Becoming an agile organization will get you to your destination faster. To choose the right destination, you must make accurate decisions about which opportunities to pursue, based on reliable data.
We can improve your organization’s decision-making by automatically turning large amounts of data into clear, actionable insights using data science.
Investing in the wrong opportunities can have major, long-term consequences.
In recent years, new technology has made data collection easier and cheaper than ever, increasing exponentially the amount of data available to organizations.
While the volume of data that organizations have at their disposal is larger than ever, few are utilizing this data to its full potential in terms of revenue and profit gains.
Competing in the digital age requires the ability to glean meaningful insights from this data, spot risks and inefficiencies, predict market trends, and quickly tailor solutions to customers’ changing needs.
Unfortunately, most companies still struggle to collect the right kinds of data, derive actionable insights, and then deliver those insights to the right people in the organization who can use them to drive revenue and profit gains.
Your organization can gain a huge competitive advantage if you are able to quickly and accurately predict market trends, identify new revenue streams, and anticipate product popularity. Being first-to-market with innovative products that best meet customers’ needs will lead to enormous gains in demand, revenue, and market share.
However, if you rely on guesswork or incomplete data, you risk investing heavily into the wrong technology and developing inadequate products that quickly lose relevance. Over the long-term, such missteps will inevitably lead to massive losses in market share.
The financial industry typically uses machine learning technology in two key areas: customer data insights and fraud prevention.
A typical use case is utilizing machine learning to assess consumer credit risk, and then trying to predict if a consumer is likely to be delinquent or defaulting on their credit card debt.
“Robo-advisors” are also disrupting financial services organizations in surprising ways. Robo-advisors almost entirely eliminate the need for human financial advisors. Robo-advisors now collect all of the customer’s financial information - including their financial goals and accepted level of risk - and then automatically output highly-accurate financial advice based on complex algorithms and large amounts of data. These Robo-advisors can also automate the purchasing and management of investments based on predefined rules chosen by the customer.
Multiple forms of artificial intelligence are now being combined in order to make highly-accurate market predictions. AI is now sophisticated enough that it can evaluate public remarks from an organization (earnings calls, for example), and then analyze them using NLP to determine meaning, sentiment, tone, speech patterns, etc. This information, along with historical data, is then used to predict future stock performance to an astonishing degree of accuracy.
In June of 2018, a study in the Annals of Oncology showed that a convolutional neural network trained to analyze dermatology images identified melanoma with ten percent more specificity than human clinicians.
Separately, researchers at the Mount Sinai Icahn School of Medicine have developed a deep neural network capable of diagnosing crucial neurological conditions - such as stroke and brain hemorrhage - 150 times faster than human radiologists.
Due to the advent of wearable devices and sensors, machine learning is a fast-growing trend in the healthcare industry. Medical experts can now analyze data to identify trends or identify issues that may lead to improved diagnoses and perhaps preventative treatment.
Robotics technology will also continue to make a big impact on the healthcare industry. Surgical robots, such as the da Vinci system, have been in use for years. However, these machines are entirely controlled by humans, not artificial intelligence. This won’t always be the case.
The Smart Tissue Autonomous Robot (STAR) can already suture stitches more precisely and cleanly than a human surgeon. Early tests have shown that the same technology can also be used to remove tumors in a less damaging and invasive manner than human surgeons are capable of.
The eCommerce industry has embraced all kinds of Artificial Intelligence technologies to meet customer needs faster and improve the customer experience as a whole time, while increasing revenue and profitability at the same time.
One of the most popular uses of Artificial Intelligence in the eCommerce industry is chatbots. Chatbots are computer programs that have been designed to communicate with shoppers in a friendly, personalized manner. Chatbots can do things like answer shopper’s questions, offer useful suggestions, and perform various tasks, such as initiating a return for an order placed online.
Predictive sales is the process of collecting large amounts of data on customer behavior in order to determine what kinds of products are in high demand. This information is then used to inform product development, purchasing, and inventory management decisions. In fact, the hit Netflix show House of Cards was the result of utilizing this kind of Data Science.
When it comes to driving repeat purchases and increasing profitability, few things are as effective as suggesting highly-relevant products to customers at specific times when they are most likely to buy. Data Science is used to analyze a customer’s recent purchases and searches to determine what additional products they’re most likely to be interested in. Then, these highly-relevant products are displayed to the customer when they are already in a “buying” mindset with the goal of increasing repeat purchases.
Amazon's Alexa shows what is possible with voice NLP. Users can speak out loud into Amazon’s Echo speaker to ask Alexa to perform any number of a growing number of tasks, such as to tell a joke, play a song, give the weather forecast, or order products directly from Amazon.com. Google, Microsoft, Apple, and China’s Baidu also have voice-based assistants of this kind.
When it comes to robotics, Amazon is also leading the way when it comes to automating warehouse tasks, such as parcel sorting, packaging, and categorizing.
Governments all over the world are looking for ways to utilize data science to analyze large amounts of data, improve decision-making, predict natural disasters and external threats, reduce crime, increase efficiency, and cut costs.
One of the most obvious uses of Data Science is to sift through large amounts of data to predict natural disasters, efficiently allocate resources during rescue efforts, and identify external threats and crime patterns. In addition, robots can be used for high-risk tasks, such as drone surveillance and bomb disposal.
When it comes to public health, machine learning can be used to detect and even predict epidemics such as food poisoning and outbreaks of infectious disease. The earlier these outbreaks are detected, the earlier they can be contained and eliminated.
The U.S. Government is also the single largest employer in the world. Hiring talent is one of the most costly burdens an employer deals with in terms of both time and resources. Data Science can help the government streamline and automate the recruitment and hiring process, including identifying qualified talent, evaluating resumes, and scheduling interviews.
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