It has been a whirlwind time for data managers and their enterprises, and the innovations that are reshaping data operations aren't showing any signs of slowing down soon. AI and advanced analytics are changing the game, of course, as are a myriad of technologies now available to help manage and extract business value from the data flowing through organizations. Here is what industry experts tell BDQ they now see emerging and what we can expect in the months and years to come.
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Software development has come a long way in the past few decades, but data management hasn't. It's time to change that. Despite automation becoming more ubiquitous, many engineering teams are still managing databases manually—a practice that can be time-consuming and complex, slowing down developers and impeding overall productivity. Additionally, current models force database administrators (DBAs) to work within silos, which creates logjams and communication lapses. It also leaves more room for error.
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As companies measure the health and performance of their IT/cloud/application infrastructure and various data-in-motion streams such as metrics, events, logs, and traces, the number of monitoring tools grows, resulting in a complicated and overloaded observability stack. Kishore Gopalakrishna, co-founder and CEO at StarTree, believes unified observability platforms can solve these pain points by providing the flexibility, data autonomy, and extensibility offered by a disaggregated model.
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We decided this Sourcebook was timely, given how enterprise AI has caught mainstream attention and triggered the imaginations of those working in IT, knowledge management, and a score of other areas within the enterprise. AI is dominating headlines, and people want to know how they can best apply the various technologies under the AI banner.
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