A very little time is spent on thinking about how to deploy a data science model into production. As a result, many companies fail to earn the value that comes from their efforts and investments. In production environment data continuously comes, result are computed and models are frequently trained. The challenges faced by companies fall into four categories:  Small Data Teams: They mostly use small data, often don’t retrain models and business team is involved in a development project. 

Packagers: Often build their framework from scratch and practice informal A/B testing , generally not involved with the business team

Industrialization Maniacs: These teams are IT led and automated process for deployment and maintenance , business team are not involved in monitoring and development

The Big Data Lab : Uses more complex technologies , business teams are involved before and after deployment of data product

Companies should understand that working in production is different than working with SQL databases in development , moreover real time learning and multi-language environments will make your process complex. Also a strong collaboration between business and IT teams will increase your efficiency. Read more at : http://dataconomy.com/2017/02/value-from-data-science-production/

Nowadays, agile customer relationship management systems are gaining importance. Today's business professionals must take full advantage of every possible revenue stream and with the right CRM system, leveraging big data to gain a better understanding of your clients and maintaining customer relationships has become easier now. This article discusses the five most important CRM trends for the year 2017. They are: Mobile CRM and Telecommuting, Mass Cloud Migration, Scalability, CRM Integration, and Artificial Intelligence. Read more at: http://it.toolbox.com/blogs/insidecrm/5-important-crm-trends-for-2017-75959

Nowadays, social media has become a central part of CRM. So, good posts in social media have gained its importance. An organization must be active on social media to recognition, which will in turn help them in selling their product. It also creates value for your followers. This article article link discusses about some important guidelines for posting on the social media. Read more at: http://it.toolbox.com/blogs/insidecrm/ten-rules-for-social-media-posts-75929

According to a research, it was found that the Human Capital Management market will grow by 10% annually through 2020. Another research found that 1. Real-time feedback and analytics will boom, and it will improve employee productivity. 2. A new generation of performance management tools will emerge to improve feedback-based approach. 3. A focus on human performance and well being will become a more critical part of HR, as it will emphasize employee wellness and engagement, and a healthy work-life balance, and 4. The employee experience will become a primary focus of HR to provide integrated, high-value experiences that excite, engage, and inspire employees. Read more at: http://it.toolbox.com/blogs/insidecrm/mobilefirst-cloudfirst-crm-75930

CRM has come a long way from a contact management system to a tool that organizations can use to create and build a meaningful customer relationships. But, the question is that how will it is how’s that going to change during 2017? This article explores the CRM trends that you should watch this year. Read more at: http://it.toolbox.com/blogs/insidecrm/crm-in-2017-4-trends-to-watch-75879

In a recent research, it was found that email has the highest conversion rate (66%) of any marketing message type (social and direct mail) to become buyers. Information is important for creating intelligent, successful and profitable email campaign.  Hence, gathering information is very critical. It was also found that personalized mailings have 29% higher unique open rates and 41% higher unique click rates than generic mailings. This is where the power of customer resource management is important as CRM can give you the critical customer metrics needed to create email campaigns that will increase your email’s open and click-through rates. Read more at: http://it.toolbox.com/blogs/insidecrm/use-crm-to-optimize-your-email-strategy-75867

A business hypothesis is a foundational theoretical concept whose good understanding helps you to achieve business goals. For instance, it provides a mathematical way to answer questions like whether you should spend on advertising or whether increasing a price of a product will affect your customers. Data collection is one part of the game, but correct data processing and interpretation is the final stage of your decision-making process. Hypothesis testing is used to infer whether there is enough data to support evidence . There are various test methods : Parametric Tests - z-test, t-test, f-test. Non Parametric Tests - Wilcoxon Rank-sum test, Kruskal-Wallis test and permutation test.

Read more at : http://www.datasciencecentral.com/profiles/blogs/importance-of-hypothesis-testing-in-quality-management

You can’t be a good data scientist unless you have a good hold on statistics and have a way around data. Here are some simple tips to be an effective data scientist:
Statistical Methods Should Enable Data to Answer Scientific Questions - Inexperienced data scientists tend to take for granted the link between data and scientific issues and hence often jump directly to a technique based on data structure rather than scientific goal.
Signals Always Come with Noise - Before working on data, it should be analysed and the actual usable data should be extracted from it.
Data Quality Matters - Many novice data scientists ignore this fact and tend to use any kind of data available to them, if always a good practice to set norms for quality of data.
Check Your Assumptions - The assumptions you make tend to affect your output equally as your data and hence you need to take special care while making any assumption as it will affect your whole model as well as results.
These are some of the things to keep in mind when working around with data. To know more you can read the full article by Vincent Granville athttp://www.datasciencecentral.com/profiles/blogs/ten-simple-rules-for-effective-statistical-practice

The CRM screen of any organization must help. But, most of the time, it doesn’t function properly. The problems arise because of lack of foresight and aging system. The basic CRM system should be updated to keep up with the changes in organizations. Read more at: http://it.toolbox.com/blogs/insidecrm/screen-organization-with-crm-75717

Things get corrupted over time. In the world of CRM, it is called bad data. Organizations must start by cleaning the data. In a research, it was found that CRM databases, corrupt at the rate of at least 10% a year. Organizations must regularly maintain their database, both on a day-to-day basis and through cleaning at regular intervals. Sometimes, databases accumulate outdated or irrelevant data. Know more about the bad data and how to clean them at: http://it.toolbox.com/blogs/insidecrm/crm-cleaning-75709

Marketing automation helps CRM to build business. Market automation automate workflows and other tasks associated with the marketing function and also include automating marketing campaigns and routine marketing functions. It doesn't replace CRM, but it supplements it by working very closely with the existing CRM system. In a nutshell, a complete market automation package provides a range of features for supporting the marketing function. Read more at: http://it.toolbox.com/blogs/insidecrm/complementing-crm-with-marketing-automation-75560

Companies nowadays are integrating social media seriously. They are also integrating social media efforts with your other sales and marketing team. The first social media strategy is to define the goals, and then define the objectives. It's important for companies to take a broad, integrated, approach to social media efforts. Read more at: http://it.toolbox.com/blogs/insidecrm/getting-serious-with-social-media-75572

Recommender systems have become the backbone of the ecommerce sector. They have helped companies like Amazon and Netflix to increase their revenue to as much as 10% to 25%.
And hence the need of the hour is to optimize their performance.
So, what are recommenders? Recommenders are the applications which personalize your customer’s shopping experience by recommending next best options in light of their recent buying or browsing activity. Recent developments in analytics and machine learning have let to many state of the art recommender systems.
Types of Recommenders: There are broadly five types of recommender systems, which are as follow:
1. Most Popular Item
2. Association and Market Basket Models
3. Content Filtering
4. Collaborative Filtering
5. Hybrid Models

In coming years, recommender system will be used by almost every organisation, whether it's big or small, and will become an inseparable part of the ecommerce world.

To know more read the article by William Vorhies at: http://www.datasciencecentral.com/profiles/blogs/understanding-and-selecting-recommenders-1

 2016 has been the year of deep learning, some big breakthrough were achieved in 2016 by Google and DeepMind.Some of the most significant achievements are as follow :

 AlphaGo triumphs Go showdown : AlphaGo the google’s AI for the game Go to everyone’s surprise was able to beat Go champion Lee Sedol.

 Bots kicking our butts in StarCraft : DeepMind AI bots were able to outperform some of the top rated StarCraft II players.

 DIY deep learning for Tic Tac Toe : AlphaToe a AI bot was able to outperform most of the people that played with it.

 Google’s Multilingual Neural Machine Translation : Google was able to make a model which is capable of translating text b/w languages, reaching a new milestone in linguistics and NLP.

 Hence , in a nutshell , 2016 was the year for Deep Learning and a lot of unachievable milestone were conquered during the annual year.

 To know more you can read the full article by Precy Kwan at http://www.datasciencecentral.com/profiles/blogs/year-in-review-deep-learning-2016

Machine Learning is the backbone of today’s insights on customer, products, costs and revenues which learns from the data provided to its algorithms. And hence algorithms are the next most important thing in data science after data.
Hence , the question which algorithm to use ? Some of the most used algorithms and their use cases are as follow :

1) Decision Trees - It’s output is easy to understand and can be used for Investment decision ,Customer churn ,Banks loan defaulters,etc.

2) Logistic Regression - It’s a powerful way of modeling a binomial outcome with one or more explanatory variables and can be used for Predicting the Customer Churn, Credit Scoring & Fraud Detection, Measuring the effectiveness of marketing campaigns, etc. ,

3) Support Vector Machines - It’s a supervised machine learning technique that is widely used in pattern recognition and classification problems and can be used for detecting persons with common diseases such as diabetes, hand-written character recognition, text categorization, etc. ,

4)Random Forest: It’s an ensemble of decision trees and can solve both regression and classification problems with large data sets and used in applications such as Predict patients for high risks, Predict parts failures in manufacturing, Predict loan defaulters, etc.

Hence based on your need and size of your dataset , you can use the algorithm that is best for your application or problem.
You can read the full article by Sandeep Raut at http://www.datasciencecentral.com/profiles/blogs/want-to-know-how-to-choose-machine-learning-algorithm

 The first block to build a winning business strategy is to create a map based on business value of the question and approximating how much time would it take to get high quality answers to that question. The idea is to break the business questions into groups that corresponds to real time data systems. It allows you to focus on a specific system at once to build a strong strategy and optimize the sequence in which each sub question needs to be answered depending upon its current business value. A pattern of actions for data strategy begins with a hypothesis and collection of relevant data followed by building models to explain the data and evaluating its credibility for future predictions. The entire process is achieved on an enterprise scale digital infrastructure using Industrialized Machine Learning (IML). This approach can have a huge impact on natural resources and healthcare industries as well.

Read more at : https://blogs.csc.com/2016/07/05/how-to-build-and-execute-a-real-data-strategy/

Customer Relationship Management (CRM) software is important for any sales team as it gives 360-degree view of customers. But, sometimes they could face some problems which in turn prevent them from reaching their goals. This article by Susan J. Owens (content creator) explores the top challenges that are threatening the performance of your CRM system and how to overcome them. Read more at: http://it.toolbox.com/blogs/insidecrm/top-challenges-for-crm-marketers-in-2017-75446

Big Data Analytics and Business Intelligence is changing the way business interacts with customers. Modern big data solutions have enabled automated decision making in debt management systems for client handling processes. Correct implementation of these tools provides a more personalized experience to each customer and avoid infringements. Debt management automation has been proven a successful solution to maintain balance between meticulous efficiency and customer satisfaction. Such a CRM automates a lot of process and thus it requires a small team days to complete debt collection process. Analytics have not just accelerated debt collection, but also enhanced customer relations.

You can read more at: http://www.dataminingblog.com/what-could-big-data-mean-for-debt-management/

Global markets are becoming more complex each day, and therefore, it has become essential for business intelligence teams to apply advanced methods for data interpretation. They believe that only the decisions based on quantitative data can be justified. Although there are some ways quantitative research may go wrong, the truth comes out only when you meet people, talk to them, involve them in creative exercises.

Read more at: http://www.dataversity.net/science-big-data-art-interpretation/

Data is the backbone of analytics and machine learning and hence one of the most important tasks in analytics is to get the right kind of data and in the required format.The importance of data can be understood by the fact that around 60 to 80 percent of the time of an analyst is spent in preparing the data.
What exactly is data preparation? In a nutshell, it is the process  of collecting, cleaning, processing and consolidating the data for use in analysis. It enriches the data, transforms it and improves the accuracy of the outcome.
How is it done? It is mostly done through analytics or traditional extract, transform and load (ETL) tools. ETL tools include self-service data preparation tools, data cleansing and manipulation tools, etc.
Since data is the foundation of the analytics, right data will helps in analysing the situation better and help organizations in reacting positively to the market shifts.
To know more read the full article by Ashish Sukhadeve (business analytics professional) at: http://www.datasciencecentral.com/profiles/blogs/why-data-preparation-should-not-be-overlooked