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Category: Analytics

Artificial Intelligence Provides Operational Solutions for the Food Industry!

Posted on by Imarticus Learning

Though Artificial Intelligence (AI) technologies have supported industries in multiple ways, the key is to identify areas specific to each industry where AI solutions are the most relevant. In the case of the food industry, solving operational efficiencies seems to be the area where AI-based solutions can make the maximum impact. And no wonder, with the relatively short timelines that food can be stored before consumption, make this an understandable challenge.

AI or machine learning relies a lot on historic data and uses this information to make predictive solutions or suggestions that can help in foreseeing certain outcomes. The more data at hand, the more closer to accuracy the solution/suggestion is.

Considering this, here are the potential avenues for the use of AI in the food business in ways that could transform conventional modes of operation by increasing efficiencies and production, predicting, assessing, and accurately solving more market demands and more.

Forecasting
Companies have used AI to determine and analyze demand variations, shopping trends during marketing campaigns, and sales drops. Stored data for these variables help machines identify problem areas and solve for them specifically.

It answers questions like – what is the optimal shelf space for this product to ensure increased sales? Which categories perform best for a specific type of promotion? How much should a certain product be stocked during peak/low sales periods? This helps optimize processes and reduce wastes through AI’s intelligent data-back prediction systems.

Boosting Productivity
Cloud computing technology, Big Data analytics, and data-driven machine learning has equipped a lot of industries to streamline their operational efficiencies. In the case of food industries, the manufacturing arm, in particular, AI assists in aiding the production processed by making certain decisions easier through its predictive features. These real-time solutions can potentially save a lot in time and moolah. These cost benefits will in turn be reflected in market satisfaction through pricing.

Automation
Technology’s increased agility in handling fragile produce helps in automating manufacturing tasks in the food industry’s operational chain. This means, tools have become advanced enough to handle delicate food items and process them without damaging them, such as eggs or tomatoes.

Not only this, but automation helps in reducing manual effort in repetitive tasks, therefore, adding time efficiency. This is especially useful for tasks where is lower decision-making potential.

Consumer Preferences
Through AI’s capability to handle large amounts of data with multiple variables and therefore make accurate predictions, consumer preferences can be assessed through their older buying / consuming patterns. Not only does this help in the development of newer products and services but also capitalizes on the key sale-drivers with eagle-eyed consistent focus.

Applications
Some ways to apply AI or machine learning in these industries would be through smartphone apps that fit into the consumer’s lifestyle, such as fitness apps, food suggestion apps based on certain body types, etc. Chatbots for online food partners could be another potential application. Quick food manufacturing machines independent of human assistance is another use case.

Conclusion
What this means for the food industry is that there is a constant need to keep an eye on AI trends and the way it is affecting businesses. Choosing the right AI tool for a certain business is a sure-shot way of intelligently increasing efficiencies and reducing costs. There are many more upcoming AI solutions in the market – keep your eyes on the radar to assess the best solution for your business!

Big Data for Big Banks – You Should Know

Posted on by Imarticus Learning

The growth of Big Data
Data is not just the new oil, but the new land too. In short, data is perhaps the most important resource to have in this century. With billions of data points and information being collected across the world every second through the internet and other avenues, the data size is increasing manifold. The upcoming technology is focusing on how to organize and sort this huge amount of data to derive insights and read patterns.
This, in effect, is referred to as Big Data Analytics Courses. Every major or minor firm, big or small player, in the consumer retail sector to healthcare and financial series, is using insights generated out of this big data to shape and grow their businesses. The lending business is no exception and can benefit immensely from the use of data. Fin-tech is changing the way the banking industry operates and making banking operations smoother, automated and more cost-effective. From fraud mitigation to payment solutions, Fintech is changing the way we think about banks.     
Data in lending business  
From the origination of the role to its continuation and life cycle management data can drive decision making in lending business. The patterns that can be read out of consumer data can predict the loans requirement, the capability of repayment of loans, the frequency of late payments or defaults and even the need for the consumers to refinance their loans. The fin-tech start-ups have already begun using the data in such a way, and hence the alternative lending businesses have bloomed over the last few years. Many banks are either merging with such alternative business lenders or taking the help of third party service providers to help boost their capabilities and skills to use big data analytics in business.
The areas of thrust
The major areas where lending business can be aided through the use of big data analytics are the portfolio risk assessment, stress tests, default probabilities and predicting the loan patterns of consumers. Credit card business already uses such technology extensively in assessing and evaluating their consumers.
For example, the credit card issuers tracked the repayments data of the users and based on the profession or the region; they may at times predict if the balances are going to be resolved or if they are going to be paid up front. They then design their marketing strategies keeping the results of analytics in mind in those areas or regions or regarding those specific consumers.
In the bygone years, the only way banks used to evaluate the creditability of a prospective borrower was to assess his or her records of past loans and repayment history. However, with new real-time data points, banks can study behavioural patterns and take appropriate decisions. Refinancing loans is another important area where technology and finance have come together to make life easier for consumers and banks alike. 
The algorithms can predict when a borrower may need to refinance his loans and can credit the amount in his account within seconds without all the paperwork and unnecessary delays. Another area that has transformed with the advent of big data and technology is the internal auditing of banks. With a digital record of every transaction or decision-making process, compliance rules and regulations are now easier to adhere to and track. 
Lastly, and perhaps most importantly, customer feedbacks have become important in this industry like never before. The algorithms can sift through loads and loads of data in the form of feedbacks and can implement solutions to enhance customer experiences on a real-time basis. Technology has changed almost everything around us and the lending operations to are no exception to the rule. In the years to come, banking may undergo a drastic transformation with elements that at this time, we may even be unable to imagine.
 

Applying Agile Methodology, The Future of Sales

Posted on by Imarticus Learning

An Introduction to Agile Sales Management
In the present Information Technology world where the agile business administration has gone from exception to industry standard – the achievement rate has been bewildering. As indicated by an ongoing coordinated review, 87% of respondents concurred that agile training methods are enhancing the nature of work life for their groups. 
Agile training Advantage
Agile business analysis & training methodologies benefit both the organization and its reps. As far as an organization is concerned, it enhances time to efficiency. For the rep, it quickens vocation advance (which additionally assists with rep maintenance). There has been across the board reduction in time to first prospecting call, disclosure call, introduction, and so on by implementing this approach. The best part is that there has been observed of speeding up so as to first arrangements and income, which is a win-win for organization and reps.
Agile onboarding drastically lessens the experimentation part in customary onboarding. The two reps and supervisors both will be substantially more prominent for going into reps’ first-time exercises, and post-training can be profoundly focused on dependent on the rep’s execution. All of this quickens time to efficiency and improves the probability that reps will have no bugs when they take part, in actuality.
Why Does Agile Sales Matter?
Despite the fact that the expression “agile sales” does not yet have a similar footing with B2B sales groups as “agile advancement” has with developers and designers, it is certain to grab hold. Actually, an ongoing overview revealed that almost one-fourth of respondents originated from ventures outside of IT. Agile training gives sales supervisors a key system for tending to and grasping the numerous patterns and changes occurring in the business world today.
Following a business procedure in Excel essentially won’t cut it when information ought to be amassed and broke down continuously. Also, giving reps a disarranged rundown of leads doesn’t bolster the focused on and customized outreach expected to associate with current prospects.
Adjust or terminate
We currently live in a quick paced world, with innovation driving monstrous changes in the manner in which we convey and work with each other. Obviously, organizations that neglect to adjust to the quick changes in business conditions could wind up in a bad position. This is particularly valid for the business.
We should take a gander at purchaser conduct for instance. Prior to the web, business people were the fundamental wellspring of item data. Purchasers constructed their buys in light of data passed on by the sales representative, and the achievement of the sale regularly depended on how viable the merchant could introduce answers for the purchaser’s concern.
Be that as it may, now, contemplates demonstrating that 57% of normal purchasers learn without anyone else, and are finished with 60% of the business procedure even before they start contact with deals experts. They utilize the web to pick among organizations that supply the items that they need and utilize interpersonal organizations to cross-check the item’s quality. Different discoveries demonstrate that the possibility of associating with is lead are multiple times more prominent if sales reps connect within five minutes.
As your customers turned out to be increasingly observing leaders, potential customers are getting more anxious; they need astounding data about first-rate items and administrations accessible to them at the snap of a mouse. 

Digital Skills and Asian Job Market – What You Should Be Aware Of!

Posted on by Imarticus Learning

Digital Skills
With increasing digitization of everything from payments to record keeping, the demand for digital skills in the job market is on an ever increasing trend. Digital skills may be classified into big data analytics, artificial intelligence and machine learning in the field of Data science; cryptography in areas of passwords and security in digital space; search engine optimization, video editing in the field of digital marketing and application development in the field of mobile technology.
The other skills which are much in demand are cloud-based applications, database management, programming and coding skills along with the ability to adapt and be adept at new technologies. Asia is one the regions where digital skills are most in demand. Moreover, the demand is poised to increase rapidly in the years to come as companies transform themselves to become a part of the digital revolution alongside the rise of digital start-ups.  
Asian Job Market
China, India, Singapore and Australia are some of the major countries in Asia where the job market for Digital skills is hot. From traditional banks and consumer retail companies to upcoming start-ups, digital skills can get you a job in a host of varied and diverse companies, roles and profiles.
In one of the top markets for Technology related Jobs, Singapore, digital marketing strategy and social media marketing are the top 2 skills that are in demand by the recruiters. Governmental as well as non-governmental agencies are looking to hire professionals with experience in the digital domain or even the freshers who possess high skills in such areas. Pricing strategy, mobile marketing and reputation management in the online space are other areas which can get you a job in Singapore.
In India, software jobs are most sought after and well-paying jobs. The Indian software job market is witnessing an upgrade of skills from basic coding to analytics, machine learning and artificial intelligence. However, application development, cloud services skills andidatabase management skills are still pretty much in vogue. Social media marketing jobs are on the rise along with digital advertisement, video editing and search engine optimization. Content creators in the digital space too are in demand in the Indian job market.
China is experiencing a shortage of high-end skilled workers. Companies like Alibaba, Tencent, Baidu and Huawei are promised high end technical and engineering related jobs to the highly skilled workers are mostly foreign educated and return to China to get jobs. Internet/ e-commerce are on a boom in China, and hence digital skills are more in demand and lucrative now than in any time in its history. Artificial intelligence skills and data science skills like natural language processing, speech recognition, visual skills, pattern recognition and data analytics in other related subjects are one of the most sought-after skills that recruiters look for in the prospective employees.
Australia too is facing a massive shortage in employee skills. With the digital boom, the market is looking for skilled professional for high-end digital marketing roles, but the present job market in Australia is facing a skill gap. The most in-demand digital skills in Australia are analytical skills regarding big data analytics, content creation and marketing skills and digital accounting to handle payments and receivables in the digital space.
Thailand is undergoing digitalization at a fast pace, and the companies are looking at strengthening their digital capabilities. Thailand is poised to become a part of the eastern economic corridor and emerge as a smart digital hub in the region.
Plenty of opportunities for skilled professionals
We notice from the above trends that there is no dearth of opportunities in the digital arena if one is skilled enough to provide solutions. The need of the hour is up-gradation of skills to match the industry demands.     

The Data Science Vacancy Gap In India

Posted on by Imarticus Learning

Quick. At the top of your head, think about a profession that has many open positions in the market. It’s not engineering. It’s not commerce or the arts. It’s data science and its better-known subset machine learning.
Did you know that data science was once termed to be the hottest job creator for the 21st century. It goes without saying that the large data generation coupled with the technology to play around with the data has given birth to a profession where the demands overrun the supply by a large margin.

Getting Straight To The Facts

  • In India alone, you will find that data science courses and machines learning are the hottest and trending topics of interest not limited to the IT sector alone but also for other industries.
  • Platforms like Thomson Reuters and Great Learning indicate that more than 50,000 positions related to data and data analytics are currently open for the taking in India.
  • The number of postings reached an all-time high towards the end of 2017 and had been rising to a constant range over the following months.
  • The reports also indicate that India currently contributes nearly 12 per cent of all data science positions when compared to the rest of the world.
  • Sectors like banking, finance, e-commerce, media, marketing and healthcare account for more than 67 per cent of the positions spanned mainly over eleven primary Indian states with the highest concentration being from metropolitan centers and urban districts.
  • Similar reports suggest that the banking and financial services are the biggest market for data analytics and data science professionals and are expected to create at least 58 per cent of all jobs for the year 2018. E-commerce is accounted for 17 per cent of the total job share.

Looking At The Factors

  • It is undeniable to say that there has been a rapid rise in the number of colleges and institutions offering data science training, data analyst training but the striking fact is that such skills are becoming increasingly common with completely unrelated professions as well.
  • Employers are no longer looking for candidates with a working level of understanding of computers but rather those who can utilize and leverage the data towards producing meaningful results.  
  • The large gap in the employment sector has been linked to the lack of institutions that properly prepare candidates for the tasks at hand. While there may have been an explosion of data science learning institutes all over the country, quick studies of the curriculum show a stark difference from what the industry is currently looking from.  
  • Government statistics, on the other hand, argue that while more and more data scientists and data analysts are being funneled out, nearly 44 per cent of fresh graduates choose

to work elsewhere outside the country or shift to another position due to dissatisfactions with the pay.

  • It should, however, interest people that average pay scales for data science positions are heavily influenced by the industry the candidate chooses to work in. Top level startups offer initial pays ranging from 89-91 K per year but tend to be higher for well-established companies.

Where The Road Leads Ahead   

  • Software and data science tools like Hadoop, SPSS, SAS, Python and IBM Watson are expected to be the next frontier for leading changes to the data science career as a whole and will likely be the most sought-after skills in the future.
  • More than 39,000 analytics jobs are anticipated to be created in India by 2020 with cyber-security taking an 11 per cent share, healthcare taking up 55 per cent, engineering studies taking up 8 per cent and space exploration 16 per cent.
  • Besides these, agriculture and aviation too will be engaged heavily with data analytic jobs as well as automation and the new buzzword driver less transportation.

How Agile Business Analysis is Working in The Public Sector?

Posted on by Imarticus Learning

Agile training can be used to explain a space or a procedure. With regards to various associations, the meaning of the word contrasts. So what precisely is agile training and for what reason is it getting to be critical for associations and public sector to apply it?
The objectives of associations in embracing agile work are to make a more responsive, productive and viable association, which enhances business execution and expands consumer loyalty. Being an Agile organization implies embracing an outlook of nonstop enhancement, making time for individuals to advance, and empowering a culture of joint effort and strengthening to help that development.
By implementing and actualizing agile business analysis in public sectors, one can accomplish a topnotch work space which empowers five-star administrations. A public sector is an ideal condition for the advancement of agile working.

Social establishments must be Powerful

Culture should be at the core of each Agile change. Especially in more significant associations, an Agile business culture should be held onto crosswise over however many divisions as could reasonably be expected. Many distinguished the powerlessness to change the way of life of their association as the most significant boundary to assist Agile selection. For bigger associations trying to adjust their working spirit, the initial step is to assess their current working society sincerely. This will illuminate the progressions expected to change the association. This implies a progress from a storehouse attitude to engaged little enabling choices to be designated, and issues settled rapidly.

Increased capacity to attract and hold astounding and high-quality ability and talent

The interest for adaptable work is on the ascent. Many qualified potential representatives are searching for adaptability – organizations who don’t offer it might pass up extraordinary ability. Giving adaptability to workers can likewise cause a lift in consistency standards. Workers who need to remove time from the workplace or travel for individual reasons don’t have to move occupations or end their agreement. The business carries on as common from an alternate area.

Train and guide your staff

The greatest blunder we see is taking existing venture conveyance staff and anticipating them that they should have the capacity to accept new Agile business analyst jobs without related knowledge. While it isn’t normal that everyone on the group will have worked in an Agile business previously, it is vital that these key jobs are filled by experienced contracts and have instructing support.
It is critical to build up an inner ability and to guarantee that experience must be there. This implies guaranteeing your staff should be supported past an initial couple of months. Two days of preparing and being a piece of an Agile group for three months does not give an individual the experience to lead and drive new activities; regardless they require continuous training to create or collect the full life cycle encounter required.
For Agile to be a win, it is an adventure that your entire association should be a piece of. There must be a reasonable vision and quantifiable results to demonstrate the change is working. A mentality of constant enhancement will enable you to rapidly adjust to any progressions emerging in the order of the pertinent open administration body.

Appraise achievement

With the end goal to assess their achievement, groups need to concur a straightforward technique to survey the esteem conveyed by Agile business trends and activities. The rule to be estimated should spill out of the corporate vision. With the goal for groups to accurately survey the estimation of an item, there ought to be an ordinary reassessment. The measurements ought to be bespoke and significant to the business capacity and esteem ought to be estimated not exclusively by financial profits but rather additionally on the genuine advantage picked up by the ‘client’.

Sports Analytics: How Data Analytics Is Changing The Game Strategy?

Posted on by Imarticus Learning

Big Data Analytics Training Courses

We have witnessed how Big Data and its analysis have reshaped the operation of many businesses. Recognizing the facts of big data analytics courses, the true scope of data analysis, the sports world making use of analytics. As we speak, the world of sports is improving its capabilities using sports analytics.

So, What Is Sports Analytics?
Sports analytics can be roughly translated to the use of data related to sports such as statistics of players, Weather conditions, pitch information, etc. to create predictive models to make informed decisions. The primary objective of sports analysis is to improve team performance. Sports analytics is also used to understand and maintain the fan-base of big teams.

The Sports Analytics was brought to the public eyes in 2011 by a movie called “Money ball” featuring attempts of the Oakland Athletics Baseball team’s 2002 season. The coach, Billy Beane restored the team using empirical data and statistical analyses on players’ performance.

He used trials with sabermetrics to improve his team and ended up finishing at the first place American League West on that season. Today with the advancement in Big Data technology every sports team is crunching data to gain a competitive advantage.

Changing the Strategy
Sports analyzers nowadays use wearable devices to collect data from players. The miCoach is such a wearable device developed by Adidas. This device attached to players’ jersey records data like heart rate, speed, and acceleration of the player. Analyzing this data, the team management is able to select the suiting players for the game. It also enables them to track the condition of players and allow them to rest before they get injured.

Video analytics is also being increasingly used across various sports for collecting data. In the NBA games, a company named SportsVU installed 6 cameras around the arena. Using advanced metrics, they were able to produce information about which move and which shots are best suited for each player. Such analytical results help teams to derive game strategies matching the strength of their players.

Big Data Analytics Training CoursesThe same is used to learn about the players of the opposite team’s players to find their weaknesses. Arsenal is one of the major football clubs to make huge investments in big data analytics courses.

They use a system that tracks 1.4 million data points per game and analyses all the data using an automated algorithm.

The Future of Sports Analytics
Without any doubt, sports analytics will continue to evolve, and the game strategies will heavily rely on the insights from the analysis than instinct. The next breakthrough sports world expecting from analytics is in the area of predicting a player’s mental ability to adjust with the rigors of the professional sports world.

There are already researches about finding the correlation between emotional regards of responsibility and on-field performance.

The current analysis is not capable of measuring an athlete’s desire to be the top performer. Lack of such features brings a slight chance for drafting busts. Looking at the rate at which the sports analytics have grown to today’s state, It is sure that more of these data-driven advancements in sports can be expected in the upcoming years.
 

How Machine Learning Is Saving The Indian Vernacular ?

Posted on by Imarticus Learning

In a nation riddled with countless cultures, unending dialects and infinite separations, the term ‘melting pot’ comes to mind. It’s common for the typical Indian being confused with the local tongues when treading into unfamiliar territories.
Fortunately for the millions of Indians beguiled by such problems, machine learning courses and a number of data science tools is proving to be a much-needed relief for preserving and keeping those languages intact.

Connecting Data To Language

Big Data

This has significantly boosted the outlook for interdisciplinary research that has allowed researchers across the country to link the aspects of linguistics and fragment all dialects to a condensed format that can be edited easily.Until now, several companies have taken to using an aggregator system to create a platform that translates the language into any other without sacrificing minor details. Several years ago, a research project under the name Technology Development for Indian Language was created by the government to scrape all the major Indian languages for data science purposes.

  • One such platform that has been making strides is the e-Bhasha platform that is making content available for citizens in their language. It was created as a big data project in 2015 and has become a starting point for many linguistic researchers.
  • As the number of internet users in India grew more than 28 per cent and is expected to be a $6.2 billion industry per year, international groups are jumping on the bandwagon to appeal to the common man.

Playing With The Locals

Seeing the enormous benefits of tapping into local consumers, big groups like Google set out to create the Google Brain which is essentially an extensive neural network to develop human language from the get-go.

  • Aspects of this have been incorporated into Google Assistant as well, having translated content from more than 500 million monthly users and 140 billion words per day in as many 158 languages.
  • The craze began by the year 2013 when e-commerce was still taking root in the country and was challenged by the numerous languages that consumers had in the country.
  • Websites like Flipkart and Snapdeal dealt with local language content for mobile websites as far back as 2015.
  • Reports suggest that Marathi, Gujarati, Tamil, Punjabi and Malayalam represented over 75 per cent of searches on Google in the very same languages. What’s even more interesting is that more than 73% of people surveyed are willing to go completely digital if the system communicates in their own language.   
  • Facebook has raised the number of Indian languages for posting to almost 12 but still lacks regional pages that use the same kind.
  • Small firms in India are collecting as much textual Corpus for languages available using translation services like Reverie, Process9 and IndusOS.  

The Technology Used

  • Most companies would confess to the use of neural networks for developing such programs, but the primary machines behind such global endeavors has been some rather sophisticated algorithms.
  • The newest additions to the industry happen to be some enhanced versions of the Hadoop MapReduce extension. A significant feature of the software is the ability to find linguistic linkers between similar words and compound phrases which makes translations more concrete. Some stellar packaged additions to the SPSS Modeler system too have taken place that is helping companies handle large corpuses.
  • At the same time, marketing groups are using modified techniques to feed invoice data collected from average consumers which are being sent into what’s being called a ‘global corpus data set.’
  • Likewise, teams across the country in data collection firms are hiring data collection engineers to converse and accumulate conversational audio recordings both in rural and urban areas.
  • The main subject remains heavily invested in cross-directional neural networks many of which are using data analysis tools and machine learning tools like Tensor Flow from Google and IBM Watson.

5 Simple Facts About Big Data Analytics Courses – Explained

Posted on by Imarticus Learning

Data Science, Machine Learning or the Big Data Analytics Courses whatever one might refer it as, the subject matter has witnessed colossal growth over the last two decades due to the increase in collection of data, improvement in data collection techniques and methods, and a substantial enhancement in the power of computing data. Various data analyst jobs are pooling talent from multiple branches of engineering, computer scientist, statisticians and mathematicians and is increasingly demanding an all-around solution for numerous problems faced by the businesses in managing their data.
As a matter of fact, not a single stream of business, engineering, science etc. has remained far from the reach of data analytics and are employing various data analysis tools on an on-going basis within their respective industries. Perhaps it can be one of the best times for students to enroll in the big data analytics courses and be future ready as the future is in data analytics.
But, as data analytic jobs are deemed to be in an upward trend shortly, here are some simple facts one needs to know about data analytics before embarking a big data analytics course or a career in data analytics. 

  1. No Data is Ever Clean

Theoretically, as taught during a  data analytics course,  analytics in the absence of data is just a group of theories and hypothesis, whereas data aids to test these theories and hypothesis towards finding a suitable context. But, when it comes to the real world, data is never clean and is always in a pile of mess. Organisations with established data science centres to say that their data is not clean. One of the major issues organisations face apart from missing data entries, or incorrect entries is combining multiple datasets into a single logical unit.
The various datasets might face many problems which prevent its integration. Most data storage businesses are designed to be well integrated with the front-end software and the user who generates the data. However, many-a-times, data is created independently, and the data scientist arrives at the scene at a later stage and often ends up being merely a “taker” of data which is not a part of the data design.

  1. Data Science is not entirely The user will need to clean some data manually

A vast majority of people do not wholly understand what data analytics is? One of the most common misconceptions about data analytics is that the various data analysis tools thoroughly clean the data. Whereas, in reality, as the data is not always clean, it requires a certain degree of manual processing to make it usable, which requires intense amount of data processing, which can be very labour intensive and time-consuming, and the fact remains that no data analysis tools can completely clean the data at the push of a button.
Each type of data poses its own unique problem, and data analyst jobs involve getting their hands dirty and manually processing data to test models, validate it against domain experts and business sense etc.

  1. Big Data is merely a tool

There is quite a lot of hype around the Big Data, but many people do not realize that it is only a collection of data analysis tools which aids working with a massive volume of data promptly. Even while using Big Data, one requires the utilise best data modelling practices and requires a trained eye of an expert analyst.

  1. Nobody cares how you did something

Executives and decision making are often the consumers of various models of data science and continuously require a useful and a workable model. While a person performing one of many data analyst jobs might be tempted to provide an explanation to how data was derived, in reality, these executives and decision makers care less how the data was acquired, and are more interested in its authenticity and how can it be used to improve any of their business functions.

  1. Presentation is Everything

As most of the consumers of analytic solutions are not mathematicians and are experts in their respective fields, presentation plays a vital role in explaining your findings, in a non-technical manner, which is understandable to the end user. A PowerPoint presentation loaded with infographics can aid a data scientist in conveying the end-user their message in a language and mode of communication with is easy of them to understand.

10 High Value Use Cases for Predictive Analytics in Healthcare

Posted on by Imarticus Learning

Healthcare organisations are having their moment when it comes to Big Data and the potential it offers through its analytical capability. From basic descriptive analytics, organisations in this sector are leaping towards the possibilities and its consequent perks of predictive insights. How is this predictive analysis going to help organisations and patients? What are the top roles a Data Analyst look for?
Let’s break this down into ten easy pointers:

  • Predicting Patient Deterioration

Many cases of infection and sepsis that are being reported among the patients, can be easily predicted via predictive insights that Big Data offers. Organisations can use big data analytics to predict upcoming deteriorations by monitoring the changes in the patient’s vitals. This helps in the recognition and treatment of the problem even before there are visible symptoms.

  • Risk Scoring for Chronic Diseases

Based on lab testing, claims data, patient-generated health data and other relevant determinants of health, a risk score is created for every individual. What this does, is that it leads to early detection of diseases and a significant reduction in treatment costs.

  •  Avoiding Hospital Re-admission Scenarios

Using predictive analysis, one can deduce risk factor(s) indicating the possibility for re-admission of the patient to the hospital within a certain period. This helps the hospitals design a discharge protocol which prevents recurring hospital visits, making it convenient for the patients.

  • Prevention of Suicide

The Electronic Health Records (EHR) provides enough data for predictive algorithms to find the likelihood of a person to commit suicide. Some of the factors influencing this score are substance abuse diagnose, use of psychiatric medications and previous suicide attempts. The early identification helps in providing the mental health care potential risk-patients will need at right time.

  • Forestalling Appointment Skips

Predictive analysis successfully anticipates ‘no-shows’ when it comes to patients and this helps prioritise giving appointments to other patients. The EHR provides enough data to reveal individuals who are most likely to skip their appointments.

  • Predicting Patient Utilization Patterns

Emergency departments of regular clinics have varying staff strength according to the fluctuations in patient flow. In this case, predictive analysis helps to forecast the utilization pattern and the requirements of each department. It improves patient wait-time and utilisation of facilities.

  • The Supply Chain Management

Predictive analysis can be used to make efficient purchasing which in turn has scope for massive cost-reduction. Such data-driven decisions can also help in optimizing the ordering process, negotiate the price and reduce the variations in supplies.

  • Development of New Therapies and Precision Medicine

With the aid of predictive analysis, providers and researchers can reduce the need of recruiting patients for complex clinical trials. The Clinical Decision Support (CDS) systems have started to predict the patient response to treatments by analysing genetic information and results of previous patient cohorts. It enables the clinicians to select treatments with more chances of success.

  • Assuring Data Security

By using analytic tools to monitor the data access and utilization pattern, it is possible to predict the chances of a potential cyber threat. The system detects the presence of intruders by analysing changes in these patterns.

  • Strengthen Patient Engagement and Satisfaction

Insurance companies encourage healthy habits to avoid long-term high-cost diseases. Here, predictive analyses help in anticipating which communication programmes would be the most effective in each patient by analysing past behavioural patterns.
These are possible perks of using tools like predictive analyses in healthcare that optimise processes, increase patient satisfaction, enable better care mechanisms and reduce costs. The role of Big Data is clearly essential as demonstrated and a targeted use can show high-value results!