Tag: Data

How business intelligence aids digital transformation process

~ Brandon McGee ~ Leave a comment
  • “BI tools quickly and efficiently collect pre-determined sets of data from enterprise systems such as Customer Relationship Management (CRMs), financial management platforms and Human Resource Management System (HRMs). In addition, Robotic Process Automation (RPA) and AI-powered bots can scrape website, spreadsheets and other documents for additional data, both structured and unstructured.
  • For decades, BI could only tell users ‘what is happening’ or ‘what happened’, leaving the decision-making part to humans. However, with the emergence of artificial intelligence (AI), that is rapidly changing. Now, using data mining, machine learning, prescriptive analytics and other innovative technologies, organisations can use BI to uncover and share new, groundbreaking data-driven insights.
  • Predictive analytics is the use of data, statistical algorithms and machine learning (or AI) techniques to identify the likelihood of future outcomes based on historical data. The use of predictive analytics is a key milestone on your analytics journey — a point of confluence where classical statistical analysis meets the new world of AI.”

https://www.standardmedia.co.ke/business/amp/opinion/article/2001443379/how-business-intelligence-aids-digital-transformation-process

How AI is Changing Digital Marketing

~ Brandon McGee ~ Leave a comment
  • “For those of us working in the realm of digital marketing, the impact has become even more clear over the last few years. To put things into perspective, 61% of marketers say AI is the most important aspect of their data strategy, according to MemSQL.
  • Have you ever searched for a particular product and then all advertisements you see after that search are for similar products? That’s AI at work. The power of DMPs (Data Management Platforms) allows AI to gather data from across the Internet – not just a particular website. This data then fuels predictive analytics, which empowers digital marketers to truly personalize their marketing campaign strategies to ultimately generate high-quality leads. Stronger leads = stronger ROI.
  • A Campaign Monitor report found that marketers saw a 760% increase in revenue from segmented campaigns.
  • What’s one AI tool we all encounter on a regular basis? Chatbots. These are used for everything from customer service to driving sales. Chatbots are becoming so sophisticated that according to a study done by Marketing Insider Group, two-thirds of consumers don’t know they’re interacting with these AI tools vs. actual humans.
  • According to a study from Bright Local almost 60% of consumers have used voice search to find business information in the last year. As the world has become largely mobile, voice search enhances the user experience, unlocking hands-free access to accurate data and results.
  • Google’s algorithms can understand human speech with 95% accuracy which is nearly equivalent to human-to-human interactions. With this in mind, embracing a humanistic SEO strategy is more important than ever before.”

https://www.datasciencecentral.com/how-ai-is-changing-digital-marketing/

AI and Data Strategy: Where Do They Intersect?

~ Brandon McGee ~ Leave a comment
Image credit: Shutterstock.com / dataversity.net
  • “Nitish Mittal, a partner in the digital transformation practice at Everest Group, emphasizes this point: ‘I can’t stress this enough: data or the lack of the right data strategy is the number one bottleneck to scaling or doing anything with AI. When clients come to us with what they think is an AI problem, it is almost always a data problem. AI depends on viable data to prosper. That’s why it’s important to think about the data first.’
  • Beena Ammanath, executive director of Deloitte AI Institute, stresses quality over quantity: ‘It’s not enough to say you have 20 years of data. You have to have the right data. You may have high quantities of data, but you may not have the quality you need. Many companies don’t have a data architecture capable of pulling in data from different places and cleaning it up so it’s usable for AI technology.’”

https://www.dataversity.net/ai-and-data-strategy-where-do-they-intersect/

Data50: The World’s Top Data Startups

~ Brandon McGee ~ Leave a comment

“To help cut through the noise after a record-breaking 2021 in which data companies received tens of billions in venture capital investment — and an already strong 2022 — we’ve compiled the inaugural class of the Data50. These are the bellwether companies across the most exciting categories in data. In aggregate, these 50 companies are valued at more than $100B and have raised approximately $14.5B in total capital, with 20 having reached unicorn status by 2021.”

https://future.a16z.com/data50/

Why You Should Think Of AI As A Team Sport

~ Brandon McGee ~ Leave a comment
  • “Domain expertise is key; machines don’t have the depth of context that people have, and people need to know the business and data well enough to understand which actions to take based on any insights or recommendations that are surfaced.
  • The 2021 Gartner® The State of Data Science and Machine Learning (DSML) report states that “client demand is shifting, with less-technical audiences wanting to apply DSML more easily, experts needing to improve productivity and enterprises requiring shorter time to value for their investments
  • With a more iterative, revise-and-redeploy model building processes, people with business context can get value from AI faster—even deploying new models to thousands of users within days to weeks, instead of weeks to months. This is especially powerful for those teams whose unique challenges may not be a high priority for data science teams, but can benefit from the speed and thoroughness of AI analysis.
  • McKinsey’s latest global survey on AI revealed that within 34% of high-performing organizations “a dedicated training center develops nontechnical personnel’s AI skills through hand-on learning,” compared to only 14% of all others surveyed. Additionally, in 39% of high-performing organizations “there are designated channels of communications and touchpoints between AI users and the organization’s data science team,” compared to only 20% of others.”

https://www.forbes.com/sites/tableau/2022/03/10/why-you-should-think-of-ai-as-a-team-sport/amp/