SAP retail chief: Why more retailers need to harness data differently

  • “Yet with SAP’s 2018 Digital Transformation Executive Study finding just 3 per cent of retailers have completed digital transformation projects, most of which focused on efficiency and cost cutting, there’s clearly still a long way to go before brands successfully harness digital for more disruptive innovation in the retail experience.
  • Again, however, only a small percentage of data is being used by retailers in decision making. While these organisations tend to collect as much data as they can, squirrelling away for future use, they’re still not able to ascertain its significance, Schneider agreed.
  • An example is Adidas in Russia, which used existing cameras plus RFID readers and RFID tags on garments and products to improve real-time inventory accuracy in its physical store from 60 per cent to 99 per cent. Adidas is now looking to rollout the approach globally.”

https://www.cmo.com.au/article/659103/sap-retail-chief-why-more-retailers-need-harness-data-differently/

Seizing change in seas of change: a 2019 compass for AI in banking

  • “Despite their heroic work to date, financial services firms will face the same competitive and regulatory challenges in 2019 as in the past years.
  • Navigating around the three-headed beast—competitive disruption from other established firms, fintechs and new digital-only banks—represents another big issue.
  • Thus the overarching mission for at least the next year involves digital transformation. Enter artificial intelligence (AI), which promises to forever transform banking and propel the industry deeper into the digital age.
  • Less analytically mature organizations are just catching up to traditional “big data” challenges
  • At the same time, consumers are becoming more savvy and cautious regarding the use of their data.
  • Banks will continue to adopt AI and machine learning technologies in 2019. Why? Perhaps the most urgent reason centers on “in-the-moment speed.” Despite having plenty of customer data, banks by and large lack the capacity for instant analysis and interpretation.
  • Inexpensive technology to process billions of transactions is commonplace—but extracting value and insights from that data remains difficult.”

https://www.bai.org/banking-strategies/article-detail/seizing-change-in-seas-of-change-a-2019-compass-for-ai-in-banking

One common data management theme has emerged in 2019: that data equals the new energy source that runs modern businesses

DATA’S GROWING ROLE IN SCALABLE ECOMMERCE​

    • “In the US, for example, ecommerce currently accounts for approximately 10% of all retail sales, a number that’s projected to swell to nearly 18% by 2021.
    • Financial analysts predict the retail giant (Amazon) will control 50% of the US’ online retail sales by as early as 2021,
    • In order to shift ecommerce from a product-centric to a customer-centric model, ecommerce companies need to invest in unifying customer data to inform internal processes, and provide faster, smarter services.
    • Data becomes valuable as it provides insights that allow companies to make smarter decisions based on each consumer
    • Data holds the key to this revolution. Instead of trying to force their agenda upon customers or engage in wild speculations about customer desires, ecommerce stores can use data to craft narratives that engage customers, create a loyal brand following, and drive increasing profits.
    • With only about 2.5% of ecommerce web visits converting to sales on average, ecommerce companies that want to stay competitive must open themselves up to big data and the growth opportunities it offers.”

    https://dataconomy.com/2019/03/datas-growing-role-in-scalable-ecommerce%E2%80%8B/

    Microsoft Excel adds photo-to-spreadsheet feature with latest update

    “Microsoft Excel for Android has been updated with a nifty new feature that was originally teased at a conference last year. If you’re in a rush and need to turn a photo or image into a spreadsheet that you can actually edit, this is now your go-to app.

    The update uses image recognition and AI to snap a photo of something like a nutrition label, then turn it into a spreadsheet full of data and separated columns for you to edit and manipulate.”

    https://www.talkandroid.com/337892-microsoft-excel-adds-photo-to-spreadsheet-feature-with-latest-update/

    AI & Data: Avoiding The Gotchas

    Image:Getty

    “When it comes to an AI (Artificial Intelligence) project, there is usually lots of excitement.

    But in this process, something often gets lost: The importance of establishing the right plan for the data. Keep in mind that 80% of the time of an AI project can be spent on identifying, storing, processing and cleansing data.

    According to Stuart Dobbie, who is the Product Owner at Callsign: ‘Fundamentally, the core recurring problem remains simple: many businesses fail to clearly articulate their business problem prior to choosing the technologies and skill-sets required to solve it.'”

    https://www.google.com/amp/s/www.forbes.com/sites/tomtaulli/2019/02/09/ai-data-avoiding-the-gotchas/amp/

    Artificial intelligence startup Databricks is now worth $2.75 billion after raising $250 million from Andreessen Horowitz and Microsoft

    “‘Many companies are excited to AI, but they’re struggling. It’s because the problems they’re trying to solve is different from sexy A.I.,’ Ali Ghodsi, CEO and co-founder of Databricks, told Business Insider. “We’re the only company that focuses on how can you do the boring things and A.I. together. We don’t see any vendors out there that try to do that.”

    Ghodsi says that Databricks has been able to grow so fast because there aren’t many other companies doing the same thing. Databricks has a platform that supports both machine learning algorithms and data. Meanwhile, he says, most companies only focus on one or the other, leaving companies to do the manual work of stitching both together.”

    https://www.lmtonline.com/technology/businessinsider/article/Artificial-intelligence-startup-Databricks-is-now-13590472.php

    A.I. Policy Is Tricky. From Around the World, They Came to Hash It Out.

    • “In the view of Mr. Pailhès and others, China is a government-controlled surveillance state. In the American model, coming from Silicon Valley in California, a handful of internet companies become big winners and society is treated as a data-generating resource to be strip mined.
    • The era of moving fast and breaking everything is coming to a close,” said R. David Edelman, an adviser in the Obama administration and the director of the project on technology, policy and national security at M.I.T.
    • One specific policy issue dominated all others: the collection, handling and use of data.”

    https://www.google.com/amp/s/www.nytimes.com/2019/01/20/technology/artificial-intelligence-policy-world.amp.html

    AI, automation and analytics: 3 critical strategies for CMOs in 2019, and beyond

    • “Technology now accounts for a whopping 29 percent of the total marketing expense budget, making martech the single largest area of investment when it comes to marketing resources and programs
    • Investments in AR and VR, a small segment of AI, are expected to grow from $11.4 billion in 2017 to $215 billion in 2021, according to IDC.
    • Meanwhile, 56 percent of senior AI professionals peg a lack of qualified workers as the single greatest barrier to AI implementation (Ernst & Young).
    • The volume of data created worldwide is growing at a staggering 40 percent per year, feeding a near-unimaginable level of intelligence into organizations attempting to make sense of and activate it.
    • It’s the next step in automation that gets exciting, and that is the combination of automation and AI. Intelligent automation takes the efficiency and productivity of automation and layers on data analysis, decision-making and even prioritizing next steps.
    • Marketing and customer analytics have been named by 40 percent of CMOs as the top capabilities needed to support the delivery of their marketing strategies over the next 18 months.
    • At least 99 percent of big data is not even analyzed, according to the IDC.
    • Today, deep learning allows us to take massive and sometimes unstructured datasets and reap actionable, relevant insights.
    • What’s more, predictive analytics activates those insights by facilitating real-time optimization, ongoing measurement, and further optimization.”

    https://marketingland.com/ai-automation-and-analytics-3-critical-strategies-for-cmos-in-2019-and-beyond-254587https://marketingland.com/ai-automation-and-analytics-3-critical-strategies-for-cmos-in-2019-and-beyond-254587

    How the Marketing Technology Landscape Will Transform in the New Year – Adweek

    • “Gartner correctly predicted that by 2017, the CMO would spend more than the CIO on IT.
    • However, the cost of acquiring a new customer is five times greater than that of retaining an existing one, and the probability of selling to an existing customer is 60 percent, while the probability of selling to a new prospect is 5 percent.
    • As AI continues to revolutionize the digital landscape, marketers will increasingly depend on this technology to maximize consumer reach and sales
    • AI within marketing technology means more effective customer segmentation, advanced and updated customer analytics and efficient marketing strategies. This will create a domino effect where businesses will want to compete in order to capture consumer demand, leading to the further rise of AI in marketing.
    • Traditional marketing is shifting toward reliance on data analytics, virtual platforms and machine learning through 2019.”

    https://www.google.com/amp/s/www.adweek.com/programmatic/how-the-marketing-technology-landscape-will-transform-in-the-new-year/amp/

    The 2018 AI Index report is here!

    These are some key graphs and data elements that I’ve selected from the AI Index 2018.

    “The AI Index is an effort to track, collate, distill and visualize data relating to artificial intelligence.

    It aspires to be a comprehensive resource of data and analysis for policymakers, researchers, executives, journalists and the general public to develop intuitions about the complex field of AI.

    The graph below shows the number of active venture-backed U.S. private startups in a given year. The blue line (left-axis) shows AI startups only, while the grey line (right-axis) shows all venture-backed startups, including AI startups. The graph plots the total number of startups in January of each year. Excluding instances where startups are removed from the data set (see appendix for details), the number of startups is cumulative year-over-year.

    From January 2015 to January 2018, active AI startups increased 2.1x, while all active startups increased 1.3x. For the most part, growth in all active startups has remained relatively steady, while the number of AI startups has seen exponential growth.

    The graphs below and on the following page show the results of a McKinsey & Company survey of 2,135 respondents, each answering on behalf of their organization. The graph displays the percent of respondents whose organizations have embedded AI capabilities in at least one function or business unit. Respondents can select multiple AI capabilities. See data for Asia Pacific, India, Middle East and North Africa, and Latin America on the next page.

    While some regions adopt certain capabilities more heavily than others, AI capabilities are adopted relatively equally across regions. We look forward to tracking how company adoption changes over time.

    The graphs below and on the following page show the results of a McKinsey & Company survey of 2,135 respondents, each answering on behalf of their organization. The graph shows the percent of respondents whose organizations have piloted or embedded AI capabilities within a particular business function. Respondents can select multiple functions. See data for Manufacturing, Supply-chain management, and Risk on the next page.

    Organizations tend to incorporate AI capabilities in functions that provide the most value within their industry. For example, Financial services has heavily incorporated AI in Risk, while Automotive has done so in Manufacturing, and Retail has done so in Marketing / sales. This implies that the rate of AI progress for specific applications (e.g., Manufacturing) will likely correlate to uptake in industries where that specialization is particularly important.”

    http://cdn.aiindex.org/2018/AI%20Index%202018%20Annual%20Report.pdf