Big Data is massive. This article will explore the issues of big data: what it is, how it will improve decision - making, and how to use it correctly.
1) What's the big deal about big data?
Big data is all about three "V's" Velocity, Volume and Variety.
2) How it will improve decision?
# Deliver customer insights.
Silent customers create major risks to companies. They don't express their dissatisfaction. Companies can avoid these issues through the proper use of technology with predictive analytics. Predictive analytics can stop the silent customer attrition by identifying four ways to retain customers:
1. Recognize customers who make a detailed analysis before they determine.
2. Determine the most effective actions to reduce the attrition
3. Distinguish between the best time, message, and channel to reach the customer.
4. Identify the full path to retention rather than one single action.
Teaching profession is facing lower job satisfaction, unclear idea related to a particular subject. Inaccurate teaching processes are generating slack in the education industry. To manage such issues we can use big data as used in famous movie "Moneyball". It can predict the future outcome by using simple statistics and work according to forecasted outcome. Proper mining could help us to find undervalued personnel with higher potential to attain a high success rate. So in educational context we can segregate major characteristics. These attributes can be:
1. High level of optimism, enthusiasm, sense of humor
2. High standard of expectation from their students
3. Continual self-evaluation
4. Self-criticizing, understanding and updating.
Big data analytics can analyze the past behavioral data of humans. It is highly correlated to future outcomes. The most important task is to understand how much emotional extent can be predicted accurately. By analyzing critical reasoning and judgmental decision, there will be higher chances to predict more efficient human behavior. Social media plays an important part by providing more sensitive data having higher emotional quotient. Search dynamics also help to predict future trends. Therefore, combining big data, transaction data and social data along with shopping behavioral data opens a new horizon which enables marketers to influence human behaviors and perceptions. To read, follow: : http://www.forbes.com/sites/forbesinsights/2016/02/02/has-big-data-taken-the-human-out-of-human-behavior/#2280493b170c
These days, faster diagnostic and machine learning on large sets of data promise a real-time understanding of health. Predictive analytics help to decrease costs, and helps in preventive disease management. Precision medicine is an approach to treatment and prevention considering individual variability in genes, environment and lifestyle for each person and also classify people precisely, based on susceptibility, microbiology and/or prognosis, at a considerably higher resolution. Health data analysis provides useful perspectives to predict the future. Precision medicine makes true predictive analytics possible. Prediction requires precision, but it does not require precision alone. Predictability comes from a wide and narrow gap which requires a data sets with high-accuracy. For more read : http://hitconsultant.net/2016/02/22/31535/
Effective data analysis is learned overtime. It takes time, patience and effort. Here are few tips which can help to make the journey of learning smoother.
· Use simple analysis terms and methods rather than complex algorithms. If your customer and engineers are not able to understand your analysis then all the effort goes in vain.
· Look for multiple data sources.
· Use familiar tools rather than new tools. We should stay updated with the newest technology in market but avoid abundant use of fancy new tools which are difficult to understand. Stick to classics.
Earlier, organizations need to analyze the impact of external factors on the basis of performance. They used old statistical modelling tools and took months of data collection and analysis and guessed the external factor which had more impact on the business. These models became outdated as soon as they were developed as external data were constantly changing. During the economic crisis, companies did not understand the economy. But, nowadays, as consumer behavior and other useful data sets have become more available, businesses can address challenges and opportunities by improving bottom line profits and helps to generate higher revenue by following correlation of real time data model. For more read the article written by Rich Wagner (President and CEO of Prevedere) at : http://www.informationweek.com/big-data/transforming-an-antiquated-business-intelligence-process-/a/d-id/1324197
Big data analytics plays an important role in supply chain management. 97% of supply chain executives have reported how big data analytics helped them to grow their business and only 17% of any particular industry have implemented this process. This process generates higher visibility and deeper insight to the customer behaviour and demand supply scenario. It also helps to discover and manage supplier relationships more effectively. Big data help to understand customer needs and make a 360 degree analysis regarding marketing channel, segmentation and acceptability. Predictability helps to create more efficient supply chain progress (increased ~10%). It identifies supply chain risk by considering the previous demand, supply scenario almost accurately. Supply Chain Traceability and recalls are data-intensive and highly correlated to supply chain risk. The ability to quickly meet customer fulfilment is an important driver. It helps in competitive advantage across all industries which can be achieved by big data analysis.
To read, follow: http://www.computerworld.com/article/3035144/data-center/overcoming-5-major-supply-chain-challenges-with-big-data-analytics.html
Nowadays, medical sensors and data analytics are used to boost medical devices. Devices can forecast unfavorable outcomes before they occur. After analyzing large data sets, researchers can identify small changes in patient behaviors. Combining with data analytics, implantable medical sensors will allow monitoring patient health. Utilizing predictive analytics, smart sensors identify unfavorable changes in data which helps to detect medical crises very fast. Data analytics is used to influence smart devices that provide guidance to patients. These devices receive inputs from their sensor data. Predictive analytics help to make unique medicines. Smart devices use data to predict how an individual patient will respond to specific courses of action. Data analytics also help manufacturers to go beyond the general results of clinical trials to better interpret the value their devices for specific groups of patients. Read more about this article written by Battelle : http://www.healthcareitnews.com/news/big-data-difference-predictive-analytics
Data analytics are the most essential part of any organization these days and requires efficient personnel having a greater skill set in data science. But, there is not enough parity between demand and supply. According to a recent study, it was found that there is demand for computer programmers with a background in data analytics, but out of the 332,000 computer programmers in America, only 4% had the necessary skill sets. So to bridge the gap, the online training method can be a helpful process and is flexible and this in turn enhances productivity. This is a continuous process of development and helps to figure out new talent within the organization. But all these processes can only be possible if colleges and universities encourage their student to learn data science and master in those skillet.
To read, follow: : http://www.cio.com/article/3033887/careers-staffing/can-online-training-bridge-the-big-data-skills-gap.html
Optimization of CRM is highly correlated to the social media presence of a company. Customers frequently generate queries via social media. So there are some steps that can be followed to create a position in the customer's mind by establishing good connections between social media and CRM.
1. Right platform should be chosen.
2. Must have a dedicated human resource to handle the social media activity and patch them up with marketing team who handles CRM.
3. Instead of putting one liner FAQs, try to personally resolve critical issues in time.
4. If you have a different presence in multiple social media, then deploy time understanding the importance
The use of analytics can bring major change in the hospitality sector. It is important for hoteliers to understand the preference of the guests, purchase behavior and profitability in order to increase the brand loyalty. By using analytics, it is easier to do segmentation according to booking trends, behavior and other constituents. To know more about the use of analytics in the hospitality sector, read the following article by Bernard Marr (Contributor at Forbes)-:
Data scientists have categorized this new era of data with "four Vs". They are volume of the data, variety of the data sets, velocity of analysis of data and veracity of data quality. Nowadays, companies are turning to external Big Data for answers. Organizations want to distinguish which external factors will influence the sales and demand of a particular product in the future. The answers they get from the above questions, is setting the trend for three distinctive predictive analytics process. They are - Predictive hypothesis testing, Closing the gap between data and delivery, shrinking the barrier between internal and external data. With the speed of technology diversity of data continues to grow. Read more about this article written by RICH WAGNER(Author) at:
A cyber - attack may happen anytime in today's world. Big data and predictive analytics help in cyber defense and convert data into actionable intelligence. Predictive indicators can identify new risks and assist in security. They can go undetected. Predictive analytics can detect these unusual data, including hidden data. By finding these unusual patterns, predictive analytics help to reduce a company's overall risk. With predictive analytics, risks are evaluated and ranked in importance. Managing the predictive analytics process requires an organization to handle the false positives and false negatives that are generated during the threat surveillance process and it cannot be too restrictive as it will block logical traffic, which can lead to a reduction in profit or customer service. It depends on how a person is using it to get the best results. Read more at:
Investment in shares requires analysis of huge historical data. Analysis is the primary phase and it forecasts the future investment process. Broking house plays a clinical role in this context and charges a percentage of hike in shares. But these days such information is frequently available in different websites and are updated on a regular basis. The usage of technology for predictive analysis is hugely correlated with big data. These are limited to institutional buyers. The automation in predictive analysis requires a huge precision which is cost effective, but its implementation could be a game changer.
To read, follow: http://www.thehindubusinessline.com/markets/stock-markets/big-data-robo-analytics-to-drive-next-phase-of-growth/article8249330.ece
Any decision pertaining to designing products, environments and brand experience is closely connected to the use of colour because it depends on the buying behaviour of customers, brand perception, strategic differentiation, and user experience. But availability of information related to colour perception is limited. Organizations are using big data to analyse colour preference and perception which contains over a 100 years of data. They divided their study into four categories regarding colour data, analytic and insight:
1. Colour competitive intelligence is relevant for aggressive in brand competition.
2. Colour legal intelligence helps to identify the colour norms available to different countries.
3. Colour Research Intelligence collates colour studies that have been conducted over the past century, world-wide, about the industry and product segments.
4. Colour listening intelligence signifies how much buzz about a particular colour is popular in the market.
The super bowl is the place where the big corporate battle within themselves to win a commercial spot. They use real time data. The use of real time data is helping marketers to solve some of the continuing mysteries of media value and also helping brands to understand the connections between paid and earned engagement. Want to experience more, then read this article by Rob Salkowitz (contributor in Forbes)-: http://www.forbes.com/sites/robsalkowitz/2016/02/07/whos-winning-the-super-bowl-ad-battle-live-blogging-the-big-game-with-real-time-data/#208c134c44c6
Design thinking is at the top of mind for business teams of big giants as well as startups. The traditional "If you build it, they will come," mentality has been taken from techniques like customer journey mapping and empathy-driven prototyping. Many companies are unsure how to implement it to improve their business - especially in areas like data analytics and decision sciences. The first step is to ask: for whom are we designing and what is the problem they are experiencing? The second: to what end are we modeling the design - to boost consumption and engagement, improve performance, or to achieve scale? These same needs to be asked at the outset of any analytics effort. Here are five simple steps that are key to infusing analytics with a designer mindset.
1) Create a design framework that allows you to fail fast.
2) Empathize with your customer to impart emotion into your product.
3) Focus on problem-solving that allows for rapid experimentation.
4) Employ methods to inspire creative brainstorming across teams.
Doctor or healthcare professionals treat us according to recent scientific arrangement. EBM (Evidence Based Medicine) is the orthodox standard for the provision of healthcare. But, in the era of big data, it is about to change. Clinical trials work compares the new treatment to other treatments by separating random patients into different groups. There is a risk of methodological flaws and the small populations used. By mining the practice-based clinical data, i.e. actual patient records for information on who has what condition and what treatments are turning for better treatment of the individual. Almost 80% of medical information about patients forms of unstructured data. For better care of individuals and to understand about the health of the population, we need to be able to mine unstructured data. So before analyzing any data, the first thing is to extract the data from these diverse sources. Then turn that information into something that computers can break down. The data can then be dissected at an individual level to create a patient data model. Data theft can be scaled down by encrypting patient data. Read more about it in the article written by Bernard Marr (contributor) : http://www.forbes.com/sites/bernardmarr/2016/02/16/how-big-data-is-transforming-medicine/#28d063381cd4
Companies which have process manufacturing as an activity to do on their list, face potential challenges with ERP. Process manufacturers need highly customized ERP packages as every process has its own special features. If the ERP package that you choose doesn't have all the functionalities to handle your needs and wants, then it can result in an expensive customization which will result in a significant hike in your costs. Traceability is a very important aspect as well. Manufacturing ERP systems need to be robust for effective tracking. Documentation is another important factor which should be kept in mind. In addition to this, process manufacturing ERP must have features for quality assurance and quality management. Read the complete article at - http://it.toolbox.com/blogs/inside-erp/process-manufacturing-erp-71400
