SigmaWay Blog

SigmaWay Blog tries to aggregate original and third party content for the site users. It caters to articles on Process Improvement, Lean Six Sigma, Analytics, Market Intelligence, Training ,IT Services and industries which SigmaWay caters to

Adopting Business Intelligence Tools in SMEs

Often small and medium sized organizations are of the opinion that Business Intelligence tools are too complex and expensive to be implemented that they can survive without them.But the truth is they can get using these tools put you in a competitive advantage over others.If SMEs can manage and harness data they can analyze it to increase their revenue and figure out what is holding them back. SMEs looking forward to establish BI and corporate performance solutions must think about users or consumers of their data and where the data sources are located.Make sure your solution is compatible with all mobile devices.No matter how small your business is , you can always benefit from BI tool.Read more at : http://www.datavizualization.com/blog/bi-tools-for-smes-not-just-maybe-but-definitely

 

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Common Mistakes in Risk Management : Big Data Analytics

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/

 

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Most Common Myths about Stream Data Processing

Data Science experts spend lots of time solving problems using streaming data processing. There are many misconceptions about modern stream process space . Here are few of them There's no streaming without batch :  These limitations existed in earlier version of Apache Storm and are no more relevant in modern stream processing architectures such as Flink. Latency and Throughput: Choose One : A good engineer software like Flink is capable of low latency and high throughput. It has been shown to handle 10s of millions of events per second in a 10-node cluster. Micro-batching means better throughput : Though streaming framework will not rely on batch processing, but it will buffer at the physical level. Exactly once? Completely impossible: Flink is able to provide exactly one state which guarantees under failure by reading both input stream position and the corresponding state of the operator. Earlier traditional data flow had to be interrupted and stored in applications to interact, but new patterns such as CQRS can be developed on continuously flowing data. As the stream processing further evolves we will have more power computational models. You can read more at : http://dataconomy.com/2017/02/stream-processing-myths-debunked/

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Customer 360 View : A Stumbling Block to Effective Business Decision Building

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

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Building Your Digital Banking Platform : A Brief Guide

The growth and development of digital financial services or modern banking platforms as we see today is a continuous evolutionary project unlike traditional software projects , it doesn’t have a beginning or end.To maintain a competitive edge, an organization must consider the following :

User Experience : Every single cent of investment in well designed customer experience would generate a huge rate of return.

Simple Personal Finance Management : A large proportion of banks still cannot offer 100% online account opening facility which shows the potential in this area. Banks should tread carefully and do not rush just for the sake of winning the competition in market.

Bots and Data :  Bots can hold intelligent conversation with your customers using natural language. Integrating bots on your financial platform would help you to achieve competitive edge.

Context : Banks should aim at varying the channel of their services according to the product.Users are more likely to access content on their mobile phones and  banks must take this fact into account.

Modern day banks need to adapt these changes and at the same time take care of current trends and experience of competitors, Simply cherry-picking best solutions doesn’t guarantee long-lasting success.Read more at : http://dataconomy.com/2017/03/choosing-digital-banking-platform/

 

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Global Household Survey Data Collection for Sustainable Development

Household survey data plays a critical role in sustainable development goals on poverty and hunger. Food constitutes for almost 50% of household budget, especially in low income countries. It was found that about 800 million people were chronically undernourished in 2015-16.Thus a proper measure of food consumption is essential for the well being of any population. Practitioners are guided to improve the design of surveys and minimize the cost of the survey. As the technologies become available a global program of survey methodology research will help the global statistical community to explore the domain. You can read more at: https://blogs.worldbank.org/opendata/sdgs-1-2-meeting-demand-more-and-better-household-survey-data

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Latest Technology Trends in Logistics World  

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

 

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Emerging World of Data Driven Logistics 

Advances have been made in applications of self-driving vehicles, automated drones, and embedded sensors. Uses of data are requiring more efficiency from existing infrastructure and challenging the industry to evolve infrastructure for the future. As the industrial internet embeds sensors across a range of products and equipment, companies have been expanding opportunities to react to, service interruptions quickly and access data to develop long-term strategic improvement. You can read more at : https://www.oreilly.com/ideas/the-coming-tipping-point-in-data-driven-logistics

 

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Building Your Digital Banking Platform : A Brief Guide

The growth and development of digital financial services or modern banking platforms as we see today is a continuous evolutionary project unlike traditional software projects , it doesn't have a beginning or end.To maintain a competitive edge, an organization must consider the following : User Experience : Every single cent of investment in well designed customer experience would generate a huge rate of return.

Simple Personal Finance Management : A large proportion of banks still cannot offer 100% online account opening facility which shows the potential in this area. Banks should tread carefully and do not rush just for the sake of winning the competition in the market.

Bots and Data :  Bots can hold intelligent conversation with your customers using natural language. Integrating bots on your financial platform would help you to achieve competitive edge.

Context : Banks should aim at varying the channel of their services according to the product. Users are more likely to access content on their mobile phones and  banks must take this fact into account.

Modern day banks need to adapt these changes and at the same time take care of current trends and experience of the competitors, Simply cherry-picking best solutions doesn’t guarantee long-lasting success.

Read more at : http://dataconomy.com/2017/03/choosing-digital-banking-platform/

 

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Scaling Databases for Enterprise 

Scaling databases for enterprise require to have to integrate wildly disparate data sources, satisfy stakeholders with competing expectations, and find the structure hidden in unstructured data.One has to carefully consider tradeoffs between data integrity and constant uptime, between.You may have a legacy system that stores data in tab-delimited files, unstructured text files coming from handwritten notes, and one or more conventional database management system and data from all of these sources needs to be read by and integrated into a single system.Read full article at : https://www.oreilly.com/ideas/insights-on-scaling-and-integrating-databases

 

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Moving Beyond Data Lakes

Hadoop, Pig and Hive, HBase and other NoSQL point solutions onto Spark, Flink, Drill, and Kafka were built to handle individual aspects of the three V’s of big data (volume, variety, and velocity).If a storage system can scale linearly, then we can put the applications on top of the storage platform. If the application runs where the data is stored, then we don't have to worry about moving the data later to perform analytics.Model of messaging delivered via Kafka and MapR Streams can achieve rates about one million events per second with a minor investment. These technologies take a little time to understand and get comfortable with, but may be worth the investment.You can read more at : https://www.oreilly.com/ideas/using-microservices-to-evolve-beyond-the-data-lake

 

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The Growth of  IoT Market

Adoption of Internet of Things rose dramatically in the year 2016. Factors like the increased numbers of sensors and connected devices, a growing pool of IoT developers, and real-time data and analytics support for IoT systems are a few of the major reasons for its expansion. "The Internet of Things Market", by Aman Naimat, presents a snapshot of IoT culture. It  describes a data-driven analysis of the companies, industries, and workers using IoT technologies. As the volume of data sets grow and more robust computation power evolves and scalability will lead to more IoT breakthroughs which in turn will lead to more  business investment in the future. You can read more at : https://www.oreilly.com/ideas/all-grown-up-the-iot-market-today

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Predictive Social Media Analytics

Social networks have been there in some or the other form since the time humans have started interacting. Social network Theory is the study how people, organizations or groups interact within their networks. To create a network using Twitter trending topic to define each city as a vertex, If there is at least one common trend topic between two cities, there is an edge and each edge is weighted according to the number of trendy topics. Network topology doesn't usually change in such scenario as the number of nodes is fixed few metrics that could be used to infer the node's importance and which could explain the type of predictive analysis are Node centrality, Clustering coefficient and Degree centrality. Social media analysis holds a great potential as the they are becoming more huge and complex each day. Read more at: http://dataconomy.com/2017/01/data-mining-predictive-analytics/

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User Generated Content Marketing

User generated content(UCG) marketing is any type of content that has been put up by unpaid contributors in the form of pictures, videos, tweets etc. rather than the brand itself. It has proven to be a great tool to increase customer engagement. Around 76% social media users think UGC brand is more trustworthy. People who actively participated in UGC campaigns are in the age groups of 25-54. Moreover, sometimes videos created by users on YouTube get 10 times more hits than the actual brand. Even in the digital age word of mouth referrals are the best kind. A successful UGC campaign Increase authenticity, app engagement, app reach and brand loyalty. Read more at : https://www.entrepreneur.com/article/289838

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5 Ws’ of Winning Data Strategy

According to a study, it was found that 78% enterprises agree that data strategy, collection and analysis have potential to fundamentally change the way their business operates. The sole aim of an effective data strategy is to utilize this potential . The 5 questions that one need to answer before building a data strategy are : WHAT is Data Strategy?: It is a strategy that allows you to have a comprehensive vision across the enterprise.

WHY do we need a Data Strategy? :You need a data strategy to find correlations across multiple disparate data sources, predict customer behavior, predicting product or service sales

WHEN should I start or have a Data Strategy?: Answer is NOW.

WHO in our organization should drive this Data Strategy?:Chief Data Officer

WHERE do we start with Data Strategy?:It depends on how the organization is structured , it’s recommended to start it in some business unit.

 Read more at : http://dataconomy.com/2017/01/data-strategy-part-i/

 

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Three Stages of Big Data Collection Methodology

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

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Data Matching and Entity Identification at Scale

Data matching is the task of identifying, matching, and merging records that correspond to the same entities from several source systems. These entities may be people, places, publications or citations, consumer products, or businesses. The major hurdle that encounters while solving this problem is lack of common entity identifiers, easily available information like name, address, etc. that may change over time is usually of low quality and produce poor results with high error rate. Technological advancements in the last decade have made it possible to scale data, matching on large systems that contains millions of records and improved accuracy. You can read more at : http://www.datasciencecentral.com/profiles/blogs/data-matching-entity-identification-resolution-linkage

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From Big Data to Small Data 

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

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Accuracy-Interruptibility Trade off in Predictive Analytics

More accuracy is better, but it may not be a good idea to keep working on a model if you are expecting negligible improvement or cost of accuracy exceeds financial gain. The sole purpose of a data science job is to create financial value and minimize loss by building more accurate models. The guiding regulatory rules say say that if your model is having a negative impact on a customer then it must explain why an individual was so rated. This is a classic tradeoff between accuracy and interpretability. In a regulated industry if someone suffers from your decision and you can’t explain why the prediction model worked that way, your technique is not allowed. A good story telling using data visualization might help you to convince management. Some techniques like Penalized Regression, Generalized Additive Models, Quantile Regression can provide better accuracy and maintaining interpretability. Deep Neural Networks have also proven a successful approach to solve this problem.

You can read in more detail at : http://www.datasciencecentral.com/profiles/blogs/deep-learning-lets-regulated-industries-refocus-on-accuracy

 

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Renovating Sales and Marketing Practices using B2B Ecosystem 

Managing Customer relations and increasing need of collaboration to build profitable business has led to the development of digital B2B ecosystem, which is a community of system working together to serve the needs of customers. These systems allow segmentation of audience and delivering a customized experience to each group. Some components of the B2B ecosystem are Enterprise Resource Planning System, Customer Relationship Management System, Product Information Management System, Order Management System, Marketing Automation System etc. A well-equipped system help marketers to Use Customer Insights to Cross-Sell, Optimize the Order and Reorder Processes, Better Manage Content ,Facilitate Lead Nurturing. In a well established B2B system Sales and Marketing collaborate to have a real time access to latest customer information. You can read in more detail at : http://www.datasciencecentral.com/profiles/blogs/how-b2b-ecosystems-big-data-can-transform-sales-and-marketing

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