In a digital age, it only takes a few years for research into cutting-edge areas like predictive analytics modelling and artificial intelligence to find practical uses in everyday business contexts. Areas like general usability, user interface, and semantics that are changed to empower a broader cross-section of potential users.

Business-focused users keen to leverage statistical methods may not be capable of or comfortable with interacting in a pure text terminal in Python or R, for example. After all, open-source code is exactly that: can you blame someone for not wanting to make an investment decision based on code found freely on the internet? Business users need an easy way to interface with powerful analytics and need a trusted brand that stands behind them. That’s exactly why Minitab has changed the game, making machine learning easy for everyone.

Today, business users do not have to compromise to do advanced analytics. For statistical analysis, predictive analytics, and machine learning, there is a 50-year-old powerhouse called Minitab.

Over the past couple of years, Minitab has revolutionized the market by bringing the world’s most advanced data gathering, processing, visualizations, and analysis to the masses. Most recently, Minitab broke the barrier to putting “Data Science” into the hands of business professionals. And unlike others who promised this in the past, Minitab delivers.

That’s because, across the company’s product portfolio, there is a strong emphasis on usability and business outcomes – Minitab is deployed by a broad cross-section of the business community.

For decision-makers tasked with a business process or operational improvements, access to data and the ability to use it to achieve clear goals is critical. The lifeblood of today’s organizations is information, so using it to examine what was, what is, and what might happen can result in in lowered costs, higher revenues, and more efficient, timely actions for long-term strategy.

The talk may be of variables and predictors in mathematics and statistics — for the business’s Change Manager, it’s data sources, outcomes, and results. The phraseology might be different, but the required data processing and analysis remain the same. Don’t be intimidated by the term “Machine Learning.” All it refers to is learning from your data, which is effectively what data analysis is. Don’t believe us? Try it yourself.

With Minitab at the core of your data operations, there is immediate “plug and play” access to hundreds of data sources via included connectors that allow companies to access the data silos, repositories and applications across the network and in the cloud. It’s not surprising that Minitab was the highest-rated data integration tool according to Gartner Peer Insights.

Coupled with the statistical core of the Minitab data analysis platform, professionals can get a full picture of their data by leveraging archived information and real-time data streams as they happen in any organization.

Data is cleaned, transformed, and presented, providing the basis for predictive analytics modelling and insights into existing work processes.

As companies begin to scratch the surface of the data resources they have, hidden relationships between events and variables are uncovered. Factors that were never apparent can surface, even to the objective observer, and visual relationships and correlations emerge. Insights gained help both data specialists and line-of-business experts to determine how best to achieve the company’s objectives.

For line-of-business managers and non-data scientists, the visual language of Minitab helps show and correlate the various factors at play. It allows them to create predicted outcomes to proposed changes to operations in safe modelling environments.

The beauty of the Minitab portfolio is its design for use in practical settings. The platform’s openness and user interface mean it can be used in multiple verticals and unexpected use-cases: manufacturer Tate & Lyle used AI techniques and plotted thousands of variables to refine its sweetener consistency for better customer experience, for example. Where one might least expect it, Minitab’s statistical power is creating change.

In a vast range of industries, advanced analytical modelling, analysis, and machine learning algorithms are being deployed by organizations to improve outcomes in thousands of scenarios. At one time, this type of statistical analysis was only seen in finance and high-end medical research and pharma, but not today. Minitab is making real, meaningful differences in thousands of settings.

It integrates with both cloud and on-premises applications and services, from marcomms to stream processors. Minitab installs locally or is now available as a SaaS, ready to be accessed from anywhere with an internet connection.

To learn more about the Minitab suite of offerings and begin leveraging its accessible power to affect change, start your journey here.

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An expert in machine learning and natural language processing discusses how these technologies are enhancing care and enabling the use of SDOH data and personalized analytics.

Some healthcare provider organizations are using machine learning and other forms of artificial intelligence to provide clinicians with the best evidence-based care pathways.

A group’s aim could be to improve a patient’s care plan based on personalized analytics. Another goal could be the further merging of evidence-based care paths with historical utilization and outcomes in order to offer optimal patient care. Provider organizations might be using social determinants of health combined with machine learning to offer clinically meaningful services.

Healthcare IT News talked over these ideas with Niall O’Connor, chief technology officer at Cohere Health, a vendor of artificial intelligence technology and services designed to improve the provider, patient and payer experiences.

Q: How is machine learning being used to comprehensively enhance a patient’s entire care plan based on personalized analytics? And how is machine learning being used to combine evidence-based care paths – with real-world historical utilization, outcomes and the latest literature – to provide first-rate patient care?

A: Evidence-based guidelines are an important component of an intelligent care path solution. In fact, they are the starting point for our models. We would never want to relearn the complexity that has been elucidated in clinical guidelines.

At the same time, guidelines were written for the average patient and can’t possibly accommodate all the comorbidity permutations that exist for patients of high acuity. This is where machine learning can help. For patients that don’t perfectly fit existing evidence-based care paths, we can employ machine learning models to infer what has been the most efficacious path for diagnostically identical patients from real world historical data.

Q: How is machine learning being used to use social determinants of health and patient lifestyle to provide precise and clinically meaningful care?

A: Data regarding social determinants of health (SDOH) and patient lifestyle are not typically captured in standard electronic health records, but diligent physicians typically refer to this type of data in their clinical notes.

We can also supplement models with SDOH data – such as the U.S. Census – that can point to access or other patient challenges and incorporate patient-reported data, whether on lifestyle or health state.

This presents a challenge for typical analysis, so we employ natural language processing to help isolate and interpret references to things like lifestyle impacts or resumption of employment following surgery. Although detection of these phrases isn’t comprehensive, when present they can help provide valuable outcome endpoints for us.

Q: How did machine learning first come to be seen as useful in these areas?

A: Clinical data analysis isn’t a big data problem; it’s a messy data problem and is plagued by the fact that much of the valuable information isn’t readily available in structured form.

Volume also plays a big part in why we use machine learning; when we end up with thousands of attributes, we need machine learning to identify the variables that are driving the model. For SDOH in particular, machine learning will be crucial for variable selection and essential to refining some signal from the noise of operational clinical data.

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The Internet of Things (IoT) and machine learning are two of the most disruptive technologies in business today. Separately, both of these innovations can bring remarkable benefits to any company. Together, they can transform your business entirely.

The intersection of IoT devices and machine learning is a natural progression. Machine learning needs large pools of relevant data to work at its best, and the IoT can supply it. As adoption of both soars, companies should start using them in conjunction.

1. Highlighting and Addressing Inefficiencies

Around 25% of businesses today use IoT devices, and this figure will keep climbing. As companies implement more of these sensors, they add places where they can gather data. Machine learning algorithms can then analyze this data to find inefficiencies in the workplace.

Looking at various workplace data, a machine learning program could see where a company spends an unusually high amount of time. It could then suggest a new workflow that would reduce the effort employees expend in that area. Business leaders may not have ever realized this was a problem area without machine learning.

Machine learning programs are skilled at making connections between data points that humans may miss. They can also make predictions 20 times earlier than traditional tools and do so with more accuracy. With IoT devices feeding them more data, they’ll only become faster and more accurate.

2. Business Process Automation

Machine learning and the IoT can also automate routine tasks. Business process automation (BPA) leverages AI to handle a range of administrative tasks, so workers don’t have to. As IoT devices feed more data into these programs, they become even more effective.

Over time, technology like this has contributed to a 40% productivity increase in some industries. Automating and streamlining tasks like scheduling and record-keeping frees employees to focus on other, value-adding work. BPA’s potential doesn’t stop there, either.

BPA can automate more than straightforward data manipulation tasks. It can talk to customers, plan and schedule events, run marketing campaigns and more. With more comprehensive IoT implementation, it would have access to more areas, becoming even more versatile.

3. Supply Chain Visibility

One of the most promising areas for IoT implementation is in the supply chain. IoT sensors in vehicles or shipping containers can provide companies with critical information like real-time location data or product quality. This data alone improves supply chain visibility, but paired with machine learning, it could transform your business.

Machine learning programs can take this real-time data from IoT sensors and put it into action. It could predict possible disruptions and warn workers so they can respond accordingly. These predictive analytics could save companies the all-too-familiar headache of supply chain delays.

UPS’ Orion tool is the gold standard for what machine learning can do for supply chains. The system has saved the shipping giant 10 million gallons of fuel a year by adjusting routes on the fly based on traffic and weather data.

4. Risk Management

If a company can’t understand the vulnerabilities it faces, business leaders can’t make fully informed decisions. IoT devices can provide the data businesses need to get a better understanding of these risks. Machine learning can take it a step further and find points of concern in this data that humans could miss.

IoT devices can gather data about the workplace or customers that machine learning programs then process. For example, Progressive has made more than 1.7 trillion observations about its customers’ driving habits through Snapshot, an IoT tracking device. These analytics help the company adjust clients’ insurance rates based on the dangers their driving presents.

Business risks aren’t the only hazards the Internet of Things and machine learning can predict. IoT air quality sensors could alert businesses when to change HVAC filters to protect employee health. Similarly, machine learning cybersecurity programs could sense when hackers are trying to infiltrate a company’s network.

5. Waste Reduction

Another way the IoT and machine learning could transform your business is by eliminating waste. Data from IoT sensors can reveal where the company could be using more resources than it needs. Machine learning algorithms can then analyze this data to suggest ways to improve.

One of the most common culprits of waste in businesses is energy. Thanks to various inefficiencies, 68% of power in America ends up wasted. IoT sensors can measure where this waste is happening, and with machine learning, adjust to stop it.

Machine learning algorithms in conjunction with IoT devices could restrict energy use, so processes only use what they need. Alternatively, they could suggest new workflows or procedures that would be less wasteful. While many of these steps may seem small, they add up to substantial savings.

It’s Time to Embrace the IoT and Machine Learning

Without the IoT and machine learning, businesses can’t reach their full potential. These technologies enable savings companies couldn’t achieve otherwise. As they advance, they’ll only become more effective.

The Internet of Things and machine learning are reshaping the business world. Those that don’t take advantage of them now could soon fall behind.

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The world today is bombarded with continuously growing big data sets. This large amount of data is being produced every minute by businesses as well as individuals. Processing such voluminous data requires advanced analytics solutions. This is where big data analytics comes in, playing an indispensable role in analysing big data sets to uncover information.

So, What is Big Data Analytics?

Big Data Analytics refers to a form of advanced analytics and helps excerpt meaningful insights, such as unexplored correlations, hidden patterns, market trends, and customer preferences. It involves complex applications with elements like predictive models, statistical algorithms, and analysis powered by analytics systems.

How Big Data Analytics Can Help Improve Customer Experience?

Effective and successful delivery of customer experience relies on data. This data is typically collected by companies to understand their customers’ behaviours and deliver personalized, on-demand offerings to make consumers happier. This collection of customer data helps businesses with market competitiveness, user productivity, sales revenues, and eventually the customer experience.

Here are some ways how big data analytics can help improve the customer experience.

Personalization: Big data analytics can find out hidden patterns present in the data. It enables businesses to get to know their customers and effectively facilitate and transform customer-company relationships.

Build Customer Loyalty: Big data analytics allow organizations to actively build loyalty rather than just respond to customer queries. As it provides a quantitative and qualitative view of business, enterprises can pull out meaningful trends that can be translated into better products, services, and operational activities.

Track Customer Behaviour: Big data analytics is constructive in identifying what is behind customers’ behaviours. It allows firms to get personal with their customers and drive the quality of customer experience.

Data Collection: Today, every search, voice command, and major purchase may be recorded as people go about their professional and private lives. In the era of digital marketing, many companies now maintain profiles with thousands of data points about each individual.

Customer Retention: Data has maximum potential that aids businesses in planning ahead based on specific users’ interests.Data-driven customer experience is imperative in improving customer retention, engagement, and growth.

Predictive Analysis: With an optimized solution and predictive analysis, data can be much more useful, especially when it comes to determining what might interest a business customer now and in the future.Predictive analytics solutions help companies get to know what might happen, and prepare a response in advance, get ahead of the risks and influence the outcomes.

More Convenient Support: Once trained, data-driven systems can work with all existing channels, including voice, email, SMS, and more, often without human oversight. With big data analytics, customers even get more convenient, get responsive support across preferred platforms.

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NEW DELHI: The Ministry of Statistics and Programme Implementation said on Monday it is proposing to establish a National Data Warehouse on Official Statistics which will leverage technology and use big data analytical tools to improve the quality of macro-economic aggregates.

As all these reforms are an ongoing process, it is important that the readers and users understand and appreciate the limitations of data and the challenges in estimation, said the ministry.

While undertaking these reforms, it is important to realise that newer data sets and survey results will invariably be used and it would be incorrect to comment that old processes were better than the new, it said.

“The reforms being undertaken in the ministry will lead to better data sets and better estimates in future. This will be duly deliberated on by the Advisory Committee on National Accounts Statistics (ACNAS) during the base year revision,” the ministry said in a statement.

Several economic commentators continue to raise questions on India’s GDP numbers and the official statistical machinery.

“The national statistical system works in an ecosystem of independence and autonomy in its statistical processes. Allusions to any external influence are altogether unwarranted,” said the statement.

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