The process—and promise—of delivering ML value to the enterprise is frequently challenged by the amount of data that needs to be used for training and then re-training. This is magnified by the emerging discipline of, in which models are constantly re-trained and released, producing even more data. It can be easy to focus simply on the volume and storage costs of data, but keep in mind the larger goals.
This can help all parties that need to make data-driven decisions to do so, whether they are software developers, business analysts, operations managers, strategic negotiators, or virtually any interested party—and whether they need solutions for the office, for working from home, or . Application design and development are no longer the exclusive purview of the developer, allowing anyone in the workforce to do more with data.Many enterprises have historically shown reticence toward openness, keeping data within the corporate firewall and locking off access to digital assets. This is the equivalent of building corporate castles—replete with moats, drawbridges, and heavy castle walls—around the data: It might feel safe, but it makes it awfully hard to get the data moving.
This can help build a culture around developer enablement while still keeping data protected. In some organizations, data is siloed even between business units, impeding development and management of products that are sensitive to a 360-degree view of customer interactions. These impediments can lead to negative customer experiences, such as having multiple apps to log in to, multiple different usernames and passwords for a company’s apps, and even multiple different payment methods.
Becoming a data-driven company requires assessing an organization from multiple angles: infrastructure rationalization and developer engagement to strategic alignment, new KPIs oriented to encourage employees to do more with data, and potential network effects that may arise from expanded data efforts.
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