Growth Strategy Harvard Business Review – Summary. Most companies struggle to capture the enormous potential of their data. They typically run massive programs that try to meet each end user’s data needs, or individual application development teams set up custom data pipelines that cannot be easily changed. Instead, companies must figure out how to create data strategies that deliver value in the short term, while laying the groundwork for future uses of data. Successful companies do this by treating data like a business product. When a company develops a product, it tries to maximize sales by meeting the needs of as many customers as possible – often by creating a standard offering that can be tailored for different users. A data product works in a similar way. It provides an easy-to-use, high-quality dataset that people across the organization can use for a variety of business challenges. It can, say, provide a 360-degree view of customers, employees, or a channel. Because they have so many applications, data products can generate impressive returns. For example, a customer data product at a large bank has nearly 60 use cases, and these applications generate $60M in incremental revenue and eliminate $40M in losses per year.
Too often, companies’ data efforts fail to lay the groundwork for future uses of the data. Individual teams create a custom data feed for each application that cannot be easily changed.
Growth Strategy Harvard Business Review
Create standard data products that can be tailored to the needs of different types of users and many applications. Products can be managed by dedicated teams within business units, supported by a central function that coordinates and standardizes design.
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While all companies recognize the power of data, most struggle to unlock its full potential. The challenge is that investments in data must deliver value in the short term while laying the groundwork for rapidly evolving future uses, as data technologies evolve in unpredictable ways, new types of data emerge and data volumes continue to grow.
The experiences of two global companies illustrate how ineffective today’s prevailing data strategies are in meeting these challenges. The first, a large Asia-Pacific bank, took a “big bang” approach, assuming it could meet the needs of all analytics development teams and data end users at once. It launched a massive program to build pipelines to pull all the data in its systems, clean it, and aggregate it into a cloud data lake without spending a lot of time aligning efforts with business use cases. After spending nearly three years building the new platform, the bank found that only a few users could easily use it, such as those looking for raw historical data for ad hoc analysis. Furthermore, the critical architectural needs of many potential applications, such as real-time data feeds for personalized customer offerings, have been overlooked. As a result, the program did not generate much value for the company.
The other company, a large US bank, had individual teams that individually leveraged existing data sources and systems and brought together any additional technologies required by their business use cases. The teams created some value by solving issues like improving customer targeting for digital channels and enabling effective risk reporting. However, the overall result was a chaotic tangle of custom data channels that couldn’t be easily reworked. Each team had to start from scratch, making digital transformation efforts painfully expensive and slow.
We’ve found that companies are most successful when they treat data like a product. When a company develops a commercial product, it generally tries to create an offering that can satisfy the needs of as many users as possible in order to maximize sales. This usually means developing a basic product that can be customized for different users. Automakers do this by allowing customers to add various special options to standard models – leather upholstery, tinted windows, anti-theft devices, etc. Digital applications also allow users to customize dashboards, including customizing the layout, color schemes and displayed content, or offer different plans and pricing structures for different user needs.
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Companies that treat data as a product can reduce the time it takes to implement new use cases by up to 90%.
Over time, companies improve their products by adding new features (engine modifications that improve the car’s fuel economy or new features in an app) and introduce entirely new offerings in response to user feedback, performance ratings, and changes in the marketplace. . All the time, companies are trying to increase production efficiency. They reuse existing processes, machines and components wherever possible. (For example, automakers use a common chassis across completely different cars, and application developers reuse blocks of code.) Treating data the same way helps companies balance current value delivery and pave the way to getting more value quickly. tomorrow.
In our work, we saw that companies that treat data as a product can reduce the time to implement new use cases by up to 90%, reduce the total cost of ownership (technology, development and maintenance) by up to 90%. to 30% and reduce risk and data management burden. In the pages that follow, we describe what a data product is and describe best practices for creating one.
A data product provides a ready-to-use, high-quality dataset that people across the organization can easily access and use for a variety of business challenges. For example, it can provide a 360-degree view of customers, including all the details collected about them by the company’s business units and systems: online and in-store shopping behavior, demographic information, payment methods, their interactions with the customer service and much more. Or it can provide a 360-degree view of employees or a channel such as bank branches. Another product could enable “digital twins” using data to virtually replicate the operation of real-world assets or processes, such as critical machine parts or an entire production line.
A Better Way To Put Your Data To Work
Because they have so many applications, data products can generate impressive returns. At a large national bank, a customer data product was driving nearly 60 use cases – from real-time credit risk assessments to chatbots that answer customer questions – across multiple channels. These apps are already generating $60M in incremental revenue and eliminating $40M in losses per year. And as the product is applied to new use cases, its impact will continue to grow.
Data products are placed on top of existing operational data stores, such as warehouses or lakes. Teams using them don’t have to waste time looking for data, processing it in the right format, and creating custom datasets and data pipelines (which ultimately creates architectural confusion and governance challenges).
Each data product supports data “consumers” with different needs, just as a software product supports users working on computers with different operating systems. These consumers are systems, not people, and our work suggests that organizations typically have five types. We call them “consumer archetypes” because they describe what the data is used for. They include:
This requires specific data that is cleaned and stored in the required format – for example, as individual messages in an event stream or as a table of records in a datastore (an area of data focused on a single topic, business function or team)—and delivered at a specified frequency. For example, a digital application tracking a vehicle’s location will need real-time access to GPS event streams or sensor data. A marketing application designed to find trends in customer browsing behavior will need on-demand access to large volumes of weblog data (often referred to as “batch” data) from a data store.
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They also need to clean and provide data with some frequency, but they must be designed to be processed by machine learning and AI systems such as simulation and optimization engines.
This requires highly managed data (data with clear definitions that is tightly controlled for quality, security and change) to be aggregated at a basic level and delivered in a controlled way for use in regulatory and compliance activity dashboards or regulations. Data typically needs to be delivered in batches, but companies are increasingly moving to self-service models and intraday updates involving real-time feeds.
This allows for ad hoc exploratory analysis of a combination of raw and aggregated data. Data scientists and engineers often use them to dive into data and discover new potential use cases.
They must adhere to strict policies and agreements about where data resides and how it is managed and protected. Banks use these systems, for example, to share information about fraud with each other, and retailers to share data with suppliers in hopes of improving supply chains.
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Each consumer archetype requires different technologies to store, process and deliver data, and requires that these technologies be assembled according to a specific pattern. This pattern is essentially an architectural blueprint for how the required technologies should fit together. For example, a sandbox pattern would likely include technologies to set up a multi-user, self-service environment that data engineers across the enterprise can access. O
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