An AI Maturity Roadmap for Becoming a Data-Driven Organization

Editor's Notes

• #9: For too long – data has been held captive within our systems of record. Isolated by the rigidity of platform/application/workload choices, segregated by business line, business function, and data type or initial usage. The result is splintered views of segmented data that’s difficult to access on the whole, and impossible to attempt to gain true analytical insight from….. And even this only speaks to the snapshot today and current models. The challenges are compounded as businesses look to change, grow, iterate practices, innovate, or disrupt markets. Attempts at data science, machine learning, and deep learning are made moot by the fact that insights are only as good as the access to supporting data – which again is too fragmented to provide full value. We believe, that in order to change this paradigm, a hybrid data management strategy should contain the elements here: Access to all data regardless of source or type The flexibility to support changing workloads and consumption cases Possess intelligent analytics such as machine learning AT the data source And… Provide access to insights across the business, its functions, and to all users for better decision making
• #10: # # # You need three essential elements on your journey to digital transformation. You need to know your data. Typically this means building a 360-degree view of your focus area—for example, a 360-degree view of your customer. You need to gather your internal data and may also need to include external data from social media, click stream, census or other relevant sources. This data must also be accessible by all users and/or applications that need it. This could mean making data globally accessible or running applications in the cloud. Consider that an application may need to access data from multiple data sources, so providing a common access layer is important to reduce application coding.  2. You need to be able to trust your data. Well-governed data provides confidence in not just the data itself, but the outcomes from analytics, reports and other tasks based on that data. There are two key points to data governance: First, you must have the ability to ensure the data is secure and adheres to compliance regulations. And second, you must have the ability to govern the data so your users can find and access information themselves, at the exact time they need it. 3. You must be able to use your data as a source for insights and intelligence. This means having not only the right skills and tools in place to surface insights, but also the right technology to learn from the data and improve accuracy each time that data is analyzed.
• #13: Three years ago Watson made it’s debut on the US Quiz show, Jeopardy, in a very public proof point of radical new technology. Jeopardy was the result of an IBM Grand Challenge – putting top scientists to work on a seemingly impossible task. IBM undertakes Grand Challenges every decade or so. The last grand challenge was “Deep Blue” in 1997 - a chess-playing computer that won the second six-game match against world champion Garry Kasparov by two wins to one with three draws. Whether you attended one of the many IBM watch parties, watched the show at home, viewed it on YouTube later, or just read the newspapers, you witnessed history. Watson bested the 2 top champions, including Ken Jennings, who won 74 games and over $3M – the longest winning streak in J history. Not only did Watson win, but in doing so it ushered in a whole new era of computing. Additional Background: What fascinated the IBM researchers was how Jeopardy was the ultimate test of IT capabilities because it relied on many human cognitive abilities traditionally seen beyond the capability of computers, such as: The ability to discern double meanings of words, puns, rhymes, and inferred hints. Extremely rapid responses (sifting through 200 million pages of information - in the span of seconds) The ability to process vast amounts of information to make complex and subtle logical connections A team of 15 IBM researchers working in collaboration with a pool of top universities as a “Deep QA” project. For the Watson team, replicating the human capabilities was an enormous challenge, moving beyond keyword searches and queries of structured data to asking questions and accessing and assessing a vast amount of unstructured data to find the best answer. But IBM that knew the solution to this challenge had the potential to change the way businesses use information and make decisions.
• #14: APIs in the cloud. FREE to play with today.
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