Predictive modelling software is known for training the model with the dataset with known results to predict outcomes for the new data. The two common types of predictive models are, classification model (example, predict outcome when a component fails) and regression models (predicts a number). The benefits of predictive analysis are: • Improved production efficiency: It allows for effective inventory forecasting, production rates for meeting demand, and the like. • Improved Decision making: It identifies patterns and trends for the data, enabling easy decision making. • Enhanced risk reduction: Predictive analysis, as the name suggests, enables prediction about the future. This is most helpful for a firm to save it from the upcoming risks. • Enhanced fraud detection: Being aware of the trend, a change becomes helpful in detection of fraud. • Targeted, personalized marketing campaigns: Predictive analysis helps in knowing the structure of the market and helps in closely targeting and personalizing marketing campaigns to attract customers. Read more at: https://blogs.opentext.com/predictive-analytics/

Improvement is a non ending necessity for any business to survive in this competitive industry. It is well known that bringing right technological change in business cuts the cost of production specially when the human labor costs too high and are replaceable with technology. In various surveys, it has been found that HR spends most of the time in transactional jobs such as data entry and minor mathematical calculations. These tasks can be automated by bots and this human resource can be provided proper certifications and they can be utilized for better tasks. There was a survey done in an organization. They implemented technology which improved the onboarding time of people from 120 minutes to 3 minutes. And the man power can be used for more important tasks such as decision making.

Read more at https://www.informationweek.com/big-data/ai-machine-learning/how-ai-and-intelligent-automation-impact-hr-practices/a/d-id/1332484?

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

Predictive analytics solutions promise to help predict customer behavior. It requires more complex inputs, analyses and interpretations than many other systems. Good predictive analytics can become a powerful tool in the marketing process to help marketers make better decisions about the content they serve their customers. Tjeerd Brenninkmeijer (co-founder of Hippo) and Arjé Cahn (co-founder of Hippo, Amsterdam, Boston), write in this article link about why predictive analytics can become powerful and unavoidable force in the content market. Read more at: http://www.cmswire.com/digital-marketing/get-ready-for-predictive-content-analytics/

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

It is not easy to understand what a dataset represents and how to use that information. One can understand the data by interacting people who know about data and through other alternative means. Usually the problem of finding and understanding the data exists in many companies which leverages data to enhance their decision making and their products. Many technologies relating to data made it easier to process massive amount of data and visualizing results in a better way. But the absence of focus on empowering users to find and understand data has made it difficult for reliability and efficiency. It has been more concrete to measure and show progress of efficiency and visualization capabilities of the process. Read more at:http://www.smartdatacollective.com/venky-ganti/332052/data-time-investment-finding-and-understanding-data

Decision making is powerful when data is coupled with insight. A survey revealed that 46% of CFOs admitted to making important decisions based solely on their gut, out of which 15% blamed poor decision making on inaccessibility of internal data. Only 23% make the effort to perform data mining for decision making. In order to make way in the competitive world, a combination of both is required for survival. According to Duncan Watts, a Microsoft researcher, ignoring data while making predictions can lead to huge losses. Both insight and data are equally important to earn better revenues. Read more at: http://www.cmswire.com/analytics/many-managers-mistrust-data-infographic/

 According to Dave Blanchard (Industryweek), customer’s demands for lower delivered costs are seen to be a big challenge. But, by using predictive analysis, we can identify patterns and predict future events. An organization using predictive analysis can make better decisions. George Prest (CEO of MHI) says that companies that continue to use traditional supply chain models will struggle in the future.  According to the MHI/Deloitte study, it was found that less than 25% of companies have adopted predictive analytics though that number is expected to climb to 70% over the next three to five years.

 Read more at: http://www.industryweek.com/supply-chain/predictive-analytics-let-manufacturers-see-more-clearly-their-supply-chains

Lean Thinking is a form of structured thinking that aims to provide a new way to think about organizing processes to deliver benefits to the organization while eliminating waste. Understanding this concept forms a base for deciding what actions to take, what to change, what to do. 

Pascal Dennis, in his book Getting the Right Things Done, has easily explained lean thinking in 10 words:

· “What should be happening?”

· “What is actually happening?”

· “Please explain.”

      To know more, read this article by Mark Rosenthal: http://theleanthinker.com/2015/02/13/lean-thinking-in-10-words/

The Bring Your Own Device (BYOD) is responsible for giving rise to the current trend in enterprise mobility. Mobility improves employee productivity, employee satisfaction, and decision making and is an extension of customer relationship management (CRM). Petra Jorgenson (Analyst with Studio B), writes about some points which one needs to consider before one allows employees to access system from their personal devices. Read more at: http://it.toolbox.com/blogs/insidecrm/3-things-you-need-to-consider-to-support-mobile-crm-in-a-byod-environment-66153

Consumer choice process has two steps. Firstly, he assigns a value to each product according to his willingness to pay. Secondly, the consumer compares and ranks these values. People evaluate the stimuli that they see even if they do not have to choose. Marketing may not necessarily need a billboard or commercial in order to materialize. It may extol the positive values of a product.  A consumer may subconsciously value a product anyway.  There are certain aspects that a company's advertisers have to worry about besides what its competitors say or how they advertise because if consumers automatically assign values to products, then marketing may not save a product that has many deficiencies. This may lay out cost effective opportunity to the present advertisers. Companies can highlight some positive virtues of a product on the product itself. Read more at: 

Inability to take decisions is one of the principal reasons for the failure of any project. Good project managers need to have proper understanding of various effective tools to manage triple constraints of any project i.e. time, cost and scope. In this article tools like SWOT Analysis, Maslow's Pyramid, Pareto Principle, Monte Carlo Simulation and Decision Tree Analysis are discussed. In the field of project management, these are regarded as highly effective tools for decision making. Please go through the link for more details: http://www.projecttimes.com/articles/decision-making-models-in-project-management.html

Inability to take decisions is one of the principal reasons for the failure of any project. Good project managers need to have proper understanding of various effective tools to manage triple constraints of any project i.e. time, cost and scope. In this article tools like SWOT Analysis, Maslow's Pyramid, Pareto Principle, Monte Carlo Simulation and Decision Tree Analysis are discussed. In the field of project management, these are regarded as highly effective tools for decision making. Please go through the link for more details: http://www.projecttimes.com/articles/decision-making-models-in-project-management.html

Decision trees can be generated based on project schedules. This article shows how to use decision tree analysis in project management as part of quantitative risk and decision analysis.

Please go through the link for more details: http://www.intaver.com/Articles/Article_DecisionTree.pdf

Guy Cuthbert, managing director at visual analytics firm Atheon Analytics, says that many of the firms selectively choose data to back up currently held views, which is just opinion based decision-making. The firms usually prefer to take decisions using untested opinions rather than data science. By applying data science, firms can deliver insights which are intrinsically more valuable than what they have today.To know more on data science, go through the article of Toby Wolpe, senior reporter at ZDNet in London:

http://www.zdnet.com/big-data-what-to-trust-data-science-or-the-bosss-sixth-sense-7000026550/

Most retail companies today operate in the master franchisee model where master franchisee manages the operations of the different stores of the brand. In such cases, it is important to understand the difference between what the company sells to its franchise stores and what the stores in-turn sell to the consumer. This metric is where the insights are hidden. Hence it is very important that companies track &understand this metric to optimally manage stocks and avoid costly stock-out or over-stock situations. Important decisions like the number of new stores that should be opened, best location & size of each store in different cities are not a straightforward choice! Data analytics usage in this space has proved to solve this dilemma. It helps companies to decide the optimal size of each new store they open, while ensuring that these decisions takes into account key parameters like real estate rental costs, catchment area, average incomes in the geographical area, presence of competition etc. Therefore, data analytics is simplifying complex business problems.

Arvind Nagpal wrote about this aspect in his recent article. To read more, please visit the following link:

http://www.informationweek.in/informationweek/perspective/288161/analytics-helping-retail-outlets-critical-decision

The visual data discovery (VDD) approach increases the probability that business managers will find the information they need in time to make effective decisions. This is the main reason behind VDD’s rapidly growing fan base. While traditional BI solutions can be invaluable, they do have limitations. Dashboards come in handy in few cases, but there is still a fundamental problem – the dashboard designer must have anticipated the need for that line of questioning or train of thought when the dashboard was originally built.  So, if unexpected questions arise, business managers end up making decisions without data, operating on gut feel alone.  And that means they end up operating without real answers at all! So, here’s the key question:  Do you ever get questions in your logistics operations that are unexpected and hard to answer? This is where visual data discovery tools excel.Visual data discovery tools provide a rich, visual environment that enables business users to easily interact with, and manipulate, information directly, hands-on.  They provide a compelling solution to help business managers explore data, without the assistance of skilled IT staff.

To know more, visit David White’s article by clicking on the following link:

http://logisticsviewpoints.com/2014/04/07/new-approaches-analytics-revolutionize-logistics/