Digital transformation is significantly dependent on data; however, organizations encounter difficulties due to the variety of data sources, formats, structures, environments, and platforms. When implementing hybrid and multi-cloud architectures, companies must address complex data challenges. Most enterprises either store their operational data in silos or have hidden it, resulting in the accumulation of large amounts of dark data.
Dark data is usually retained by organizations for compliance purposes only, and securing and storing dark data usually incurs greater costs than benefits. It is costing United Airlines almost US$1 billion in lost revenue to collect and store dark data.[1] Enterprise employees are building bottomless lakes of dark data without any regard for its purpose. Enterprise Strategy Group stated in a report that 47% of all data is dark data, on average, with a fifth of respondents saying more than 70% of their data is dark data.[2]
In terms of restaurant brands, Domino’s Pizza ranks among the best in the world. Using the company’s AnyWare platform, customers could order pizza online, through mobile apps, smartwatches, TVs, and even social media. In order to gain a holistic view of its customers and global operations, Domino’s wanted to integrate information from all channels – 85,000 structured and unstructured data sources in total. It was difficult for them to achieve those goals because they had 11,000 business users, 35 data scientists, and marketing agencies – all of whom wanted to build their own databases. Using Talend as a data fabric solution it is now able to capture data, cleanse it, standardize it, enrich it, store it, and allow it to be consumed by multiple teams. With its modern data platform in place, Domino’s now has a trusted, single source of truth that it can use to improve business performance from logistics to financial forecasting while enabling one-to-one buying experiences across multiple touchpoints.[3]
Cost is just one thing that matters when storing dark data, cause its increased data growth can create unstructured data nightmares. Huge volumes of dark data can make it harder to find what is useful and may mean that you may miss business opportunities. If demand for a piece of information increases, and we have access to it, and we can facilitate its re-use with a common framework, then the economic model by which such value is extracted will necessarily be more efficient than one where use is siloed or distinct. Point-to-point integrations and data hubs are no longer feasible solutions, as neither is suitable for highly distributed, siloed data. Most, if not all, business data has value, and that value changes over time depending on the business’s mission, goals, and operations. Businesses can exploit unanalyzed operational data as a source of economic opportunities. They can look at using this data to drive new revenues or reduce internal costs. The data businesses hoard can assist them in driving innovation, cutting costs, earning loyalty, and running circles around the competition.
Real-time access to business entity data is crucial for improving business outcomes. However, data can often remain inaccessible and unusable within an enterprise. A data fabric solution can address this issue by providing instant access to unified, clean, and complete data. With this solution, businesses can make more intelligent decisions, enhance customer experiences, and improve operational outcomes. This solution is especially helpful for addressing data challenges arising from a hybrid data landscape. By acting as a virtual connective tissue between data endpoints, it balances decentralization and globalization. The system enables efficient combining, accessing, and governing of diverse types of data. The ultimate goal is to provide contextualized data accessible to all users and applications within an organization, which is essential for achieving business value.
