Every second there is a plethora of data generated in various forms. Organizations value this vast quantum of data, analyzing it and deriving insights to make data-driven decisions. However, the ability to drive actionable insights from the data is still limited to data analysts or data scientists, who know how to organize, crunch, and interpret data.

Data democratization is a game-changer. It is the process that enables everyone in an organization, irrespective of their technical know-how, to have access to data without any gatekeepers that create a bottleneck, work comfortably, make data-driven decisions and build delightful customer experiences. It establishes the base of self-service analytics and allows everyone (technical or non-technical) to gather and analyze data without seeking help from data professionals. The ability to instantly access and understand data will translate into faster decision-making and more agile teams.

The first step would be to break down information silos. Customizable analytics tools capable of desegregating and connecting previously siloed data, making it manageable from a single place, are imperative. This single source of all data is called a Data Marketplace; It enables online transactions which facilitate data sharing and data monetization, driven by the volume, velocity, variety, and veracity of big data.

Factors that shape maturity of the Data Marketplace

A data marketplace is not a one size fits all model. Instead, multiple factors shape the maturity of a Data Marketplace. The most important factors to consider before adopting the data marketplace ecosystem are the authenticity, quality, and reliability of the data in use. The reliance would be on data coming from multiple sources including, but not limited to – third-party datasets, primary sources like Business units data, or various personas within and outside the organization. Checking the quality and reliability of data on multiple checkpoints is of utmost importance since that data would drive critical business decisions.

Decisions based on incorrect data can be catastrophic to an organization. Once the quality is in check, next-in-line is data governance, the value chain of collection of processes, roles, policies, standards, and metrics that ensure the effective and efficient use of information.

Then, it’s important to decide and determine the level of access for data architecture (centralized or decentralized) data for various members of the organization and the subscription model: free, freemium, or pay-per-use basis.

Brillio’s Maturity Framework

Brillio has built a Data Marketplace Maturity framework that assesses multiple stages of maturity for an organization’s data strategy and its ability to adopt a data marketplace ecosystem. They are designed in such a manner that each one is an addition to the previous one i.e., each successor stage consists of all the features that the predecessor had and adds them up with enhanced features. Below, we define the four maturity stages in the Data Marketplace model –

• Explorer – A beginner stage where the organization employs basic reporting without significant data onboarding. Data is used solely for reporting purposes, with ad-hoc analysis. While there is a great amount of data available most of it is still in a raw format, needing to be processed to derive actionable insights. The processed data can be used to derive descriptive analytics but cannot be used to predict future behavior. Organizations leverage this data for internal users but advocate manual data governance, which lacks consistency at an organization level. Data exists in siloes, with limited access to a few stakeholders. Thus, an organization would still require the help of data professionals at this maturity level.
• User – This is the stage where data is no longer in the source format and can derive business insights. The firm no longer uses basic level reporting but advanced to interactive dashboards through Business Intelligence tools like Power BI or Tableau. Data no longer exists in siloes, being present in a very centralized architecture with closed market access instead – for which the firm pays a fixed price or uses the freemium model. The organization takes help from various data providers using the marketplace to enrich and supplement its own data. The data flow is automated using specific architecture; however, the data is still used for descriptive and diagnostic analysis but is not at the scale to predict future actions.
• Leader – The business stakeholders have access to preprocessed data, self-serve analytics, without the help of data professionals. They use interactive, personalized dashboards for a variety of audiences, based on multiple use cases. This is where third-party data comes into play as the differentiator. Data is not used only for descriptive analysis but also leveraged for predictive analysis, setting the course for future actions. Data governance is well-monitored, and all individuals across the organization have access to the data via a role-management approach. The firm may choose a pay-per-use subscription following a hybrid architecture operations model.
• Innovator – This stage is the most advanced in the maturity framework, where data is instrumental in a continuous evolution of business strategy. The organization has built the AI/ML algorithms that adapt and improve business objectives using prescriptive analytics, with data governance integrated into all business processes. It is a decentralized, open exchange platform with access to all users with varying complexity levels based on individual roles, scalable as needed. It tracks and combines the journey across channels and devices and provides online and offline integration. The organization pays listing, service, and storage fees and earns revenue by selling data to other organizations/individuals.

The Building Blocks of the Maturity Stages

The foundational building blocks are the key to improving the maturity of an organization’s data strategy and adopting the data marketplace ecosystem.

• SaaS Application Strategy – The SaaS approach can lead to firms having licensed access to the data marketplace without worrying about maintenance. Moreover, for companies having data spread out across channels, the omnichannel integration with cloud architecture, multitenancy and microservices would lead to the integration of all the available data. Moreover, APIs (Application Programming Interface) can function as a key enabler for the revenue channel with a subscription model, which can be availed/canceled at any time.
• Interactive dashboards – They would host rich, interactive data visualizations and user personalization using clickable wireframes of the user journey and would make sure that anyone that has access gets the processed data, ready to be used for decision-making, minimizing technical help required.
• Data Products-Ready insights – Data Marketplace would offer multiple data products that deliver contextual insights for relevant business problems, as opposed to the traditional methods that force a data scientist to devote maximum time towards data preparation and cleaning.
• Use case backlog – Data would be categorized into multiple use cases, which would lead to the easier tracking of data by any individual/business, as needed. The use cases can help provide guidance and serve as a baseline for decision making and a framework can be leveraged to classify the multiple use cases among free, freemium, and charged models, depending on the complexity.
• Human-centric Design approach – The UI/UX would be highly personalized to be usable by all tech and non-tech personas and the future models would be mobilized based on feedback generated to increase productivity. There would be real-time updating of the marketplace incorporating all the customer feedback generated over time.
• Operations/delivery model – To fulfill the goal of data being accessed and used by all user personas, the infrastructure as a code service would help organizations avail all the benefits of public cloud on their own private cloud infrastructure.
• Financial model – The financial model is determined based on the delivery model that the user opts for.

All the above factors combined can provide a good head start for anyone who wants to venture into a data marketplace model. Keep in mind all the factors, decide on and analyze the building blocks, and assess the maturity level your organization currently stands at. Only then conduct a gap analysis with the maturity level that you want to get to. Brillio’s assessment methods provide a clear roadmap laying out the features and building blocks on the path to a successful data marketplace journey.