Just as oil was the most valuable fuel of the 20th century, many industry leaders believe that data is the most valuable fuel for modern digital enterprises. But while oil may have its limits, data does not. Many organisations are already exploring ways to unify and speed up the way they use data in various business scenarios. As a result, we’ve seen the rapid emergence of data marketplace tools and platforms that seek to address this business requirement.
Essentially, data marketplaces are a “one stop shop” that businesses use for business requests, reports, and insights. Within an enterprise, data marketplaces offer a simplified architecture that can easily collect, collate, organise and integrate data from various sources to become a unified data platform. An effective data marketplace is able to structure the unstructured data across the organisation, by creating a compatible data model across silos that standardises data formats. This data is further emboldened through various enhancements such as data search interface and data visualisation, which allow for up-to-date, fast and easy projections and estimations of business scenario simulations, making it a valuable business tool.
This approach to accessing data allows businesses to utilise their company-wide data through a single point of interaction. This saves time and effort, as data no longer needs to be sourced, selected, and interpreted for each business case. As a result, businesses are able to achieve cost savings and quick turnover timelines by reducing data replication and data movement.
Realising the potential
However, as we continue to look at the changing digital data ecosystem, we can see that the potential of data marketplaces can be significantly enhanced when we leverage cognitive solutions to further extend their functionality. These technologies are far more powerful than the day-to-day data process automation tools most organisations are aware of and involve numerous innovations in machine-to-human or machine-to-machine interactions that can transform business outcomes.
Traditionally, most data operations have been done through manual interventions that require enterprises to invest an immense amount of workforce resources. However, cognitive solutions change this as they can speed up the outcomes by simplifying tasks such as driving data governance, continuously updating metadata, handling knowledge management and most importantly, monitoring data operations.
Data marketplaces in the enterprise
All the goals of the next-generation enterprise – speed, accuracy, and excellent customer experience - require companies to be quick to respond to changes and develop well thought out business scenarios to leverage emerging opportunities.
In global retailers, for example, the ability to integrate data from across diverse nations, customers, and people, doesn’t just end in the front end but also extends across their value chain. Ensuring a harmonious data ecosystem across geographies and sources is critical to being responsive, fast, and efficient in serving their customers across geographies with a high level of quality. Having active access to disparate data sources, such as inventory data, finance data, supplier data, and customer data creates the ideal data pool needed to track lead times and efficiently manage business plans over seasons, cycles, and different business scenarios.
In such a case, having access to a data marketplace is not only beneficial but also critical for sustained business growth and success. However, the challenge to being responsive at such a scale can prove daunting. While data marketplaces are useful in bringing data together, the final bottleneck remains invariably human. This is where AI and other cognitive technologies have played a big role and helped ensure rapid access to insights and intelligence.
Potential of AI in data marketplaces
Data marketplaces in a cognitive world don’t have to be “one stop shops” but instead can act as deployed agents. These cognitive agents function like an engine that continues to handle data operations and governance including all administrative tasks. As a result, they can support the daily needs of enterprises and enable quick outcomes in an agile business world that isn’t dependent on a human worker’s knowledge and ability to make decisions.
These data bots help organisations manage data operations, processes, and system performance in order to help contextualise business needs and enable monitoring of business KPIs. Consequently, they are able to help enterprises easily discover actionable business insights and help users sift through the hundreds of data variables and fields, while also being able to locate data and link it to the appropriate metadata for a more comprehensive dataset.
This type of holistic integration can help users acquire the most optimum data selection that is suited to their business scenario and removes the need for manual searching. In fact, with advanced AI and Natural Language Processing (NLP) based tools, data bots can assess a user’s data requirement history and proactively offer a more specific dimension of selections, saving time and effort. Users can also easily discover the data’s availability, and the bot can share various useful details such as the previous use-cases of a particular dataset, as well as the feedback surrounding its previous uses.
AI behind the platform
Cognitive intelligence tools can provide prescriptive suggestions for orchestrating new data sets as they become available on the platform, thereby making the process more proactive. They can also be used to assess the core effectiveness of the data provided. By evaluating the degrees of success of past deployments, cognitive intelligence tools can help businesses quantify the data KPIs and assess its value in achieving business goals. This process enables businesses to effectively plan the correct data for the correct business problems, by knowing which scenarios offer the greatest ROI, and whether it leads to better business outcomes.
Moreover, cognitive intelligence can play a valuable role in assisting in the management of data processes at their very fundamental level. Similar to virtual personal assistants, advanced cognitive intelligence tools can help in the processes that surround data administration by monitoring data flows on the platform. These solutions can also ensure an automated performance and quality check of the data marketplace platform.
Digital data ecosystems are undergoing a paradigm shift where enterprises need to adopt a data fabric approach to make data more accessible and actionable. Moreover, as more and more organisations begin to adopt a fully digital approach, the challenges of digital at scale can only be tackled with the benefits of cognitive technologies. C These solutions offer all enterprises the correct combination of tools with which to unleash their business intelligence potential and blaze into the modern digital age.
Venkata C. Krishna, Global Solutions Head, Digital & Analytics, HCL Technologies
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