Richard Fusco

In an age where data is one of the most valuable commodities in the world, organizations across the business landscape are racing to build or rebuild their data platforms to make sure they are equipped to meet the demands of an increasingly data-dependent future.  The many challenges of updating data platforms is complicated by two significant trends today: the pervasive nature of data and its link to value creation and decision making, and the fact that data platform and analytics technologies are in the midst of substantial transformation of their own – driven by new licensing models, advances in cloud computing, serverless architectures, AI and machine learning.

But despite these complexities, leading executives are realizing complacency is not an option. The simple fact is that older data platforms can no longer handle the workloads needed to support new business models and growth in a data-driven economy. Having made the commitment to transition to modern data platforms and data products, knowing where to start and how to go about it without disrupting current business operations are key.  Here are five steps that can help you make your inevitable transitions go smoothly:

Start with your business goals

As a business committed to transformation your challenge is to evolve your data infrastructure into a platform that meets current day-to-day business needsand which supports innovation and anticipated future use cases, all while delivering a return on investment to the business along the way.  Identifying the needs of the business required for today and anticipated for tomorrow is the biggest factor in creating a a business strategy to guide a new data platform. Consider the following questions at the beginning of the upgrade initiative; What are you trying to achieve as an organization? What are the problems you are trying to solve? What is the value of data inside the organization? What needles are you trying to move?

It’s from the perspective of well-defined business goals that current architectural deficiencies can be understood, and from which a modern data strategy and data plaform roadmap can begin to take shape.

Establish your data strategy

A data strategy forces you to articulate the value of data inside the organization.  It tells you how to move forward, it helps the business at-large understand the importance of data in creating  value and it drives consensus around the strategy and technology roadmap. Data strategy is not simply about reducing risk or implementing governance.  An effective data strategy integrates a wide range of factors: the goals of the business and product teams, the capabilities and workflow of the analytics and engineering teams, the health of existing systems and data resources, and on down to the “plumbing” aspects of data management.  A coherent data strategy is an essential component in protecting and leveraging enterprise data. 

Rank projects based on prioritized goals

Because few organizations have the luxury of building a modern data platform from scratch, a progressive, pragmatic approach is most effective.  Projects are the incremental steps toward building a data platform, one slice at a time. Solve for the most important problems first. This approach allows organizations to keep the lights on, easily track returns on investments and begin to workout which new products may be attractive and when.  As new projects come on, and with more capabilities in place, the velocity of project execution will improve. 

Building the platform

With transformations occurring in every quarter of the data and analytics ecosystem, the incremental approach makes sense for most organizations.  The initial steps of this approach address the essential components that must go into building a modern data architecture, among them data security, governance and master data management.  Two other components stand out as especially crucial to the long-term value of the architecture: capabilities supporting data as-a-service and developing a real-time infrastructure. 

Most organizations need to manage data from multiple internal and external sources.  Providing accesses across diverse data sets and data management systems is challenging and costly. Modern data architectures are able to overcome these access issues by enabling a virtualized data services layer.  Data virtualization integrates data from various sources, physical locations and formats to create a logical abstraction layer that standardizes data services for multiple applications and users.

Modern data platforms need to be geared for real-time or near real-time capabilities to support the movement of data and the results of data analysis to decision-makers and to customers at the right time it’s needed.  Whether it’s predictive analytics coming from a data warehouse or a recommendation  based on an analysis of streaming behavioral data from a website, the ability to act on this information in real time will be a distinguishing feature of leading brands.

Operate like a data company

Traditionally, the design of data systems centered around the processes they perform to make them more efficient.  It wasn’t until the earlier stages of digital transformation that customer experience became the center of attention, and data began to be seen as  the valuable resource it is today.

Among the first businesses to realize this were digital native start-ups like Uber and Airbnb.  For them customers weren’t viewed as isolated, anonymous consumers, but contributed data within an ecosystem in which their participation makes the product better for everyone.  Today,  experience transcends product and price as the key differentiator for customers.  Curated data, and data that’s been turbo charged by AI and machine learning, fuels the customer experience and speeds decision-making in every corner of the enterprise.

Being a data platform company means data management, data analytics, and ultimately, data products are at the heart of business operations.  For some it means shifting the focus from data reporting to data analytics; enabling data as a service; responding to data signals in real time; creating data products that are never “done” but evolve; and embracing agile to continuously improve and create lifetime customer value. 

Operating like a data company is essential to remaining competitive in the digital economy. This means building data platforms that are grounded first in business strategy and which are responsive to the needs of organization and customer today as well as the anticipated needs and innovations of tomorrow. There is and will continue to be a constant stream of competing and disrupting technologies entering the marketplace to challenge the status quo.  However, organizations that have developed the operational experience to adapt quickly to change by aligning data strategy with modern data platform development, are the ones who will survive and thrive.  

data, execution, strategy

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