Essential aspect of your data strategy

Seven of the ten most valuable companies are powered by the platform. The success of these organizations has come from creating platforms that unlock tremendous value for both users and producers. The platform enables your organization to deliver value faster by allowing users to focus on what matters to them without worrying about hardware and software.

The data platform is an enabler for enterprise-wide analytics on massive amounts of data. The data platform may store data from a variety of sources, including customer, product, financial and data from surveys or other research projects. Over the years, data strategy has become an important part of an organization’s overall strategy. You will easily find lots of documentation on writing a solid data strategy.

One of the important parts of the strategy is building the data platform that fits the needs of your organization. There is ample literature on building a data platform stack and choosing the appropriate techniques or patterns. Unfortunately, there is a paucity of good articles on writing a solid data platform strategy. It’s great that every organization wants to build a future-proof, scalable platform, but spending time writing a data platform strategy is essential.

Defining the vision is the first and most important part of your strategy. The goals you set are a measure of the success of your platform. This is where you articulate your short to long term vision. Building communities, process feedback, define innovation and measure the value you deliver will be part of your principles.

Modern data platforms should provide analytical processing, acquisition, storage, mining and data visualization. Platform strategy borrows many principles from data strategy, but focuses more on being an enabler. The platform’s goals should be to meet all requirements from a comprehensive data strategy and, if possible, expand the scope to those more relevant to it.

These are non-negotiable and are a part of the existence of your platform.

The target market for your data platform is important in deciding your technology options. I have personally built data platforms that are used by both internal and external stakeholders. Platform status can come from the need for advanced critical applications, ad-hoc analysis, or well-defined reports that result in multiple use cases and capabilities for your system. Additionally, understanding the skill demographics of your target user base will help you make informed decisions.

One of the important decisions you have to make here is team structure. Having a central team or a distributed team to develop and maintain data workloads will decide whether to adopt your platform. Typically, the data platform team is responsible for defining and maintaining the data assets that will be made available to end users, such as data models and data warehouses. They are also responsible for developing and maintaining the architecture of the data platform and ensuring that it is properly integrated with the organization’s business processes.

The success of a platform is measured by the number of empowered users leveraging its capabilities to produce value for its stakeholders. Participation is an important measure and driver of platform adoption. You should also look at existing organizational forums and do a SWOT analysis if possible. There are many ways to increase utilization, but a key driver of utilization is generally your enterprise technology strategy. You cannot build a platform without socializing with others and showing the value you plan to deliver in the future. Data platforms typically sit as a horizontal offering across your technology and business landscape, cutting across all initiatives and verticals.

Define methods, processes and techniques to develop trust in your platform and increase its adoption. With a data platform, most likely, you will not be the owner of the data but its gatekeeper. This is an excellent time to define data ownership and the roles you see within the platform from an accountability and responsibility standpoint. Work with the positioning pillar of your strategy to solve a data governance and management problem.

Design patterns are the first step towards building or buying a data platform. With the information you have now, you are beginning a journey of assessing requirements and making technology choices. Even if the vision is readily apparent, it is hard to translate it into tangible high-level patterns. You need to understand and document internal and external integration with a focus on answering key questions related to design. Modern data platforms must provide the data needed for advanced critical applications and ensure data availability for the modern enterprise.

Creating a platform for the future and making it work in the present is not an easy task. Whether you take a self-service approach or a governed design, the time and effort it takes to create it is significant. So it is important to take time in evaluation, but do it in one go. From real-time to batch use cases, the platform should look at all aspects of ingestion, curation and reporting, with security and governance at the center of everything. Data is sensitive in today’s world, and it is your duty as a platform to abide by the laws of the land apart from protecting the interests of your users.

Platforms live or die by their scope and adoption. Development is essential for new and well established platforms in use cases and users. With rapid changes in technology, keeping pace with trends and updating your platform with new capabilities, your platform will be secure for the future.

Will the future of data platforms be built on blockchain, or is they not something in the metaverse that I can easily predict?

Undoubtedly, the data platform will remain relevant in providing insights to business users, even though they can engage with insights in a number of ways.

There is no doubt that the future of data platforms is bright. With the proliferation of big data, the need for scalable, efficient data platforms has never been greater. Organizations of all sizes are turning to data platforms to help them understand their data and enable them to develop more effective strategies. Data platforms are becoming more sophisticated and affordable all the time, and they are becoming more versatile. They can now be used to manage data from traditional sources, such as customer records, and data from new sources, such as sensor data.

In addition, data platforms are becoming more automated, which can help organizations process and analyze large amounts of data more quickly and efficiently. As data platforms grow, we expect to see more innovative and powerful data products.

Businesses and organizations can use data platforms to more effectively manage their data and make it available to their users in a variety of ways. It can manage datasets, store and access data from multiple sources, and make that data available to users on a variety of devices. The Data Platform may also be used to create applications that use data to provide personalized experiences to users.

“Success is 20% skill and 80% strategy. You probably know how to be successful, but more importantly, what is your plan to be successful?” — Jim Rohney

Strategy is to choose the best course of action to achieve an objective. The goal of the strategy is to identify opportunities and threats, assess the strengths and weaknesses of each option, and choose the best course of action to accomplish the objectives. There are several types of strategies: operational, tactical and strategic. There is no one-size-fits-all answer to writing a solid platform strategy, as it will vary based on many factors. From analysis to implementation, the strategy should cover all aspects.

An organization investing heavily in realizing the value of data should continue to evaluate and refine its data platform strategy over time.