On Data Product Strategy. Q&A with Anthony Deighton
- How and when do you know if there is more value out of an organization’s data?
In all my history and experience working organizations of any size I have yet to meet one which doesn’t believe there is a huge opportunity to improve the value they get out of their data. Most organizations’ data is messy, incomplete, incorrect and managed in data silos. Typically organizations manage their data in a way which aligns to how they themselves are organized – by product line, by geography, by business unit, etc. BUT, real value from data comes when organizations break down those silos and see their organization as a whole (often for the first time).
- What is a data product strategy?
A data product strategy brings structure to the ownership, processes, and technology needed to ensure an organization has clean, curated, continuously-updated data for downstream consumption. Data product strategies define key objectives and metrics, such as increasing competitiveness by improving the customer experience or creating product differentiation and delivering value by helping companies to drive growth, save money, and reduce risks. Companies implement data product strategies by designing and using a data product. - Do you have any guidelines to share on how to possibly implement such a data product strategy?
We recommend five simple steps to get you started with a data product strategy:
- Define your objective: Do you have a clear idea of why you are creating a data product strategy? If not, it’s important to define your objective as a starting point. Without a clear understanding of the purpose behind your strategy, it may be challenging to make progress and achieve your goals.
- Assess your data, your organization, and your technology: Determine if you have the right capabilities in place to implement a data product strategy.
- Define your objective: Do you have a clear idea of why you are creating a data product strategy? If not, it’s important to define your objective as a starting point. Without a clear understanding of the purpose behind your strategy, it may be challenging to make progress and achieve your goals.
On the data side, it’s important to understand the following:
- Where does the data live?
- Is it integrated across systems and departments?
- Is the data accurate and complete?
- How often is it updated?
Understanding the answers to these questions will help you determine the quality of your data, how much budget you need, and the number and types of resources it will take to build a high-quality data product.
It’s also important to assess your organization. For example, knowing the skill sets and identifying any gaps that need to be filled. One key role that is necessary is a data product manager who can oversee the design, development, and management of a cross-functional data platform for internal and/or external customers. It’s crucial to identify any resource gaps and create a plan for filling them.
In addition to assessing your organization, it’s crucial to also have the necessary technology to execute a successful data product strategy. Your technology should enable you to easily master your data, enrich it with external datasets, and integrate it across your systems and departments. It should blend pre-built machine learning models with human feedback so that your data product delivers the best possible version of data. Without these capabilities, you’ll struggle to deliver analytical insights or drive operational efficiencies.
- How do you take into account the various application domains when defining such strategy?
On the one hand, good data products are usually aligned to a domain-specific universal schema. When you align data products to a domain-specific, universal schema, you benefit by providing a common frame of reference for your messy source data that aligns with your organization’s common understanding of the entity. For example, your organization may require that all customer records have a name and a social security number; On the other hand, machine learning can help map disparate sources into one unified schema, reducing the need to manually map them and reducing the risk of errors.
- Once we know how to define a data product, what are the next steps?
The next step is to build your data product strategy — please refer to the response in question 2, outlining the blueprint.
- Specifically how do you assess the data, the organisation, and the technology for a given use case?
Please refer to the response in question 3.
- What is customer mastering? and how does it relate (if any) to a data product strategy?
To improve the customer experience, it’s important to have a complete understanding of your customers, which requires a holistic view of their data. Customer mastering enables organizations to realize the full potential of their customer data. It’s a turn-key, templated data product that accelerates time to value by combining optimized machine learning, a low-code, no-code environment, and integrated data enrichment, enabling organizations to have the clean, curated, continuously-updated data they need to gain a holistic, 360-degree view into their customers and deliver the experience they expect. - You offer a so called templated data product. What is it ? And how can it help with customer mastering and/or data product strategy?
Tamr provides integrated, turn-key data product templates to help organizaitons implement a data product strategy quickly. Tamr’s data product templates combine a unified schema, data enrichment sources (such as D&B or BoldData), data quality services, a portal for data stewardship, and data publishing service for populating downstream systems and warehouses. With Tamr’s data product templates, you can deliver a consumption-ready set of comprehensive, clean, curated, and continuously-updated data sets quickly and easily. - Who is using the Tamr Platform and for what kind of applications?
Tamr’s customers have found great success in using data product templates to accelerate time to value for data products such as B2B Customer Mastering, B2C Customer Mastering, Market Data Linkage, Healthcare Provider Mastering and Supplier Mastering. By leveraging Tamr’s date product templates, our customers are able to drive efficiencies, save money, and increase opportunities and revenue.
Resources
Tamr’s e-book, Getting Started with Data Products: A CDO’s Guide, is an invaluable resource for data leaders looking to implement a data product strategy. This blueprint provides a comprehensive overview of the key capabilities required for a successful data product, as well as a helpful checklist to guide you through the five-step process of developing a data product strategy. With real-world case studies of how Tamr’s customers have leveraged data product templates to drive business impact, this e-book is an essential tool for any organization looking to unlock the full potential of their data.
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Anthony Deighton
Anthony Deighton, GM, Data Products, Tamr Inc.
Anthony has over two decades of experience building and scaling enterprise software companies. Before joining Tamr as chief product officer, he served as the chief marketing officer at Celonis. He established the company’s leadership in the Process Mining software category and created demand generation programs resulting in 130% ARR growth. Before Celonis, Anthony worked at Qlik for over ten years, growing it from an unknown Swedish software company to a public organization. He was an instrumental market leader, with roles that entailed product leadership and marketing before his promotion to CTO. Anthony began his career at Siebel Systems, learning to build enterprise software companies in various product roles.