Friday, December 6, 2019
Business Intelligence and Analytics 41
1 A Simple Three-Step Process For Generating Value Through Business Intelligence
https://www.forbes.com/sites/forbesbusinesscouncil/2019/11/21/a-simple-three-step-process-for-generating-value-through-business-intelligence/
As data becomes more readily available, organizations face the question of how to effectively manage and use it in a competitive landscape. Many companies are realizing the need for and value of collecting, storing and analyzing data. Just look at a couple of examples of market activity in various industries: In December 2018, Nasdaq acquired Quandl, which is a company that “offers alternative data to over 30,000 monthly users compiled from roughly 350 sources,”
2 How CEOs can help lead technology transformations
https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/the-ceos-new-technology-agenda
We’ve seen numerous companies boost their financial performance after their CEOs made it a priority to strengthen the technology function and bring more technology capabilities closer to the business’s strategy and operations. Fulfilling this mandate, however, can be a challenge.
3 Best Artificial Intelligence Technologies to know in 2019
https://www.datasciencecentral.com/profiles/blogs/best-artificial-intelligence-technologies-to-know-in-2019
Technology decision-makers are (and also should keep) seeking methods to successfully carry out artificial intelligence innovations into their businesses and, therefore, drive value. And though all AI innovations most definitely have their own merits, not all of them deserve purchasing, with each passing day we come across a number of AI development techniques.
4 Top 7 Data Science Use Cases in Trust and Security
https://www.kdnuggets.com/2019/12/top-7-data-science-use-cases-trust-security.html
What are trust and safety? What is the role of trust and security in the modern world?
We often come across this word combination ‘Trust & Safety’ on numerous web sites and platforms. It is called upon to regulate the interaction between the visitors and specialists so that it would be fair and peaceful.
5 How B2B online marketplaces could transform indirect procurement
https://www.mckinsey.com/business-functions/operations/our-insights/how-b2b-online-marketplaces-could-transform-indirect-procurement
For people who remember when consumers first started using digital marketplaces, the realization that the oldest of those platforms are now almost 25 years old can come as a surprise. Even business-to-business platforms have been around for two decades, becoming an almost invisible engine of commerce at many small and medium-sized businesses.
6 Digital Transformation Lags In Chemical Industry
https://www.chemicalprocessing.com/articles/2019/digital-transformation-lags-in-chemical-industry/
At a recent ARC Industry Forum many discussions revolved around digital transformation. According to a presentation by Mike Williams, ARC associate, although industry research indicates that there has been more than 75% of the process industry participating in Industry 4.0 technology evaluation or pilot projects, there is still less than 25% of the industry moving beyond the pilot phase. It would appear that the process industries are lagging behind other industry segments such as automotive and other discrete manufacturers.
7 Machine learning can boost the value of wind energy
https://blog.google/technology/ai/machine-learning-can-boost-value-wind-energy/
Carbon-free technologies like renewable energy help combat climate change, but many of them have not reached their full potential. Consider wind power: over the past decade, wind farms have become an important source of carbon-free electricity as the cost of turbines has plummeted and adoption has surged. However, the variable nature of wind itself makes it an unpredictable energy source—less useful than one that can reliably deliver power at a set time.
8 The platform play: How to operate like a tech company
https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/the-platform-play-how-to-operate-like-a-tech-company
“The question is not how fast tech companies will become car companies, but how fast we will become a tech company.” This is how the board member of a global car company recently articulated the central issue facing most incumbents today: how to operate and innovate like a tech company.
9 Will personalization’s role in marketing shrink as challenges grow?
https://www.marketingdive.com/news/will-personalizations-role-in-marketing-shrink-as-challenges-grow/568607/
Eighty percent of marketers will drop personalization efforts by 2025, according to a report from research firm Gartner, “Predicts 2020: Marketers, They’re Just Not That Into You.” The key reasons cited for why marketers could retreat from personalization are a lack of ROI and difficulties with managing customer data.
10 Industry Averages, Benchmarks and the Death of Innovation
https://www.datasciencecentral.com/profiles/blogs/industry-averages-benchmarks-and-the-death-of-innovation
Sustaining industry averages and benchmarks are the antithesis of innovation and a great way to ensure average performance. Doing whatever everyone else is doing is a “paving the cow path” management mentality, lacking aspirational goals which are critical for organizations to fuel innovation and create customer and market differentiation. Which brings me to why I teach.
11 When Is The Right Time To Personalize?
https://about.crunchbase.com/blog/personalization-in-sales/
Undoubtedly, automation should play a vital role in every business’s sales funnel. Automation takes care of manual, time-consuming tasks and frees up time for brands to focus on other essential areas. It also increases efficiency, improves accuracy, and speeds up the sales process.
12 Turn around a stalled oil and gas digital transformation
https://www.mckinsey.com/industries/oil-and-gas/our-insights/rebooting-your-stalled-digital-transformation-in-oil-and-gas
Does this sound like your company? You’ve been investing in digital and advanced-analytics solutions for at least two or three years. You’ve set priorities, given them plenty of leadership attention, and hired new talent to build your organization’s capabilities. You’ve invested in new technologies, software solutions, and external support to make sure you’re on the right path. In short, you’ve done everything you can to make your digital transformation a success. Yet you can’t help feeling that it is stalling.
13 The Best of This Week
https://sloanreview.mit.edu/article/best-of-this-week-big-tech-cybertruck/
The week’s must-reads for managing in the digital age, curated by the MIT SMReditors.
14 Top 10 Data Science Use Cases in Energy and Utilities
https://www.kdnuggets.com/2019/09/top-10-data-science-use-cases-energy-utilities.html
The energy sector is under constant development, and more of significant inventions and innovations are yet to come. The energy use has always been involved in other industries like agriculture, manufacturing, transportation, and many others. Thus these industries tend to enlarge the amount of energy they consume every day. Energy seems to be very demanding in terms of new technologies application and development of new energy sources.
15 “The Boom In Sales Technology”
https://www.business2community.com/sales-management/the-boom-in-sales-technology-02262847
I suppose it’s normal to see the flood of articles, prospecting emails, calls and such on “sales technology.” Dreamforce is over, we all have visions of our technology based futures dancing in our heads.
We see the MarTech and other charts, with 1000’s of suppliers, in unimaginable niches, giving us the most essential sales and marketing technologies, guaranteed to drive performance and customer engagement.
16 Exploratory Data Analysis …A topic that is neglected in Data Science Projects
https://towardsdatascience.com/exploratory-data-analysis-topic-that-is-neglected-in-data-science-projects-9962ae078a56
Exploratory Data Analysis (EDA) is the first step in your data analysis process developed by “John Tukey” in the 1970s. In statistics, exploratory data analysis is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. By the name itself, we can get to know that it is a step in which we need to explore the data set.
