In the previous articles within our data series, we looked at the current composition of Data Teams, as well as the energising effect of Big Data on Project Management. But what does the future of these teams and data projects look like? Looking ahead, how will the management and use of data interact with the wider business?
This article looks at the relatively nascent role of the Data Product Owner or what is now being referred to as the Data Owner. Already emerging in progressive businesses to head up data teams, the Data Owner is beginning to take a lead role in ensuring that datasets and data products are utilised by companies effectively and commercialised fully.
Why is Data a Product?
In 2022, McKinsey discussed unlocking the full value of data by managing it like a product, which this role will enable. They outlined the early data strategy approaches in the form of the ‘grassroots approach’ and the ‘big-bang’ strategy.
The ‘grassroots approach’ allowed individual teams to combine their data in the way that they needed. With no coordinated strategy across the business, this approach leads to significant duplication for similar data uses. While this system can still work for smaller organisations, as the same person handles the majority of data, typically it is a drain on time, energy, and cost.
The ‘bigbang’ strategy is what we see in large numbers of organisations today, with a centralised data team handling all company data en masse but with no clear mission and little interaction with overall business objectives. There has been a great deal of work within companies’ organisational development initiatives to promote horizontal data functions to work across organisations. This aims to tie data utilisation to the business’s strategic drivers.
Managing data like a product, McKinsey says, delivers a high-quality, ready-to-use dataset that people across an organisation can easily access and apply to different business challenges. This allows the organisation to have a complete view across a range of factors such as customers, sales, or energy usage, for example.
Why do we need Data Product Owners?
Data now guides most company strategies and acts as a true north for most roadmaps. A data strategy should identify key stakeholders capable of unlocking the true potential of a company’s data assets and who possess the capability to translate insights generated from the wider data team into commercial gain. These key players will play a crucial part in the utilisation of data.
Data Owners will champion the effective development and maintenance of datasets to ultimately commercialise them, and to ensure that datasets are accessible to the wider organisation to apply to wider business challenges.
Responsibilities of a Data Owner
Different from Data Strategy Managers, Data Owners combine data science components with classical product ownership.
As a role that is still being developed within teams, the remit of a Data Owner is continually developing. Currently, their main role is as an intermediary between business executives and data specialists. This, Nick Hotz at the Data Science Process Alliance states, can take many forms, namely:
Collaborating with all stakeholders to build the roadmap for the datasets they own.
Understanding the audience that will be interacting with the dataset to improve accessibility and utilisation.
Maintenance of the datasets through tracking KPIs.
Ensuring each dataset is secure and compliant.
In essence, Data Owners are the quarterbacks of data functions. They will be coordinating the function to ensure that datasets are enabling the organisation to progress, and developing ‘plays’ to achieve business objectives.
Managing data like a product
In order to manage data this way, it is important to have someone keenly focused on the direction of datasets, who possesses the technical capability and stakeholder management to communicate with both data teams and those overseeing central business strategy.
There are many benefits to taking an aligned view of data management. Having a consistent viewpoint strategy means that teams using data products and datasets don’t have to waste time reassessing data, searching for it, and reformatting and processing the datasets, which brings about compliance issues and an architectural mess.
Having a Data Owner to oversee the management of the datasets negates the need for repeat work as they have oversight of its previous uses and decide whether the business request fits the overall strategy.
McKinsey suggested that dedicated management and funding would facilitate the development of each dataset, which should be managed by a specific owner who has a team to explore new use cases and respond to business requirements from within the wider organisation.
Managing data in this way can also enable the commercialisation of datasets as there is a specific stakeholder whose primary role is to explore possible uses and to improve its accessibility.
Therefore, the development of the Data Owner is a natural progression to not only link the business needs to the dataset but also find extra commercial value in each dataset to be further distributed. Having a defined role that manages this development and monetisation can fast-track the use of data within the business, and scale the use of the organisation’s data.
Implementing this strategy
There are a number of ways that this strategy could be implemented and the work of the Data Owner is central to this aim. One of the key aspects of the role is to improve accessibility, which is done through a combination of User Experience (UX) and User Insight (UI) initiatives.
Treating datasets like a product enables greater accessibility to the wider organisation which allows for an enhanced experience of the data. In turn, this is positive for the achievement of business objectives. This work is critical to the accessibility of the data.
It also contributes to improved communication of analytics and business insights for those who do not possess deep technical capabilities but are decision-makers within the organisation. The development of these workstreams enables clearer and more effective lines of communication that are aligned with business objectives and are easily translatable to the wider organisation.
Using a Jobs-to-be-Done framework further enables a common understanding of what a data requirement is and what customers need. When the Data Owner has the end-to-end view of the customer they will be able to determine which needs are unmet, explore further customer segmentation operations, develop new offerings for further market capitalisation, and align the actions with the wider organisation.
The Jobs-to-be-Done Framework would be central to the role of the Data Owner as it looks to understand who the customers and what the wider needs of the market are and therefore, builds a structure around the most interesting role of the Data Owner, which looks to gain further revenue from the organisation’s datasets.
What skills do Data Product Owners need?
While the role of the Data Product Owner is still nascent, with only around 200 professionals thus far holding that job title in the UK on LinkedIn, they do possess a key stratum of skills that can be found within the Freshminds network.
Given the commercial and strategic nature of the role, it lends itself to the consulting toolkit with the necessity to be able to communicate with senior stakeholders clearly and develop a vision for each dataset.
This, of course, requires technical expertise, which consultants who are data-driven might take as an opportunity to further develop their skills.
More information
To speak to our On Demand team about data professionals in our network, contact us.
Check out our other articles in our data series.
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