Enterprising value(EV) is a better measure of a firm's real market value than market capitalization as it takes debts and cash reserves into account. Market capitalization does not give a clear idea of the amount an investor needs to buy a firm. If a firm has cash reserves, the buyer is incurring lesser costs than the amount he is paying and it is opposite when the firm has debts. EV is the sum of a firm's market capitalization and its net debt. The drawback of EV is that it does not consider the interest costs and income over time. So EBIT is necessary despite the fact that EV gives a rough idea of the true value of a firm. Read more at: http://value-picks.blogspot.in/2017/05/using-enterprise-value-to-compare.html?m=1

According to the author, there has been an increase in global workforce turnovers. Recruiting and retaining skilled employees with high potential is a challenge nowadays. According to statistics, about 87% of the companies are considering redesigning their talent management programs. The best human capital management programs are strategy driven and try to accommodate the following trends. 1. Widespread globalised population. 2. Shifting Demographics. 3. Increased burdens on HRs, which can be relieved by integrating systems and functions .4. Increasing compliance requirements. 5. Increased cost, which may arise due to inability to acquire and retain key talent. Read more at https://www.adp.com/spark/articles/5-trends-that-will-redefine-human-capital-management-7-205

The cohort analysis tool can help to dig in deeper which traffic and page views don't allow. The cohort is grouping because of similarities. It's perfect for finding certain things at a glance. But the website keeps changing very often. When those changes happen, it's important to put these metrics in context. Cohort helps by layering on a filter to add context to the data you're looking at. By viewing those segments, one can produce more accurate findings. Also, cohort sorts the data in every way possible. There is a list of factors, one can analyze using this tool. Cohort analysis allow you to view data by segments of people, helps determine changes and facilitates comparison so that the strategy can be set accordingly. Read more at: https://blog.kissmetrics.com/cohort-analysis-google-analytics/

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 imply huge sets of organized and unstructured information. It is very complex and regular data processing techniques don't work in managing this kind of data sets. In business, the 3V model is utilized to depict enormous information. These 3V's are: volume, velocity, variety. Now let's understand how big data helps businesses and why it is important:

• Examining big data helps understanding the current economic situation so that it can get an idea of getting ahead of it’d competitors.

• Big data analysis helps to understand your customer better, predict what they need in advance and can provide better service.

• You can control your online reputation with the help of doing sentiment analysis.

• Big data tools, save lots of money as it reduces the manpower to handle large amount of data.

Read more at: http://www.business2community.com/big-data/big-data-important-businesses-01818898#DOiRF3OOYiZ3JlAb.97

Optimization has a range of applications. It is best applied when you're deciding among many alternatives. One of the areas where optimization can have a significant impact is planning. To answer these planning questions, businesses face a series of hurdles. First, they need to assemble the data to get a true picture of their business. Second, evaluate options by exploring trade-offs and asking, "what if" questions. Lastly, identifying the best path forward. Businesses can link data, from across their organization in an environment they know and evaluate possible planning scenarios. Organizations can capture more value in the marketplace by improving operations, save money by managing resources more effectively, and mitigate risk by gaining insight into how decisions can impact their business. The power of optimization, however, isn't in finding a path, it's in finding the right path. Read more at: : http://www.ibmbigdatahub.com/blog/what-optimization-and-how-it-improves-planning-outcomes

Segmentation in a CRM system is one of the basic management tools. Dividing the database helps to focus on the markets as well as spot trends and in turn helps sales people to become effective. It can also speed up the prospect of zeroing in on prospects, seek out new opportunities for cross-selling and up-selling and more importantly make your efforts more productive. The advantage of segmenting with CRM is that the system gives many ways to divide up your database. Read more about segmentation at: http://it.toolbox.com/blogs/insidecrm/slicing-and-dicing-with-crm-76197

Interactions that happen generally in normal retail business are important, but are seldom recorded. Online business has the advantage of recording information like how long a customer spent looking at different items, which items or words they searched, even what items they put into their shopping cart and later removed. But, nowadays, with the help of IoT and advanced analytics, the offline business can track the sentiments of customers and automate the process. This article discusses how IoT can feed customer data into the retail CRM system. Read more at: http://it.toolbox.com/blogs/insidecrm/4-ways-the-iot-can-feed-customer-data-into-retail-crm-databases-76111

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

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/

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/

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/

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/

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

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

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

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

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

Your perspective on data depends on the type of task you want to accomplish. They could be broadly specified as: Analytics : Helps you explore what happened and why.

Monitoring : Looking at things as they occur to find abnormalities.

Prediction : To predict what might happen in future.

Some of the most popular algorithms that can be applied to a predict future trends are :

The Ensemble Model : It uses multiple model output to arrive at a decision , however, one has to understand how to pick correct models and what problem does one want to solve.  

Unsupervised Clustering Algorithms : These algorithms help to group similar people and objects together.

Regression Algorithms:  These are used to predict future values of a product/service

There is no ideal formula to find the best suitable method for predictive analytics. A strong level of business expertise is required to master ‘art’ of predictive modelling. Read more at: http://www.analyticbridge.com/profiles/blogs/the-ultimate-guide-for-choosing-algorithms-for-predictive

The value of data has changed over time. Companies have realized that collecting, analyzing, sharing, selling data and extracting actionable insights is critical to the development of their organization. Geospatial data is captured and analyzed by engineers and product managers to develop creative solutions and thus increasing productivity. People can view the flow of geospatial data from the instant it is collected throughout its lifecycle using a framework known as 'Data Value Chain'. Data intersects with analytics and can turn this information into decisions. A technological ecosystem built around a geospatial system provides new ways to work and reduce costs, accelerate schedules and supply high-value deliverables along the value chain. Read more at : http://dataconomy.com/2017/02/power-of-data-value-chain/