is always achieved is by introducing a visualization tool that automatically pulls out key information for the user.
How will the data product become available?
If your end goal is to sell your data as a product, you’ll also need to understand what makes it engaging and interactive for your target data consumers. Perhaps most importantly, here you need to consider what kinds of features you’re going to include within it. There also needs to be a clear and tangible way for anyone using the product to measure key metrics.
Advantages of data products
Data products have a wide variety of advantages for any company that chooses to create one. This interactive and streamlined way of interacting with datasets can be transformative for any organization. But what are these benefits?
Designed to meet demand
At their very core, most data products are designed to find a solution to a problem, or accentuate an already successful aspect of a company. A product of this nature helps to focus attention where it’s needed most, delivering detailed insight that helps its users make actionable decisions.
Supported data federation
Data federation is the process of mapping out several databases to one centralized platform. This is possible with the use of a data product that allows for multiple teams within one organization to interact with the same datasets. How much freedom is given to each department to process data can be customized in accordance with business requirements.
Enhanced efficiency
Time is money – and that’s perhaps no truer than when working with data that can provide tangible impacts to your bottom line. Interaction between internal teams, heightened autonomy, machine learning and task automation, and real-time data insights make it possible for your output to increase without the need for additional input.
Reduce overhead costs
By making data accessible to less technologically-minded users, key business decisions can be made without the need for expensive IT solutions. Putting readily accessible data insights in the hands of everyone in a business means specialists are not a requirement, which saves thousands on a monthly basis.
Heightened agility
Modern business calls for a flexible and versatile approach. With real-time insights that are provided by both automated and human workers, companies can act in the moment to make critical decisions on seasonal or trending factors. A data product negates the need to spend extensive time studying what the numbers show you – with clear answers already laid out in a matter of seconds.
If you feel like you need further support with the creation or adoption of a data product, make sure to reach out to Quantexa today. We specialize in ensuring you use the right data to make the right decisions to drive your enterprise forward.
Useful links
We’ve discussed a lot in this guide, but there might still be more you want to discover about data products. Browse these handy sources to learn more.