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How to adopt a Data-as-a-Product mindset

The guide to Data as a Product

The guide to Data-as-a-Product in 2022

What use is owning data sets that could transform your organizational outcomes if most of your organization can’t access them?

Executives in every industry are facing that headache-inducing business problem every day. Here at Kin + Carta, we call it The Great Data Tension. The potential energy stored within data has been growing for years, but prescriptive issues with people, processes and technology are keeping it locked away and rendering it redundant.

The possibilities of releasing that energy with a new tightly scoped, pragmatic approach to data are endless. It’s time to redefine our organizational relationships with data and become truly and sustainably agile.

Some of the world’s most adaptable organizations are already harnessing that energy and using it to accelerate the value of their data, reduce time to insights and, as a result, make better, faster and fewer decisions as businesses.

They are treating data not as a project, but as a product; one that is accessible, visible and usable for everyone, no matter their discipline or desire. They are empowering their people to think differently and modernizing their processes to work differently when it comes to unlocking insights. They are changing the way they see data at every level, for every user, in every line of work.

The era of data tension is coming to an end. The era of true data potential has finally begun—and it is embodied by a Data-as-a-Product (DaaP) mindset.

What is Data-as-a-Product?

Modern consumers of all kinds expect to be able to access the product they want, when they want it, from a supplier they know and trust—the definition of a product being an outcome or set of outcomes a user expects from a physical or digital experience, such as ordering a taxi via an app and knowing its ETA, journey time and driver identity. When they can’t access the desired outcome, the value of that product is lost to them and they can’t benefit from it the way they had hoped.

Traditionally, data hasn’t been treated as this kind of outcome-driving asset. However, the same product-focused logic can be applied to the decades’ worth of potential insights that organizations have now accrued. Employees should be able to access the specific data and insights they need, when they need it, from an instantly identifiable source within their organization. The trouble is that years of data growth without governance, standards and a menu of services, provided via a central product function, have led to masses of untapped energy kept in scores of useless silos.

Nobody knows where to look and nobody knows how to look because the paths to access haven’t been defined. The organizational architecture has not been built to facilitate an accessible and agile approach to data and insights.

This is where a product-focused way of thinking can change everything. By treating data as a product and making it available to anyone with a specific problem to solve, you unlock the same set of tools that product owners use: hypothesis-driven measurement and iteration, a clear and compelling vision and strategy and a cross-functional roadmap that brings teams together.

How to use Data-as-a-Product

Adopting and embracing a DaaP mindset means building data-centric products is no longer confined to one-off projects; it becomes an iterative process of building and improving models that help you maximize your impact and achieve your vision. It means embedding agility at every level of an organization so every brand, every department and every end-user therein can get exactly what they need from data, exactly when they need it.

This way of working isn’t built overnight, of course. It’s the objective of a maturity model that’s naturally different for every organization—a model that begins with data silos and solutions and helps a business mature into Data-as-a-Product thinking that mirrors the performance of the most agile of digital natives.

There are four stages to this model for businesses on a journey towards data maturity:

  1. Realizing

    An awareness of the data opportunity and a mandate to become data-driven exist, but organizational silos prevent users from accessing—or sometimes even knowing about—tools and data that will be useful to them. There is no clear data strategy or governance, while cloud strategy and self-service analytics certainly have better years ahead of them.

  2. Transforming

    A Chief Data Officer (CDO) is now onboard and business and engineering teams are collaborating on tightly scoped data products addressing specific user needs. The data strategy has moved from solutions to products, a modern cloud data stack is on its way and a Change Management program is ushering in a new data-centric way of working.

  3. Leading

    A clear data decisioning culture is prevalent and product monetization is spawning new revenue streams, with cross-functional teams adopting a product/market fit approach to all new data products. Synergies between these data products are identified by a central product team, allowing repeat usage, removing duplication and, thus, creating multiple efficiencies within the organization.

  4. Innovating

    DaaP thinking is the fundamental driver of business growth and customer experience (CX) through monetized data products. Operations and analytics are fully integrated in the cloud, artificial intelligence (AI) and machine learning (ML) are built into every business domain and the organization is a recognized innovator in data strategy and products.
Graph showing the four stages of data maturity
The four stages for businesses on a journey to data maturity

While large global organizations aspire to achieve the optimal state of data maturity at stage four, most organizations find themselves at stages one or two, champing at the bit to unlock the potential of one of their most valuable assets. That’s where Kin + Carta comes in; our DaaP experts leverage world-class skills and experience to map out a data transformation journey that turns potential into products for organizations that have a mandate to become completely data-driven.

We think that accessing data and insights should be as easy as identifying and taking prescription medication when you need it; trust the Product Owner of the data as you would the medical experts who penned the prescriptions and instructions for the drugs you need and, naturally, you’ll know precisely what you’re getting and how to use it. So long as the technology is in place to streamline the transaction, all the signs of an agile and digitally astute operation are manifest.

Clearly, this requires not only a technological shift in the way data is stored and owned, but also a cultural shift in the way it is accessed and used if faster and better decisioning is to become the norm. A DaaP vision can only be realized against this maturity model if the people within an organization are equipped to make the most of the changing technologies around them.

Examples of Data-as-a-Product

Top FMCG drinks brand's Data Science Platform

The consolidation of access to consumer insights and first- and third-party data has delivered an operational breakthrough at the company; for the first time ever, multiple brands can now leverage the same consumer information in a streamlined data market.

Together with the company's Chief Data Officer and team and Microsoft Azure, we applied DaaP thinking to pave the way for a Global Data Marketplace to empower the organization’s many brands. Along with an “Intelligent Prospecting” Leads Engine data product, Data Science for Net Revenue Management, and a Global Ag Data Strategy, the outcomes have delivered a 12% improvement in forecasting and an error reduction worth $2.7B to the company.

Top tech hardware brand's Global Print Division

Automated AI has enhanced Supply Chain Forecasting for the company's Print Division by making accurate upstream and downstream predictions a reality. Previous models had been unable to assess future market demand due to a narrow focus on sell-in and sell-through, but our experiment-driven approach allowed us to devise a model that unlocks the value of the company's data in real-time.

This new approach to supply chain automation, powered by our proprietary automated AI platform, Octain™, was deployed only three months after our strategic discussions began. It quickly improved average EMEA error levels from 17% to 5% and now plays a crucial role in the company's supply chain down to individual SKUs for its ink cartridges.

Creating a data-driven business culture

Business-wide digital transformation can change relationships with data for the better, which, in turn, opens up greater possibilities for data-focused products that improve people’s lives. A significant part of that culture shift, though, is the necessity to build data literacy in from the start. Without a workforce that is fluent in data, modernization is redundant.

This means it is of paramount importance to organize departments and people around data domains and educate them in how to interpret and apply them. The application of this approach to App Modernization has garnered groundbreaking results with Microservices and we believe Data Modernization will realize the same outcome.

If discovery, analysis and insight are built into everyday workflows via immediately accessible data and robust training, the outcomes will be more likely to benefit the business.

If cross-functional data decisioning is possible because the intellectual foundations of collecting, storing and managing data exist, the new data linguists will be empowered to do more and do it better.

If a single source of truth is present and coping mechanisms like static spreadsheets are banished, users can access the same outputs from a data product and remove any differences of opinion about whose KPIs are right.

Every organization is at a different level of fluency when it comes to data; DaaP aims to instill an absolute focus on people and process until data becomes a second language for everyone. It brings together the ideal combination of product strategy, data strategy, engineering and Change Management to prepare an organization for a data-fueled future of innovation and growth.

Two people looking at a whiteboard

Leading and innovating with a DaaP-first mindset

Once data is being leveraged product-by-product on an iterative basis and not project-by-project on a rigid basis, an organization can begin to reap the rewards of modern agility.
Machine learning initiatives become accelerants to value, monolithic systems become things of the past and DaaP thinking reigns supreme as everyone can access what they want, when they want it—from customers to content marketers to CEOs.

An end-to-end trust is woven into the fabric of company culture and data sets and insights are consumed at all levels because the Product Owner makes it pain-free to do so. Crucially, they care about the outcomes for all data users and carry a request through to the final product to achieve them, which is a luxury that perhaps only digital natives have had until now.

New sources of revenue are continually emerging as more products are getting monetized and more resources are being saved because innovation with data can happen faster and more efficiently. Time to insights is drastically shortened and the same goals can be achieved with fewer teams.

Even talent becomes easier to hire and retain because specialists are free to specialize; the fact that organizational silos have become a thing of the past means that people can enjoy reduced friction in the movement of resources. In other words, they can collaborate more and wait less.

Ultimately, operations around data are far slicker and more rewarding for everybody because finger-in-the-wind decision-making has been eradicated by a product-first mindset around data. Informed choices breed refined products that directly address audience needs and, what’s more, they can be achieved at a higher development velocity than ever before. Sounds almost utopian, doesn’t it?

Unlock the energy in your data

If you are on the verge of transforming your relationship with data, you can lead as a truly modern and agile organization if you embed product thinking into every move you make.

Change requires innovation and momentum to affect every level of your organization for the better, but it also takes courage and persistence because it can’t happen in a heartbeat. On your journey to data maturity, a steadfast focus on people, process and technology will nurture the outcomes required to make a tangible difference to the world.

To discover how you can unlock your data and make it owned and easier to discover, consume and trust by everyone in your organization, discuss a strategic review with one of our data experts today and start making fewer, faster and better decisions tomorrow.

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