Profile of Pierre DeBoisFounder, Zimana
Member Since: 7/6/2017
News & Commentary Posts: 43
Pierre DeBois is the founder of Zimana, a small business analytics consultancy that reviews data from Web analytics and social media dashboard solutions, then provides recommendations and Web development action that improves marketing strategy and business profitability. He has conducted analysis for various small businesses and has also provided his business and engineering acumen at various corporations such as Ford Motor Co. He writes analytics articles for AllBusiness.com and Pitney Bowes Smart Essentials and contributes business book reviews for Small Business Trends. Pierre looks forward to providing All Analytics readers tips and insights tailored to small businesses as well as new insights from Web analytics practitioners around the world.
Articles by Pierre DeBois
IT teams must work with managers who oversee data scientists, data engineers, and analysts to develop points of intervention that complement model ensemble techniques.
Gaps in data quality, particularly due to supply chain issues during the pandemic, is becoming a serious influence on planning effective machine learning models.
Planning a data model takes a clear look at how variables should be used. A few techniques like factor analysis can help IT teams develop an efficient means to manage a model. Here’s how.
A gift usually is given to you neatly wrapped. Data, however, is rarely a gift that is prepared with similar care. Here are some concepts on how to keep ML models in production with balanced data.
As CI/CD flourishes to aid ML development, IT professionals have several options to learn about pipelines and maintaining data model reliability. Here’s an overview.
Accountability for diversity and inclusion revolves around data. The ability to manage that data can raise thorny questions over which executive should lead the way.
Here's how IT leaders can better manage the bias that becomes embedded in algorithms and datasets to help increase opportunities for minority tech professionals.
Maintaining ethics means being alert on a continuum for issues. Here’s how IT teams can play a pivotal role in protecting data ethics.
The high interest in the developer community to explore TensorFlow capabilities holds even higher potential to yield valuable insights in quantum computing research and applications.
Remote work and communication styles that arise with it will not disappear after the current pandemic concerns ease. Here’s how IT teams can play a vital role in managing this.
Technical debt, a developer’s term for trade-offs, is starting to impact marketing tech and other choices. Here’s how IT teams can reduce the impact of technical debt choices.
A key to enterprise success with predictive analytics is to get the IT group involved, and do it early in the process.
Take the DevOps concept a step further and see how DataOps can improve data quality and customer experiences.
Digital video is popular, particularly for customer-facing applications. Here is what managers should consider to make video usage effective and efficient.
Time series is a standard analysis, but advanced machine learning tools introduce statistical techniques for more accurate forecast models.
Just because an analyst hits the ground running does not mean that analyst has the ongoing breadth needed to solve every day analytics problems.
Sample size affects the statistical results for correlations, regressions, and other models. See how to compare data to ensure an apples-to-apples comparison.
Learn how research of predictive analytics can impact models used in the transportation sector.
Continuous integration and continuous deployment are IT practices that encourage testing code often. Learn how these practices also shape data-driven initiatives.
Data is important, yet processing data sometimes can make teams feel like they're chasing their own tails to unlock its value.
Automaker experiments with vehicle tech offer established businesses lessons for managing machine learning and DevOps influences on products and operations.
The Internet is not fake, but protecting the value derived from online activity must account for social engineering. Here’s why.
Machine learning classification needs accurate data to avoid bad prediction that places marketing efforts in ethical peril. Here's an overview to help explain how data can brands at risk.
Businesses are leveraging the data gathered through mobile ordering and delivery. Here's how.
DataOps IS a methodology to design, implement, and maintain a distributed data architecture. Its purpose is to provide teams with a process framework to manage quality improvements in data and to reduce cycle time for data analysis.
With rising concerns about how a message is conveyed on social media, marketers are considering micro-influencers as a branding alternative to attract consumers in niche markets. Here's how the choice complements analytics strategy.
It's no longer a struggle to get management to invest in analytics programs. But new challenges have arrived. Here's a closer look at the state of analytics today.
A Chief Data Visualization Officer can help your analytics team tell a useful story with data and establish the right measurement framework for your organization.
Analytics pros from many different industries employ clustering when classification is unclear. Here's how they do it.
As content marketing becomes important, aging content becomes a concern. Here are some ways of using analytic reporting to develop content ideas and to align content with customers in the sales cycle.
Self-service analytics is becoming a popular option, but there are some steps professionals should use to make sure that they are interpreting results correctly, working well with others, and avoiding mishaps in reporting.
Amazon is making an impact in voice search with its Amazon Echo and Alexa ecosystem of products. The rise of Alexa shows how machine learning strategy ultimately becomes an effective sales and business strategy.
The rise of social media has led to a drive to understand all the new data it generates. Here's a look at how sentiment analysis can be applied.
Undetected data bias can render a launched predictive prototype model useless. don't let it happen to you. Here's a look at the biases possible and where marketers and agencies should start in their efforts to detect bias.
Amazon is establishing machine learning as a crucial competitive element to personalize customer experience and build sales.
Technology skills can take you through multiple careers that make lives, businesses, and communities better. Here's the story of one such career journey.
Are your digital marketers creating gaps in customer privacy? They could be unless they vet tags and third-party app access. Here's how to do the vetting.
The rise of Amazon Alexa and Google Home introduce a new competitor for search engines -- IoT home devices with supporting AI. Here's how analytics planning must change to accommodate the new world.
The struggle to learn how customers engage with IoT devices as part of customer experience is real. Here is how marketers can approach strategies with analytics reporting that monitors related traffic in real time.
The rise of services based on artificial intelligence is adding more variety and reporting challenges in analytics. Here are tips for marketers to look for in managing AI influenced metrics and reports.
IS an emphasis on encouraging managers to understanding technology masking a need to understand a management framework that mesh digital and real-world concerns?