SigmaWay Blog

Tons of data is generated everyday in the industry. And making sense of this pile of data has become an important task for many businesses. To achieve this, they are turning into Big Data analytics and Data Science. Data Scientists have knowledge about various algorithms suitable for various types of data and these statistical algorithms are implemented in several programming language. Selection of the programming language depends on many factors. So here is the list of top 6 programming languages that are used by most of the data scientists and analysts.

1.  Python

2. R Programming

3. Matlab

4. Java

GST-Network (GSTN) is trying to provide hi-tech security and analytics center to give protect the data under the Goods and Services Tax (GST) for the cyber threats. GSTN will be appointing security companies by August to build up Security Management and Analytics Centre (SMAC). SMAC will implement data analytics to protect the GST system from cyber attacks and will provide a better and protected environment for GST ecosystem. Read more at: http://cio.economictimes.indiatimes.com/news/digital-security/paranoid-about-security-setting-up-analytics-centre-gstn-ceo/59092397

Very often a customer 360 view can be dangerous and distracting as it sets the organization of the track by providing it a false goal to pursue and diverts it from pursuing financially rewarding initiatives. As a consequence, business acquires a constant monitoring stage with their data and analytics investment. Customer 360 view data is not actionable until you don't apply analytics and you can't apply analytics until you know the business problem organization is wanting to address. A more active approach would require focus on identifying the decisions that an organization is trying to make about customers and validate, justify and prioritize those decisions. Read more at : http://www.datasciencecentral.com/profiles/blogs/the-danger-of-pursuing-customer-360-view

It has been evident that a great customer experience is one of the signs of a healthy business model. Machine Learning and Data Analytics are playing a fundamental role in building consumer intelligence systems. It is important to capture data and there is no single magic source to collect data. Telecoms are making billions by selling data. You need to ensure that the data is relevant to business. Once you have the right data, you are ready to model, design and engineer and deploy your 360-degree customer view platform and achieve the enhance customer experience for your organization.

You can read more at: http://www.datasciencecentral.com/m/blogpost?id=6448529%3ABlogPost%3A508502

While developing data strategies in the future, organizations must consider these new emerging trends: 1) Data's center of gravity is moving more to the cloud day by day, which means one should be looking to keep all their data tools for processing to analytics in the cloud. 2) Hybrid data technologies are critical to the cloud system and hybrid data systems like SQL Server, MySQL, etc. is predicted to become the norm by 2018. 3) With new tools for data analysis being innovated, businesses need to connect to many data sources that span across databases, Hadoop ecosystems, and web applications. Read more at: http://data-informed.com/how-to-capitalize-on-the-data-landscape-of-tomorrow/

In a dynamic business environment every organization is seeking profit maximization & risk mitigation approach, the article talks about the latter, in uncertain business environment business leaders at time of complex & critical decision making take decision on the basis of intuition (gut feeling) rather than holistic analysis of situation. Even with data backed decisions there is a narrower approach attached in form of point estimates & averages. In this article a new approach Prescriptive Analysis is used where business are simulating probabilities to reduce risk that helps in robust analysis. This approach gives us broader perspectives showing us a range of possibilities & helps in better decision making.Read Full article at :- Target=_blankhttp://www.forbes.com/sites/gartnergroup/2016/03/24/use-prescriptive-analytics-to-reduce-the-risk-of-decisions/#465f9994785b

If in 1 line I have to summarize the importance of article it will be, the solution is not limited to just analyzing a data, but also making sure it’s understandable & is communicated wisely to the end user for decision making. In the digital world huge amount of data is recorded every second, organization massive amount of time of monetary resources to analyse the data to churn out valuable information for decision making purpose, but unless & until these analysis are communicated in a right manner that could be understandable to the, otherwise its will be a sheer waste of valuable resources of organization. In this regard the article talks about “Info graphics” & the latest innovation in this field that can amalgamate business intelligence with narrative sciences.Read Full article :- http://www.forbes.com/sites/bernardmarr/2016/01/21/why-management-dashboards-and-analytics-will-never-be-the-same-again/#4cce3b7c3963

Big data analytics is becoming self-explanatory day by day to even a lame person who has lesser knowledge in statistics, analytics and data science. It is a positive sign with respect to current business purpose because we need real time analysis and that can be only possible if data interpretation and visualization becomes more feasible. There are different software like R, SAS and many other online service providers like google analytics, Piwik helps to create better data visualization and interpretation of customer and business data. Even our very own Microsoft excel has some in built data interpretation and visualization programs. But one program, called Quill, takes the trend a step further, producing text-based reports that explain the data clearly and concisely.  Think of it as an executive summary created by a computer to explain a set of data at the click of a button. So it’s time for personalize big data analytics.

to read, follow: http://www.forbes.com/sites/bernardmarr/2016/04/27/will-we-soon-no-longer-need-data-scientists/#540a99bf55f3

EMR (electronic Medical Record) adoption, big data and other trends are helping a lot in the generation of data in the healthcare industry. But data is not what drives the healthcare industry- managing this data does. In healthcare, data analytics is done in use cases. Multiple use cases are created according to the need like one while patient got admitted in some department, a use case gets created. To provide best services to patients timely, immediate access to patient’s data without the barrier of time or location. Read the full article here: http://www.computerworld.com/article/3038315/data-analytics/accelerate-time-to-value-with-healthcare-discovery-analytics.html

Technology is fundamentally transforming the world around us, in every aspect of our lives as well as in business. Budget 2016 has shown encouraging initiatives towards the commitment to focus on Digital India initiatives, technology platforms and data analytics to automation. The budget supports the few initiatives like-:

• Fiscal plans to fund the provision of entrepreneurship training across schools, colleges, and through an online program on a large scale

• An online procurement system for food grain procurement

• A new digital literacy mission scheme for rural India to cover 6 crore additional households in three years

To know more, please read the article by Jochelle Mendonca (Writer at ET tech)-:  http://tech.economictimes.indiatimes.com/news/internet/budget-2016-tech-announcements/51191308

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

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.

Some volunteers of a nonprofit volunteer outfit are going door to door in Odisha. They are collecting data about schools, health facilities, Panchayat, roads, etc. These data points are being analyzed by a not-for-profit development organization which will use this data and will draft a development plan for Member of Parliament. The idea is to filter the data into meaningful information which will help in reducing the gap between real needs and actions. Some big consultancies are also working together with politicians for the same purpose. Data analytics tools are used to determine demographic profiles and socioeconomic aims. Read the full article here: http://articles.economictimes.indiatimes.com/2016-02-02/news/70283182_1_data-analytics-tools-prime-minister-narendra-modi

Data analytics is now not only restricted to the IT sector, but also serves other sectors of the society. If we look at the services and other sectors where data analysis can be used then, the list will be very long in today's world. Data analytics is used in each and every sector including sports, politics, medical and financial firms. Some of the predictions of how analytics will shape the business, sports, and political landscape in 2016 are:

Prediction #1: The hottest corporate job of 2016 is data analyst.

Prediction #2: IT will embrace the self-service analytics movement.

Prediction #3: Predictive analytics go mainstream.

Prediction #4: The "Internet of Things" (IoT) will get more organizations interested in geospatial analytics.

Hadoop and Apache Spark are seen as the competitors in the world of big data, but now the growing consensus is that they are better convention in together. Here is a brief look at what they do and how they are compared.  1. They do different things: Both are the big-data frameworks, but they do not serve the same purposes. Hadoop is a distributed data infrastructure. It also Indexes and keep track of that data, enabling big-data processing and analytics. On the other hand, Spark is a data processing tool. Secondly, both can be used individually, without the other. 3. Spark is faster 4. You may not need Spark's speed: Spark is fit for real-time marketing campaigns, online product recommendations, cybersecurity analytics and machine log monitoring. 5. Failure recovery: differently, but still good. Read more at: http://www.computerworld.com/article/3014516/big-data/5-things-to-know-about-hadoop-v-apache-spark.html

Health care providers, lose track of patients as soon as they relocate from the healthcare facilities. The process of relocating within hospitals is equally complicated for patients. A well-defined IT infrastructure can be used to address both the issues. It is important to match patients with their appropriate health care facilities, by collecting and analyzing the data on the patients’ needs. It will also be possible for healthcare providers to draw valuable insights, by collecting and analyzing data on patient transfer procedures. Hence, we have both big data and data analytics, coming into play, by increasing the operational efficiency, but dampening the revenue earnings of the healthcare industry. Read more at: http://healthitanalytics.com/news/big-data-on-patient-transfers-raises-quality-snags-revenue

Permission marketing is now trending. Consumers don’t like to get interrupted often and so they take actions to block online ads, promotion emails. This new era of permission marketing can be broken down into “anticipated”, “personal” and “relevant”. A recently concluded study shows customers are willing to share information with trusted brands. Data privacy acts now protects consumers from personal data collection through websites and other online sources. Cookie law is such an example. Customer data can be explicit, implicit or structured by analytical tools. Thus data security is of utmost importance and it is the responsibility of organizations. Consumers are always looking for their needs to be met by companies and this gives the companies the chance to make the best use of data collected providing value to the organization. Read more at: http://www.cmswire.com/digital-marketing/tracking-customer-data-you-better-provide-value/

Today, fast and accurate decisions are critical for an organization's success. But there is a risk of incorrect decisions if we rely on approximate sciences such as intuition and judgment of individual decision makers. The value of data can be realized only once a coherent ‘data strategy’ is established. A data strategy requires an organization to embed and integrate data analytics into the process of decision-making. The organization must seek and utilize data based insights that are most fruitful. Big Data demands a tighter integration of business functions and better mechanisms for integration. Evidently, various teams shall work together to understand and exploit cross-functional data. Identifying key data gaps and taking collective decisions for data gathering will ensure that specifically data with potentially useful information is collected. Read more at: http://www.bobsguide.com/guide/news/2015/Jul/13/big-data-small-data-and-fast-data-using-data-to-drive-better-decisions.html

As researchers and scientists get access to the plethora of information of weather patterns, they are getting better equipped to deal with upcoming disasters. This data however is massive and highly complex thus the data gathering serves as only a part of the solution. Governments today are realizing the need for data-driven forecasting to keep up with the volume and variety of information required for smarter forecasting. Predicting weather anomalies more effectively could save thousands of lives during natural disasters. The process requires delivering more accurate forecasts and delivering them sooner. Data analysts use the voluminous data gathered to develop reliable forecasting models. Making sense of the data and building actionable intelligence will largely help protect the public from natural disasters. Read more at: http://www.forbes.com/sites/centurylink/2015/07/08/hurricane-forecasts-get-better-give-more-warning-thanks-to-big-data/