Machine Learning Everywhere – the new normal for competitive advantage
Learn how to put the power of data science and machine learning to work for your business.
Welcome
The data explosion is no longer on the horizon. It is here. Just a few years ago, businesses were talking about the scarcity of data. Now, the Internet of Things and wearable technology have turned people and devices into data-generating machines that can yield a treasure trove of insights about people and organizations. And data science is the most effective way to derive value from all of these data sources.
There has never been a better time to move your organization forward with data science. Open source software tools afford users new levels of power and agility, and can meet analytical demands in ways many traditional solutions cannot. Massive datasets are widely available from government agencies, non-profit organizations and academic institutions. Powerful capabilities such as machine learning are within reach for just about any organization with the right people, data and tools.
All of these advancements offer tremendous opportunity. However, many organizations may not know the best way to leverage them for business growth and competitive advantage. In these articles, featuring new research from Gartner, we explore how to put the power of data science and machine learning to work at driving results in your organization.
IBM Analytics Content
Machine Learning – The competitive advantage for your business
Artificial intelligence (AI), a scientific discipline that empowers machines to comprehend, learn and act, is transforming and reinventing how businesses operate. When implemented as part of a holistic data science strategy, AI helps organizations transform customer experiences, improve productivity, lower costs and create new growth opportunities. AI is often injected into modern applications to assist humans, such as recommending specific products or services to buy, or helping them perform tasks faster and more efficiently. [...]
Succeeding with data science takes a holistic approach
From large enterprises to small businesses, nearly every organization recognizes the benefits they can achieve with data science. Whether looking for patterns in financial transactions to better detect fraud, mining social media posts for customer sentiments or using telecommunications data to improve cell phone networks, business leaders realize their data holds the key to competitive advantage. [...]
Data science for all – empowering business with visual approaches to data science
Data is an unstoppable force that is transforming industries before our eyes. Decision makers now have access to more data from more sources than ever before, including IoT, weather, mobile and traffic data. Organizations that can uncover new trends and opportunities in big data and apply that knowledge to differentiate themselves will be the ones to lead in their sectors. [...]
Five Ways Data Science and Machine Learning Deliver Business Impacts
19 October 2017
Data science and machine learning can have profound impact on a business, and are becoming critical for differentiation and sometimes survival. Being able to quickly identify that impact into one of five categories this research presents will help data and analytics leaders further drive results.
Impacts
- Data and analytics leaders should understand the powerful and tangible business benefits that data science projects can deliver prior to rounding up and engaging with their analytical teams, to have a greater chance of success.
Recommendations
Data and analytics leaders responsible for analytics and BI strategies should:
- Ensure that senior data scientists are part of innovation projects – only then can you be sure not to miss out on innovations that could be framed as data science projects.
- Use your data science team to support production teams for continuously improving enterprisewide model management and performance monitoring.
- Create a portfolio of analytical scenarios and use cases, including those that your organization is already executing or planning, to better rationalize funding decisions for data science projects. [...]