SigmaWay Blog

Today, "big data" is associated closely with profit maximization techniques (such as recommendation lists on e-commerce sites and targeted ads), high-profile data leaks and privacy issues. However, not all data are bad. Here are the positive side of Data Science:  LOGISTICS: e.g.: Airlines Schedule Flights predict delays based on precise weather forecasts, and other market and political happenings. HEALTHCARE : Big data power the idea of self-learning healthcare programs, which will be able to interpret the data of individual patients: not only their gender, age, weight, and medical history, but also their lifestyle, habits, preferences and give the personalized recommendation about adjustments that would be the most beneficial.  FACE RECOGNITION: Long time ago, it was tough to even think about it, but nowadays, Many tools are there which capture details of the current face and match it with millions of faces in about no time.  Self- Drive Cars : It has made life much easier. Either you are driving or you are on a walk, you don't have to worry at even new places. Many taxi companies are dependent on this and even self-driving cars are also there which is dependent. It finds shortest routes, routes without traffic, etc. in about no time. Read more at: http://www.datasciencecentral.com/profiles/blogs/how-data-science-has-changed-everyday-life-for-the-better

CRM is the major contributor of many businesses, but sometimes it turns out to be an inefficient and expensive sales killer. The truth behind this is most CRM platforms aren’t intended to help the end users who work with them day in and day out that is your sales representative. Most of them waste time in doing manual data entry. When there is poor execution of CRM, the organization has an insufficient picture of how it is selling its products. The data we are seeing is incomplete and possibly misleading. To know more, please read the following article: http://www.business2community.com/big-data/4-reasons-big-data-failed-impact-sales-01787213#R6gklCOP8mL83XgS.97

Big Data is the Buzzword of 21st century as we know it and has been extremely useful in several risk assessment tasks. The effectiveness of Big data on risk management depends on accuracy,consistency ,completeness and timeliness of data. Some most common mistakes made by Big Data experts who are involved in risk management are : Confirmation Bias : It occurs when data scientists use limited data to prove their hypothesis.

Selection Bias : When data is selected subjectively, Analyst comes up with the questions and thus almost picking the data that is going to be received ( Ex : Surveys) 

Outliers : Outliers are often interpreted as normal data

Simpson’s Paradox : When group of data points to one trend, but can reverse when they are combined

Confounding Variables are overlooked

Analyst assume bell curve

Overfitting and Underfitting models

Read more at : http://dataconomy.com/2017/01/7-mistakes-big-data-analysis/

How will these emerging technologies and evolving business models be adapted to and used in developing countries? Consider three trends that are rapidly developing, both in the logistics space and elsewhere: the Omni-Channel Approach, the Sharing Economy and Big Data.Shipwire provides a logistics marketplace of value-added services, allowing companies to send inventory to any warehouse and store on demand, by providing integrated order-entry systems that handle the pickup and delivery of goods.In this case, our “learning laboratory” looked at how emerging technologies and evolving business models can transform logistics systems – not just in advanced economies like Singapore, but also in developing countries in the East Asia and Pacific region and beyond.You can read more at:http://blogs.worldbank.org/trade/future-here-technology-trends-currently-shaping-world-logistics

The word Big Data is connected with 4 Vs' Velocity, Volume, Variety, Veracity and each V plays a significant part in the Big Data world. The event that combines all these components, paints a clarified picture of what big data actually means. Big Data management methods adopted by many companies involve various stages: 1. Collecting Data: It includes accumulation of data from various information sources. 2. Store: It includes storing data in the appropriate database framework and server 3. Information Organization: It involves masterminding information on the premise of Organized, unstructured and semi-unstructured data. Read more at : http://www.bigdatanews.com/profiles/blogs/how-to-collect-big-data-big-data-a-new-digital-trend

Big data refers to huge amount of structured and unstructured data collected from multiple sources and devices, Explosion of Internet of things is expected to connect 26 billion devices by 2020. There have always been two challenges : Organizing all information in a warehouse so that it can be fetched and processed efficiently . Second processing it in a way that it will provide meaningful results. It turns out only 58% company is understanding the value of their big data solutions. In contrast, small data address a specific problem in limited domains. It tends to focus on log analysis like user behavior on a website. A logging mechanism allows to capture specialized data for business teams and engineers without the need to dig into the ocean of big data. You can read more at : http://www.datasciencecentral.com/profiles/blogs/how-big-data-is-becoming-smaller-than-small-data

The emergence of massive unstructured data sources like Facebook and Twitter has created a need to develop distributed processing systems for Big Data Analytics. Hadoop (A Java based programming framework) has become the first choice of developers and industry experts mainly because its: Highly scalable, flexible, and cheap. An application is broken down into various small parts which runs on thousands of nodes to achieve fast computing speed and reduce overall operation time. Hadoop architecture continues to operate even if a node fails. Its incredible design allows you to process large volumes of data and extract computationally difficult features of users/customers.

Read more at : http://www.datasciencecentral.com/forum/topics/how-to-use-hadoop-for-data-science

Modern needs of Big data consumption require data integration before data actually hit the business intelligence tools. This includes leveraging complex and unstructured data and enables raw data to flow securely through business. Today, even the smallest companies produce huge amount of data across systems which need to communicate with each other and therefore requires a platform to pipe all these data sources into Data Lakes.

Read more at: http://www.dataversity.net/dont-put-cart-horse-comes-big-data/

Today world has become smaller and faster, with increasing computation speed decisions are done in seconds instead of days. Product information and comparison is available on any device any time. Real Time analytics involve solving problems quickly as they happen or even before they happen. Companies now have more insights into their assets. Several industries are using streaming data and putting real time analytics. The big data revolution has further accelerated the demand of real time analytics to analyze customer behavior. Gone are the days when decisions were based on data stored on a disk , actions are taken on streaming data. Read more at: http://www.datasciencecentral.com/profiles/blogs/do-you-know-what-is-powerful-real-time-analytics

We all get those helpless looks on our face when a stack of information and numbers are thrown right in front of us which are not only difficult to understand but includes intense labour in order to be managed.

People who are regular at managing and working with data can also get lost in this labyrinth of information and end up drawing farcical conclusions. However, such troubling days are finally over. As the ways to collect and process data increases, so does the insights. So, it is high time that we ditched the traditional method of basing decisions on past experience and anecdotes in order to sell a product, and used the art of big data management to ensure better product management

Read more at : http://www.business2community.com/big-data/use-big-data-sell-account-management-01679323#yhUtXuzWvCDbDYI4.99

Most of companies are facing problem with big data. The top 5 Big Data problems are: - To get correct insights from the data, Data silos make it time consuming to draw conclusions from it, Unreliable data, Speedy technology, and Lack of skilled personnel. Thus, if decisions are not taken at a high pace it can affect the outcome as Big Data is fast data. The solution to the above problem is Data Integration. This allows to get deeper insight from Big Data. This will also increase the trustworthiness of the data. Now to integrate data it is of utmost importance to use clean data. This can be done by removing duplicates, verifying and updating data on a timely basis and implementing consistent data entry.

To read more follow: http://www.business2community.com/big-data/top-5-problems-big-data-solve-01597918#f8ZXhxxChp3ySQwP.97

 Customers get to know about any product from the sales professionals, and sales people use certain key tips to attract the customers. Now, as the technology is advanced, people get to know about all the products, specifications, alternatives and the reviews from the customers who already used it through social media. Sales people use big data have to research on customer behavior. Big data have transformed customer behaviors. Nowadays, sales and marketing professionals also take advantage of these conversions to study the customer behavior and plan accordingly for the promotions and advertising events. Read more at: 

http://bigdataanalyticsnews.com/big-data-has-proved-to-be-the-absolute-enhancer-of-business-sales/

In the task of predictive analysis, predicting the unknown itself is a challenging problem. Moreover, the entry of an unknown variable in the equation makes the task all the more troublesome. Summary-level data are generally inaccurate and lack deep insights, because of which sometimes such unknown variables manage to creep in. Buyer life cycles generally vary in length in spite of which analysts generally tend to work with smaller cycles, which is dangerous because sometimes important marketing decisions are taken based on flawed information. B2Bs are also depending on real-time insights and are scrapping linear prediction models. It is noticed that, combining Big Data with traditional CRM information is also not sufficient because data science involves lot of research and experimentation. Hence we can conclude that predictive analysis derives its success from data governance and collection. Read more at: http://www.marketingprofs.com/opinions/2016/30118/predictive-analytics-has-a-scaling-problem-and-bad-data-is-to-blame

Big data Analytic can give real time analysis of huge, complex and continuously increasing dataset. It can be applied to many Business operations .But companies do not use this potential of big data analytics to supply chain management. Company can control the inventory and other supply chain operations more efficiently with help of analytic .for instance, companies can use unstructured data set in the inventory, forecasting and transportation for analysis and new insights can be delivered, like forecasting next date for restocking in supply chain planning. For the last 2 years big data had been used in the supply chain management but there is still a long way to go.

To know more read article by Bernard Marr : http://www.forbes.com/sites/bernardmarr/2016/04/22/how-big-data-and-analytics-are-transforming-supply-chain-management

Companies are looking for talented personnel especially in the analytics and big data sector. According to a new report, start-ups are offering phenomenal packages to data scientists as they give them a competitive advantage. Indian start-ups are willing to pay around 10.8 lacks to a talented analyst. Although the global market has been very fluctuating for the past five years but the need for a specialist is growing with the emerging work in this sector. Demand has grown but the supply has failed to meet .An Experienced Analyst (minimum 5 years) demands 12.3 lac to companies. Kolkata in India holds maximum number of analysts or data scientists. The average package salary of an analyst professional is 9.36 lacks.

To know more: http://economictimes.indiatimes.com/jobs/analytics-big-data-to-see-robust-hiring-high-pay-packets-report/articleshow/51105814.cms

The dilemma of supply chain exists in every sector; even in the pharmaceutical industry. Availability of medicine is an issue because of uncertainty and at the same time one can’t stock the shelves unnecessarily. Analytics tool plays a big role here in terms of efficiency and effectiveness. Analytics had been used for a very long time in this sector but today's analytical tools are quicker, collaborative (to suppliers and customers), advance and give visual effects to the users. FusionOps is a new analytical tool which lets pharmaceuticals analyze the data at one place which was difficult earlier. It also gives users an opportunity to see different scenarios and then take final decision. This tool lets the user anticipate demands, sectors the data and meet the targets on time.

To read more, please visit the following link: http://www.cio.com/article/3063513/heres-why-analytics-is-eating-the-supply-chain.html

All new on-line communication that both consumers and retailers access on a regular basis is creating even more data for retailers to store. It is time to extract valuable information from all the existing data in order to meet customer demands, increase sales and improve business performance.

In the simple terms, predictive analytic is a technique that is used for forecasting. It uses past data like how many products were sold and at what rate? Predictive Analysis is a big help in the retail industry. It doesn't mean that the whole process has to work on automation. Human decision making (sometimes gut feeling or intuition) and software like Predictive Analytic would give even better results. Some constraints in adapting this type of analytics is that decision power is transferred to a machine. This is always a difficulty because downsizing or resistance can also be a result. Analytics work on big data; which is difficult, expensive and can fluctuate.

To read more:http://www.in.techradar.com/news/world-of-tech/What-is-predictive-analytics-ndash-and-should-we-fear-it/articleshow/51945739.cms

Marketing dances the fine line between pure numbers and human touch. No business of a subdivision of business can run without data. Similarly, marketing have been embracing big data to meet their needs as well. Now marketers can successfully judge who is a loyal customer and who is not. Using big data marketers has successfully identified potential customers, their demographics and ways to retain them. They can identify what the customers want and need which the competing company fails to deliver. Analysis of results has also become easier. Read the full article here: http://it.toolbox.com/blogs/hosting-facts/how-big-data-is-transforming-marketing-for-good-71635

Business intelligence provides you a greater customer insight and predictability on a product life cycle. But such digitalization has also some disputes as well. Most people around the world are tech savvy and surfing for a new product with better features. Analytics is growing at a fast pace and is creating a backlog. Day to day new product launch is providing zero visibility on upcoming product life cycle. Removing backlogs to smart productivity as per customer's likability is the new trend of business analytics. So there are challenges like cost, security and infrastructure, availability of skilled professionals. So overcoming challenges in BI and complete leverage on it is the new mantra for business empowerment.

To read, follow: http://www.business-standard.com/article/management/the-challenges-of-business-intelligence-116041000615_1.html

In this digital world at every touch point huge amount of data is being created in seconds that tells us about the needs & wants of consumer. The capability to extract valuable insights from the huge data poses a critical challenge for any modern organization. It has become an more & more important to efficiently & effectively ( both in terms time & labor) to  conduct data mining and extract valuable information out of big data to meet the strategic objective of an organization. In this paper MAS (multiple agent system) concept is being introduced to extract value data from CRM aspect. It also gives us a 3 point model to extract out potential valuable data to manage CRM optimally.Read full article at =_blankhttp://www.csroc.org.tw/journal/JOC26_4/JOC26-4-7.pdf