Here’s how to make the most of your data

Package your data as if it is a product if you want to really exploit its value.

Big Data Thin Line Series

Though every company recognises the power of data, most struggle to unlock its full potential. The problem is that data investments must deliver near-term value and, at the same time, lay the groundwork for rapidly developing future uses. While data technologies evolve in unpredictable ways, new types of data emerge and the volume of data keeps rising.

We find that companies are most successful when they treat data like a product. When a firm develops a commercial product, it typically tries to create an offering that can address the needs of as many kinds of users as possible to maximise sales. Often that means developing a base product that companies can customise for different users.

In our work, we’ve seen that companies that treat data like a product can reduce the time it takes to implement it in new use cases by as much as 90 per cent, decrease their total ownership costs by up to 30 per cent, and reduce their risk and data governance burden.

WHAT IS A DATA PRODUCT?
A data product delivers a high-quality, ready-to-use data set that people across an organisation can easily access and apply to different business challenges. Because they have many applications, data products can generate impressive returns.

Data products sit on top of existing operational data stores, such as warehouses or lakes. The teams using them don’t have to waste time searching for data, processing it into the right format and building bespoke data sets and data pipelines.

Each data product supports data “consumers” with varying needs. These consumers are systems, not people. Our work suggests that organisations typically have five kinds. We call them “consumption archetypes” because they describe what the data is used for. They include:

Digital applications: These require specific data that is cleaned, stored in the necessary format – perhaps as individual messages in an event stream or a table of records in a data mart (a data storage area that is oriented to one topic, business function or team) – and delivered at a particular frequency.

Advanced analytics systems: These too need data cleaned and delivered at a specific frequency, but it must be engineered to allow machine learning and artificial intelligence systems, such as simulation and optimisation engines, to process it.

Reporting systems: These need highly governed data (data with clear definitions that’s managed closely for quality, security and changes) to be aggregated at a basic level and delivered in an audited form for use in dashboards or regulatory and compliance activities. Usually, the data must be delivered in batches, but companies increasingly are moving towards self-service models and intraday updates incorporating real-time feeds.

Discovery sandboxes: These enable ad hoc exploratory analysis of a combination of raw and aggregated data. Data scientists and engineers frequently use these to delve into data and uncover potential use cases.

External data-sharing systems: These must adhere to stringent policies and agreements about where the data sits and how it’s managed and secured. For example, retailers use such systems to share data with suppliers to improve supply chains.

Each consumption archetype requires different technologies for storing, processing and delivering data and calls for those technologies to be assembled in a specific pattern. This pattern is an architectural blueprint for how the necessary technologies should fit together. Like a Lego brick, a data product wired to support one or more of these consumption archetypes can be quickly snapped into many different business applications.

MANAGING AND DEVELOPING DATA PRODUCTS
Whether they’re selling sedans, software or sneakers, most companies will have internal product managers who are dedicated to researching market needs, developing road maps of product capabilities, and designing and profitably marketing the products.

Likewise, every data product should have a designated product manager in charge of putting together a team of experts to build, support and improve it across time. Both the manager and the experts should be within a data utility group that sits inside a business unit. Typically, such groups include data engineers, data architects, data modellers, data platform engineers and site reliability engineers. Embedding them within business units gives the data product teams ready access to both the business subject-matter experts and the operational, process, legal and risk assistance they need to develop useful and compliant data products.

A company also needs a centre of excellence to support the product teams and determine standards and best practices for building data products across the organisation. While most companies already have some, if not all, of the talent needed to build out their utility groups and centres of excellence, many will need to deepen their bench of certain experts. Companies will need to increase their teams of data engineers who can clean, transform and aggregate data for analysis and exploration.

TRACKING PERFORMANCE AND QUALITY
To see whether commercial products are successful, organisations look at barometers such as customer sales, retention, engagement, satisfaction and profitability. Data products can be evaluated with commensurate metrics, such as number of active monthly users, the number of applications across the business, user satisfaction and the return on investment for use cases.

And just as manufacturers routinely use quality assurance testing or production line inspections to make certain their products work as promised, data product managers can ensure the quality of their offerings’ data. To do so, they must tightly manage data definitions, availability and access controls. They must also work closely with employees who own the data source systems or are accountable for the data’s integrity.

WHERE TO START
Leaders often ask which data products and consumption archetypes will get the highest and fastest return on investment. The answer is different for every organisation.

To find the right approach for their companies, executives need to assess the feasibility and potential value of use cases in each business domain (such as a core business process, a customer or employee journey, or a function). Leaders should group these cases first by the data products they require and then by the consumption archetypes involved. Data product decisions often involve trade-offs between impact, feasibility and speed.

Most leaders today are making major efforts to turn data into a source of competitive advantage. But those initiatives can quickly fall flat if organisations don’t ensure the hard work they do today is reusable tomorrow. Companies that manage their data like a product will find themselves with a significant market edge in the coming years, thanks to the increases in speed and flexibility and the new opportunities that approach can unlock.

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Veeral Desai is a senior expert in the Sydney office of QuantumBlack, AI by McKinsey & Company. Tim Fountaine is a senior partner in McKinsey’s Sydney office. Kayvaun Rowshankish is a senior partner in McKinsey’s New York office.