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Organizations must prioritize strong data foundations to ensure that their AI systems are producing trustworthy, actionable insights. In Session 2 of our Analytics AI-ssentials webinar series , Zeba Hasan, Customer Engineer at Google Cloud, shared valuable insights on why dataquality is key to unlocking the full potential of AI.
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Rapid advancements in artificial intelligence (AI), particularly generative AI are putting more pressure on analytics and IT leaders to get their houses in order when it comes to datastrategy and data management. If you go out and ask a chief data officer, a head of IT, ‘Is your datastrategy aligned?’,
Wartons Navigating Gen AIs Early Year Report says 57% anticipate slower AI spending increases, an indicator that enterprises are still searching for ROI on their initial investment. For AI to deliver safe and reliable results, data teams must classify data properly before feeding it to those hungry LLMs.
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Or even better: “Which marketing campaign that I did this quarter got the best ROI, and how can I replicate its success?”. These key questions to ask when analyzing data can define your next strategy in developing your company. As Data Dan reminded us, “did the best” is too vague to be useful. Giving the most ROI?
Before we jump into a methodology or even a datastrategy-based approach, what are we trying to accomplish? Agility as a concept in business is really powerful and certainly deserves a place in every data and analytics team.”. DataOps Maximizes Your ROI. Automate the data collection and cleansing process.
I recently led an online session, Data Monetisation and Governance , looking at the evolution of data governance , defining data ethics (from the Turing Institute ), and touching on the balancing act between using data to monetise (by increasing revenue, decreasing spend, or mitigating risk) and meeting ethical obligations.
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Anmut’s own clients estimate that poor dataquality and availability causes at least 16% additional cost per year. Worse still, these organisations’ competitors are actually pouring twice as many resources into creating value from their data assets, giving them a massive advantage.
Most organisations undergoing a digital transformation understand that data is critical, but how many are actually managing data as an asset ? While businesses are happy to make investments in their underlying technology to become more data-driven, they could fail to realise an ROI because their data assets are poorly managed.
AI adoption requires a proactive approach; you need to set the objectives, identify the key performance indicators or KPIs, and track ROI to assess and track the growth of AI. Strong Data-Driven Culture. Data is at the core of AI. AI adoption can generate quality results if it can utilize data properly. Compliance.
Risk of data swamps A data swamp is the result of a poorly managed data lake that lacks appropriate dataquality and data governance practices to provide insightful learnings, rendering the data useless. Key steps include: Define business and data objectives –What are your company’s goals?
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CIOs are being viewed as business strategists who can navigate AIs impact, manage outsourced IT functions, and drive ROI and measurable business value, she says. Still, many organizations arent yet ready to fully take advantage of AI because they lack the foundational building blocks around dataquality and governance.
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