Healthcare Business Intelligence Market : A Breakdown of the Industry by Technology, Application, and Geography

Posted on by Teddy Nee

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The global healthcare business intelligence market is anticipated to reach value of USD 15.14 Billion by 2027, according to a current analysis by Emergen Research. The global healthcare business intelligence(BI) market is expected to expand significantly during the forecast period, owing to increasing demand for improved claim management solutions in the healthcare industry. Rising demand for better cost management solutions is likely to drive the global healthcare business intelligence market further in the near future. Moreover, rising adoption of data-driven decision-making solutions in the healthcare industry is also expected to drive the global healthcare BI market during the forecast period.

𝐓𝐨 𝐠𝐞𝐭 𝐚 𝐬𝐚𝐦𝐩𝐥𝐞 𝐜𝐨𝐩𝐲 𝐨𝐟 𝐭𝐡𝐞 𝐆𝐥𝐨𝐛𝐚𝐥 Healthcare Business Intelligence Market 𝐫𝐞𝐩𝐨𝐫𝐭, 𝐯𝐢𝐬𝐢𝐭 @ https://www.emergenresearch.com/request-sample/460

The study outlines the rapidly evolving and growing market segments along with valuable insights into each element of the industry. The industry has witnessed the entry of several new players, and the report aims to deliver insightful information about their transition and growth in the market. Mergers, acquisitions, partnerships, agreements, product launches, and joint ventures are all outlined in the report.

𝐋𝐢𝐬𝐭 𝐨𝐟 𝐓𝐨𝐩 𝐊𝐞𝐲 𝐂𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬 𝐏𝐫𝐨𝐟𝐢𝐥𝐞𝐝 𝐢𝐧 𝐭𝐡𝐞 Healthcare Business Intelligence Market Domo Inc., Tableau Software, Sisense Inc., Microsoft Corporation, Qlik Technologies Inc., Infor Inc., SAP SE, Salesforce.com, Inc., Oracle Corporation, and MicroStrategy Incorporated

𝐌𝐚𝐫𝐤𝐞𝐭 𝐃𝐲𝐧𝐚𝐦𝐢𝐜𝐬:

The report offers insightful information about the market dynamics of the Healthcare Business Intelligence Market . It offers SWOT analysis, PESTEL analysis, and Porter’s Five Forces analysis to present a better understanding of the Healthcare Business Intelligence Market , competitive landscape, factors affecting it, and to predict the growth of the industry. It also offers the impact of various market factors along with the effects of the regulatory framework on the growth of the Healthcare Business Intelligence Market

Increasing demand for improved claim management solutions in the healthcare industry and rising demand for better cost management solutions are driving the healthcare business intelligence market.— Emergen Research

𝐅𝐨𝐫 𝐬𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐚𝐥 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐬𝐭𝐮𝐝𝐲 𝐨𝐧 Healthcare Business Intelligence Market 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐫𝐞𝐩𝐨𝐫𝐭, Request for Free Sample Report

𝐒𝐨𝐦𝐞 𝐊𝐞𝐲 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬 𝐢𝐧 𝐭𝐡𝐞 𝐑𝐞𝐩𝐨𝐫𝐭 :

In August 2020, Knarr Analytics LLC was acquired by Qlik Technologies Inc. The deal would improve Qlik’s Cloud Platform Active Intelligence capability, which offers comprehensive insights to enable data-driven activities.

During the forecast period, the software segment is expected to retain the largest market share, expanding at a CAGR of 14.7%. Business intelligence software helps healthcare organizations collect, interpret, and process data into appropriate business information, which is projected to fuel the segment during the forecast period.

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10 Ways Machine Learning Is Revolutionizing Supply Chain Management

Posted on by Teddy Nee

Machine learning makes it possible to discover patterns in supply chain data by relying on algorithms that quickly pinpoint the most influential factors to a supply networks’ success, while constantly learning in the process.

Discovering new patterns in supply chain data has the potential to revolutionize any business. Machine learning algorithms are finding these new patterns in supply chain data daily, without needing manual intervention or the definition of taxonomy to guide the analysis. The algorithms iteratively query data with many using constraint-based modeling to find the core set of factors with the greatest predictive accuracy. Key factors influencing inventory levels, supplier quality, demand forecasting, procure-to-pay, order-to-cash, production planning, transportation management and more are becoming known for the first time. New knowledge and insights from machine learning are revolutionizing supply chain management as a result.

The ten ways machine learning is revolutionizing supply chain management include:

Machine learning algorithms and the apps running them are capable of analyzing large, diverse data sets fast, improving demand forecasting accuracy.

Reducing freight costs, improving supplier delivery performance, and minimizing supplier risk are three of the many benefits machine learning is providing in collaborative supply chain networks.

Machine Learning and its core constructs are ideally suited for providing insights into improving supply chain management performance not available from previous technologies.

Machine learning excels at visual pattern recognition, opening up many potential applications in physical inspection and maintenance of physical assets across an entire supply chain network.

Gaining greater contextual intelligence using machine learning combined with related technologies across supply chain operations translates into lower inventory and operations costs and quicker response times to customers.

Forecasting demand for new products including the causal factors that most drive new sales is an area machine learning is being applied to today with strong results.

Companies are extending the life of key supply chain assets including machinery, engines, transportation and warehouse equipment by finding new patterns in usage data collected via IoT sensors.

Improving supplier quality management and compliance by finding patterns in suppliers’ quality levels and creating track-and-trace data hierarchies for each supplier, unassisted.

Machine learning is improving production planning and factory scheduling accuracy by taking into account multiple constraints and optimizing for each.

Combining machine learning with advanced analytics, IoT sensors, and real-time monitoring is providing end-to-end visibility across many supply chains for the first time.

Read more at 10 Ways Machine Learning Is Revolutionizing Supply Chain Management

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Analysis – what impact will Brexit have on supply chain operations?

Posted on by Teddy Nee

Brexit is a great uncertainty for businesses operating cross-border. Therefore, it is crucial for companies operating complex supply chains to consider the implications of Brexit on their businesses.

A PESTLE is an analysis tool that provides an understanding of the factors and external changes to the business, which may impact their ability to operate and thrive.

In this article, Nicholas Hallam considers the elements of Brexit that are out of the control and influence of businesses, but which they should still be planning for, as well as the proactive steps they can take to guide strategic decision making.

Political

Brexit has been an intensely political issue – from the original promise of the In/Out referendum (made by David Cameron to prevent a haemorrhaging of Tory support to UKIP) right through to the political and legal disputes about the triggering of Article 50 and the ongoing controversy about the trade-off between free movement and the single market. The debate – which cuts across traditional political alignments – pits sovereignty against efficiency, and the citizens of definite somewhere against free-flowing globalists.

Economic

The UK runs a constant trade deficit with the EU. While the UK’s biggest individual export trade partner is the US, over 62% of all exports went to the 27 EU Member States during Q1 2017, totalling £33.1 billion. And during this time-period the UK’s top import partner was also an EU Member State, Germany (£17.6 billion worth of goods).

Social

While Brexit essentially means untangling the links that the UK has with the EU, there are many ways in which we will stay connected irreversibly. Some of the biggest technological advances in recent years – such as smart phones and social media – have been made to connect people no matter their location, language or economic status. So, while the government may have a protectionist ethos, it may be increasingly impractical to implement to live up to most people’s expectations and habits.

Read more at Analysis – what impact will Brexit have on supply chain operations?

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The Bank of England has a chart that shows whether a robot will take your job

Posted on by Teddy Nee

The threat is real, as this chart showing the rise and fall of various jobs historically shows. Agricultural workers were replaced largely by machinery decades ago. Telephonists have only recently been replaced by software programmes. This looks like good news for accountants and hairdressers. Their unique skills are either enhanced by software (accountants) or not affected by it at all (hairdressers).

The BBC website contains a handy algorithm for calculating the probability of your job being robotised. For an accountant, the probability of vocational extinction is a whopping 95%. For a hairdresser, it is 33%. On these numbers, the accountant’s sun has truly set, but the relentless upwards ascent of the hairdresser is set to continue. For economists, like me, the magic number is 15%.

Another data analysis about jobs which will be phased out as time goes. It is an interesting analysis of historical job data. However, after I glanced through the bank report referenced in the article, I am not sure robots are the reason of the job replacement. For example, it could be replaced by cheap labor in foreign countries. The bank report shows only the jobs subject to be phased out due to technology advancement. People could just become productive. So, do not take robots too seriously!

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What’s Behind the Inventory Crisis of 2016?

Posted on by Teddy Nee

The last time the inventory-to-sales ratio was this high was 2009, when we were in the throes of the Great Recession – people lost jobs, businesses closed, nobody was spending, nobody was growing.

What does it mean that inventory levels are this high in 2016? Are consumers not spending? Are we headed for another recession? Or are other forces at work?

Well, in April the Bureau of Economic Analysis reported that consumer spending experienced its biggest gain in six years. And while JPMorgan recently reported an increased probability of a recession in the next 12 months, no one’s sounding the alarm bells quite yet. Besides, inventory levels have been high since last fall.

So what else could be at work?

The Marketplace

Traditionally, a drop in consumer demand would cause a short-term build-up of inventory. But businesses would eventually compensate by cutting orders and manufacturers would produce less. But as we’ve seen, demand isn’t going down. And yet, inventory isn’t moving. Why?

One major culprit is the way consumers shop. Their expectations have changed. This is the age of Amazon Prime, Instacart, Uber and Lyft. Free shipping. In-store pick-up. 1-hour delivery. Easy exchanges and returns. Above all – convenience. If it isn’t convenient for a customer to buy something they want, they won’t buy it – or they’ll buy it somewhere else. Fulfillment has usurped the throne of customer satisfaction.

Traditional retailers have struggled because of this. As young, tech-driven start-ups bite into market with the luxury of fresh starts, traditional retailers have tried to stay competitive. One common tactic has been to keep buffer inventory on hand. Out-of-stock inventory kills customer loyalty. Not being able to fulfill quickly kills customer loyalty. But having lots of inventory doesn’t equate to efficient fulfillment. That requires having a modern, flexible supply chain. Without agility, retailers often lack the competence to satisfy customer demand, let alone fulfilling profitably.

Read more at What’s Behind the Inventory Crisis of 2016?

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Automating Big-Data Analysis and Replacing Human Intuition with Algorithms

Posted on by Teddy Nee

Big-data analysis consists of searching for buried patterns that have some kind of predictive power.

But choosing which “features” of the data to analyze usually requires some human intuition.

In a database containing, say, the beginning and end dates of various sales promotions and weekly profits, the crucial data may not be the dates themselves but the spans between them, or not the total profits but the averages across those spans.

MIT researchers aim to take the human element out of big-data analysis, with a new system that not only searches for patterns but designs the feature set, too.

To test the first prototype of their system, they enrolled it in three data science competitions, in which it competed against human teams to find predictive patterns in unfamiliar data sets.

Of the 906 teams participating in the three competitions, the researchers’ “Data Science Machine” finished ahead of 615.

In two of the three competitions, the predictions made by the Data Science Machine were 94 percent and 96 percent as accurate as the winning submissions.

In the third, the figure was a more modest 87 percent. But where the teams of humans typically labored over their prediction algorithms for months, the Data Science Machine took somewhere between two and 12 hours to produce each of its entries.

“We view the Data Science Machine as a natural complement to human intelligence,” says James Max Kanter, whose MIT master’s thesis in computer science is the basis of the Data Science Machine.

Read more at Automating Big-Data Analysis and Replacing Human Intuition with Algorithms

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Automating Big-Data Analysis and Replacing Human Intuition with Algorithms

Posted on by Teddy Nee

A new and unique computer system from MIT has outperformed human intuition using its algorithms, and it’s amazing, and perhaps a little frightening: the Data Science Machine beat out over 600 human teams in finding predictive analysis.

Big-data analysis consists of searching for buried patterns that have some kind of predictive power.

But choosing which “features” of the data to analyze usually requires some human intuition.

In a database containing, say, the beginning and end dates of various sales promotions and weekly profits, the crucial data may not be the dates themselves but the spans between them, or not the total profits but the averages across those spans.

MIT researchers aim to take the human element out of big-data analysis, with a new system that not only searches for patterns but designs the feature set, too.

To test the first prototype of their system, they enrolled it in three data science competitions, in which it competed against human teams to find predictive patterns in unfamiliar data sets.

Of the 906 teams participating in the three competitions, the researchers’ “Data Science Machine” finished ahead of 615.

In two of the three competitions, the predictions made by the Data Science Machine were 94 percent and 96 percent as accurate as the winning submissions.

In the third, the figure was a more modest 87 percent. But where the teams of humans typically labored over their prediction algorithms for months, the Data Science Machine took somewhere between two and 12 hours to produce each of its entries.

Read more at Automating Big-Data Analysis and Replacing Human Intuition with Algorithms

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10 Tips For Getting Started With Global Supply Chain Risk Management Programs

Posted on by Teddy Nee

In exploring AGCO’s success with implementing a global supply chain risk management (SCRM) program, we can summarize our key recommendations to other manufacturers and services oriented companies in 10 tips:

Start to engage with solution providers – Try them out, start to inflict the pain of visibility on your internal stakeholders, teach your organization to act with many blinders removed and adopt a more strategic level of thinking.

Solutions are in a state of flux – Early adopters will likely have to go through radical changes in their programs as this industry matures, but this is preferable to remaining on the sidelines, getting stuck deeper in the old ways.

Heuristics will make a big difference over time – Both in helping to eliminate false positives and also in identifying real issues with greater precision. Aggregated metadata from your third parties, combined with other big data sets, all processed in real time, will drive a change toward solutions that not only show what your supply base looks like but also helps manage risk scenarios and develop mitigation plans of action.

A picture is worth a 1,000 conference calls – Think of a map, showing all your major internal and external business relationships (manufacturing facilities, warehouses and distribution facilities, logistical paths, suppliers and their suppliers, etc.). This simple illustration can quickly rally stakeholders around a common cause.

Good SCRM analysis requires good data – Don’t skimp on the prep work. You know that sooner or later you do need to get to a clean master data management understanding, as well as item level PO analysis. You also need to fully assess your key suppliers and their immediate supply base and product lifecycles. This is a good time to start on that journey.

Read more at 10 Tips For Getting Started With Global Supply Chain Risk Management Programs

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Creating Value Across Supply and Demand Chain by Innovative use of BI, in Operational and Tactical Management of Balanced KPI, and Risk

Tag Archives: analysis

10 Ways Machine Learning Is Revolutionizing Supply Chain Management

The Bank of England has a chart that shows whether a robot will take your job

What’s Behind the Inventory Crisis of 2016?

The Marketplace

Automating Big-Data Analysis and Replacing Human Intuition with Algorithms

Automating Big-Data Analysis and Replacing Human Intuition with Algorithms

10 Tips For Getting Started With Global Supply Chain Risk Management Programs

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