Tips for SEO for Recommendation Engines and E-Commerce Websites

When you are going to develop and plan an e-commerce website or online portal, it is mandate to do the proper SEO of that related one. In general, most of the part of the e-commerce websites and portals can be modernized and optimized to maximum in the same manner as of the content websites though there are few problems and issues to be taken care of. In order to avoid such issues arising during the development and search engine optimization process, you can find few tips related mentioned in below.

1.       Deployment and creation of recommendation engines – There are few things that should be considered for effective SEO approach and the needed features and attributes to be coded on the website. If the websites are coded in java scripting language then there are chances that it creates issues with search engines. You may figure out the performance and efficiency including time on a site, pages per visit, conversion rate and more with the help of big data recommendation engine.

2.       Add relevant links and URLs – Related and relevant linking is considered big and huge especially for SEO professionals and experts working in the e-commerce industry with a lot of web pages. Making use of relevant and apt linking in a well presented manner renders broad advantages in arenas beyond SEO especially in e-commerce field as users like such websites and buying and selling online a lot.

3.       Co-relate entry page with bounce rates – In e-commerce industry, get the relevant search traffic reports to demonstrate and represent the search term along with the resultant and final entry pages. Afterwards you can really analyze and determine the bounce rate of both term as well as page combinations in e-commerce industry.

4.       Act like a speed demon – In an e-commerce industry, dealing with innumerous and zillions of web pages every day is not anything out of the normal process for enterprise websites. E-commerce platforms could be overly crushed and hauled by traffic, and on the other side, higher content delivery network solutions are the need and must by e-commerce websites.

5.       Determine and kill redundant and duplicate product URLs – Most of the e-commerce websites are bad in having umpteen numbers of web pages of products and services. Try to find them, re-structure them according to the need and use the correct link structure, and use canonical Meta link tag to narrate duplicates and redundant data with a single URL in e-commerce websites.

There are many experts working in varied internet sales and marketing techniques. In addition, do you know about hotspot that is the Google’s new recommendation engine system? It not only helps in personalizing your local searches but also in bringing results based on the recommendations suggested by your friends and special ones. What to discuss more? The leading search engine made the platform easily available for mobile phones, cell phones and mobile mapping software to be available for all Android mobile operating systems. Apart from the tedious and clumsy set up, people prefer to get the user friendly and search engine friendly interfaces and aggregated reviews for the same. Hotspot is capable enough and full of potential to compete with the established and renowned recommendation engine record especially when it is all about social applications, as evidenced and stated by the Wave and Buzz.

Social commerce and development is quickly becoming a standardized norm in online shopping. Facebook is better positioned and situated to provide a better shopping market place with integrated exciting social features similar to the real realm world. Though, it is not going to happen in the long run. With the open source recommendation engine, it is far better to easy to use, integrate, maintain and blend web enabled applications that are available to enhance the performance efficiently and effectively.

Big Data Recommendation Engine in Retail Industry

Each e-commerce industry is focusing on more use of big data these days to improvise operational efficiency and performance especially in the retail industry even before the term big data existed. Walmart is moving on the same path and understood that by reaping the power of data, it could really streamline as well as consolidate its critical supply chain management to take benefit of economies of scale and robustness, making a restriction on extra inventory costs and its related costs to be incurred upon. It basically passed some of these enabled savings on big data to users in the form of lower prices that in few cases excavated and undermined the retailer’s competition. Well, this was the scenario of early 2000s. After this, retailers have started making use of innovation and creativity in data to deliver not only value added solutions in their offerings but also that could be advantageous for both customers as well as the bottom line users. As compared to other advanced and innovative retailers available at online platform helping users in buying and selling and suggesting them recommendations to them, Amazon is the one that in mid 2000s started using what it actually knew about its users buying preferences and behavior to recommend similar stuff and related things to clients at the checkout point of time.

In today’s challenging and competitive world, big data recommendation engines and data driven supply chain optimization are like the table perils and sticks for most of the retailers. And in last few years, forward thinking and moving retailers endeavor to holocaust an innovative path especially in big data. Based on the chats and conversations conducted with number of retailers and members of Wikibon community and other users, the following big data recommendation applications have been identified amongst the more promising, successful and innovative methods must used mentioned in below.

1.       Dynamic price optimization – Retailers are making use of big data backend techniques and approaches to dynamically price up goods and services at both online as well as offline stores. In its most advanced form, dynamic optimization keeps into consideration umpteen numbers of data streams including supply chain, competitor pricing, inventory data, consumer behavior data and market data to fix and compensate on prices to optimize sales and profits, enhance profit margin along with meeting up with other strategic aims and objectives.

2.       Video enabled product placement analysis and store layout – In order to drive high conversion rates, a bunch of retailers have started examining and thinking about video data, not only the associated metadata with videos but also the content of the video to enhance and make it better in terms of store layout, promotional displays and product displacement criteria’s. In fact, according to a survey, the retailers who are using video to analyze and understand the video data are actually trying to grab attention of a large base of customers not affecting the actual significant sales.

3.       Decision support and staffing analysis – Both national as well as multinational retailers with diversified and geographically spread and scattered workforces usually have long struggled and optimized in-store staffing services. There are many factors that affect staffing prerequisites and needs including promotional campaigns, weather forecasts and time of a particular month, year, week or day. These days, retailers are examining and evaluating data associated with other factors to assure stores are optimally staffed and casted.

Adding more to the point, retailers are using a wide variety of technologies and methods to support big data applications involving usually Hadoop, enterprise data warehouses, immensely parallel analytic databases, data visualization tools and many others. As a conclusion, bigger retailers who have started using 

big data recommendation engine technology to consolidate and streamline operations, examine marketing campaigns, improvise and enhance customer experience, boost sales and optimize profitability to put plans immediately. As stated, the retail industry is like the early innovative users as well as adopters of big data driving those vendors who haven’t even started harnessing data for their own benefits are farther behind dawdlers and slow starters in other industries. In all, retail CIOs at this peak of time should not at all waste their time in bringing together business stakeholders and IT people to lay out a bigger big data vision for the practical and enterprise plans to deploy them. And, the few reckoned leaders offering in the same arena are 

IB Technology, Wipro, Persistent, Polaris, Nucleus, R Systems, Global Logic, Infosys, TCS and Cognizant.

Ifs and Buts of Master Data Management

According to a recent conversation on SOA (Service Oriented Architecture), SaaS (Software as a Service) and mergers and acquisitions have made the creating and maintaining of a precise and apt master data a business acute and insistent. In this post, you will come across many reasons behind adopting master data management solutions and how the process of building a solution can help in the technological implementation and efficiency of the solution. The mere truth is that firms and organizations have been experiencing these days around regulatory compliance, consistent reporting, and a strong deviation towards SOA and SaaS has ignited a great interest in MDM Pharma . Explaining and depicting the importance of MDM, it will help in identifying some of the basics of master data management patterns and the best practices that are evolving. Drilling down into the technical as well as procedural issues involved in master data management, most of the software systems have huge lists of data used, accessed and shared by varied applications that makes up the system. Just for example – a typical ERP system has a customer data, an account master and an item master. Been one of the major assets of any company, master data is not unusual for an enterprise to be obtained primarily to have access to its related customer master data.

Need for managing MDM

As master data is used by multiple applications, a flaw in it can really cause errors in all the applications that use it. Example, an incorrect thing could represent flaws in bills, orders and marketing literature in customer master, all being sent to the wrong address. Even if the master data has no issues, most of the organizations still use just one set of master data. Many firms and enterprises grew through mergers and acquisitions. Each firm you obtain comes with its own master data, item master, customer master and so forth. In many cases, part numbers and customer numbers are addressed by the software that builds the master records, so that the chances of same product or customer having similar kind of identifier in both databases is private and outlandish. Item masters could be even hard to consolidate, especially if the varied parts are bought from different vendors having different vendor numbers. Integrating and merging of master data lists could be a troublesome job to do. The same customer could have different names, addresses, customer numbers, and phone numbers in different databases. Normal database searches and joins would not be able to solve such differences and demands a very diligent and advanced form of tool that acknowledges nick names, typing errors and alternate spellings. The tool must have to reckon and admit that different name variations need to be resolved even if they all are having the same phone number and address. Cleaning up an entire master list can be a baffling job but still there are many positive advantages rendered to your bottom line from a common master list.

·         A unique, single and consolidated bill improves user satisfaction as well as save money.

·         Sending up the same marketing strategy to all customers from multiple customer lists not only irritates the customer but also waste money.

·         Stocking up the same item under distinct part numbers is not only a waste of money along with the shelf space, but could highly drive to artificial shortages.

The recent discussion towards SaaS and SOA makes master data management a severe issue. And, for all such reasons, maintaining a consistent and premium quality set of master data for your enterprise is instantly becoming the need of the hour. In addition, the processes and systems needed to maintain such data is highly well known as master data management. Moreover, MDM phar006Da acknowledges different tools and techniques for using and managing master data covering single copy approach, multiple copies approach needing single maintenance, and continuous merge approach. And, all such things could be easily planned out and dealt with making customers life easy and simple at the expense of a more critical infrastructure to sustain and work for data stewards. And, it could be an acceptable and favorable trade-off that should be done and addressed consciously.

The Need for Data Management and its Confidentiality

One of the most secretive, vital and confidential aspects of any business is data management. The sensitivity, character and nature related to the information make it a highly challenging and demanding job. Likely to data management, data resource management is the creation and implementation of policies, procedures, practices and architectures effectively managing and handling the full data cycle demands and requirements of an enterprise. Master data management covers a broader meaning and encompasses a wide number of opportunities that may not have any direct links and associations with lower level demeanors such as inter and intra relational database management. One of the most famous saying regarding data management is that it is the development, implementation and administration of programs, plans and policies that safeguard, control, boost and render the value of both – data and information assets. There are plenty of opportunities and jobs in data management field including posts could be related to data governance, record, document and content management, data analysis and design, data architecture, data quality management, database management, data security management, business intelligence management, data warehousing, reference data management, master data management, contact data management and Meta data management. In addition, there are few listed units too grouped and consolidated by data management framework.

In management and supervision process, one needs to distinguish and analyze the normal trend of the given data in the compound nomenclature within the knowledge and expression information while assisting in non technical and methodological concept. It not only exists in jobs but also has a strong presence in knowledge management and information management. By somehow, it could be the traditional form of data that is processed and managed overly from the second looks. Along with processing, the main part is left to keep up the extreme apt distinction in between data and derive values at the information scale. Data is always kept secretive so that all the employees whether working for information or data management always pursue the jobs in private channels. It is mandatory for them to maintain their jobs for a longer term and sustain them till their employer’s exit. The vital role of a data manager is to trust and keep a track of the desirable database that contains the data gathered from research and initial operation actions. Research initiatives could be different varying from an enterprise to another, may be in clinical, pharmaceutical, life sciences or health care domains. Though, they all require to be assured of about the basic magnitude and extensiveness including reliability, accuracy and precision of the data so that it can fulfill the values of the expected enterprises. Keen responsible for processing data and applications, data manager also takes care of a wide range of computer applications and database systems to hold up and clear out, compilation and analysis of the parent source and subjective data.

The rapid growth and development in the quantity of the data has raised the chances of risks associated to insufficient processing due to inaccuracy and low quality of the data. Financial creations and endowments need to find ways for the same to effectively distribute the relevant, accurate and valuable information all over the enterprise to their varied processes, members and applications at any time and whenever required. This scenario made a lot of firms put more emphasis on reference data management especially for its targeted and potential ones to transform raw data into valuable information. It is actually this valuable data that a firm needs to improvise its operations.

MDM pharma helps in understanding more the standard definitions of the data and can actually use it to minimize operational risks. Embodied with a data based object and a categorization scheme, each value should be quadrate in terms of analysis and interpretation. The data management is both crucial and vital in observing acquiescence with regulations that brings about better exposure to a given business as well as to market segment and firm. 

MDM Transforms Multiple Data Sources into Single Unit

Master data management process accumulates data and all business information collectively from multiple sources. It then stores all the information gathered into a depot, also known widely as master data. In between these procedures and processes, there is a proclamation and disclosure of the fact that how messy and conjured your workflow could become. The usage of this application will drive you towards the mere fact and truth that some of the existing and new bugs could cause problems in your business actually occurred by inaccurate, inconsistent, irrelevant, and incorrect enterprise data and information. Your data assets are dispersed and sprinkled across varied domains and verticals including people such as employees and management personnel either situated at the main office arena or nearby locations and applications involving enterprise resource planning and customer relation management.

Just think about all the source of information these sources used to keep and gather. Inaccuracy and irrelevancy of records and data is likely to be there especially is there is no as such single system to keep all gathered data all together and on the same hand could filter all the information depicting what stays is consistent and accurate across the entire value and information chain. Conflicting and mismanaging business data will for sure affect business operations, their efficiency and performance and lead to disputes, customer loss, miscommunication and misinterpretation. Master data management software and solutions is in real what exactly are your needs and what you aim for data quality improvisation.

Data quality and source is a significant constituent for the success and progress of this application. Its blended functionalities and capabilities will be of no worth without assuring high quality standards of information. If your firm is about to deploy the system, the prior and foremost thing to do is to determine, evaluate and consolidate all of your data assets. The major purpose is to rectify the wrong stuff, determine what is valuable and unnecessary and remove the chances of redundancy and duplication. This is from where master data management solutions take it a step higher. It’s mandatory and essential to consider few things mentioned in the post. One is to hire an expertise who can take entire responsibility and authority to control and access the company’s data and information, along with safeguarding and preventing business information confidentiality and security at all times. The other reason is to go for proactive data governance to address not only the existing flaws in the system but to annihilate new issues to arise. As a business oriented and centric solution, MDM pharma handles and manages data to smoothen the business process and workflow.

Allowing the sharing of accurate, correct and consistent information through a well defined and systematic approach, master data management solutions are applicable in varied domains including life sciences, health care and pharmaceutical sectors. In general, MDM solutions not only create a good recognition and reputation value for companies at a global level but also are helpful and beneficial in raising efficiency, productivity, and lower down costs and most importantly avoid misunderstanding. All in all, all such factors help firms and organizations achieve a good customer satisfaction rate and for sure high profits and revenues. There are many online firms catering and maintaining proactive data governance and clearing erroneous and incorrect data that creates existing problems and hindering other issues to arise in the long run. And few of them are IBM, Informatica, IB Technology, Oracle and Cognizant. 

Significant Elements of a Quality Master Data Management

A good quality and efficient master data management system in a pharmaceutical firm can significantly improvise the superior quality medicines for patients, net profit status, less recall and rework that can save not only a huge amount of money but also provide a good working environment and norms as per national and international regulations and authorities. Quality management is like a philosophy and it takes thorough understanding, commitment and engagement to introduce as well as deploy the concept. Once acknowledged and applied a good quality and efficient quality management system creates or reshapes a sustainable firm culture that pays off instantly and within less time period.

Some key and significant elements of quality assurance to be taken care of while managing MDM pharma applications are mentioned in the post.

·         Standard operating procedure – Along with preparing forms, manuals and templates, they should be used for keeping a track record and a routine of people.

·         Documentations – It is mandate to build qualitative and sound master files and documents to have a good master data management system for your websites. There should be proper documentation, approval and retention requirements defined for effective communication in between varied processes.

·         Quality management and change control – There is a need to create procedures depicting how to generate new quality documents, review of the documents, change control of existing ones, approvers, document control officer, role of document author and satellite file administrator. In such defined system, the numbering has to be done of different quality documents including templates, audit files, SOPs, manuals, forms, QA agreements, training files, project files and their respective archiving system.

·         Preparation, maintenance and change control of documents – Procedures and processes to be defined entirely focusing on the management of master file documents including control methods, specifications, finished goods, raw materials and packaging specifications like stability files, test reports and formulation to generate during the product registration in the marketplace.

·         Deviation report system – Been a regulatory and authorized requirement to grab all the deviations needed to be in your system in respect to maintain the continual improvisation of your processes and systems. Procedures have to be build describing how to segment the deviations in between audit, production, technical deviations, quality improvements, environmental and customer complaints, safety and health deviations.

·         Selection and Evaluation – During the vendor evaluation and assessment, procedures should be followed for buying raw materials, laboratory supplies, both critical and non critical packaging constituents, engineering supplies and imported finished goods.

·         Vendor certification – The procedure focuses to describe the process and services by which a vendor may be certified to supply materials and services.

·         Product compliant procedure – There should be a strong and sound procedure to cover all facets including logging, receipt, evaluation, reporting and investigation of all complaints received from customers for the advertised and promoted products. The procedure should involve step by step edifications to be followed involving registration and numbering of complaints, evaluation, determination and implementation of preventative activities, handling of counterfeit products and trending of complaints.

·         Rework procedure – Procedure is needed and followed step by step especially when the rework of an in process or finished goods is demanded.  

·         Annual product review – It is must to do annual product review along with evaluation of key trends, data and identify corrective and preventive actions leading to product quality improvements and reporting them to management respectively.            

·         GMP audits – Procedure should be created to describe the procedure of planning, reporting, performing and follow up of different audits for your systems including environmental health, internal quality audit, safety audit, vendor audit and EHS workplace inspection.

There are firms offering and following all norms as mentioned while integrating MDM pharma solutions in small, medium and large sized organizations. Helpful in enhancing productivity and performance of your business, master data management solutions are the need of today’s technological and advanced era.

Background Picture of the Recommendation Engines

gRecommendation engines help users by providing customized write ups suggestions by comparing stuff with the searched history done by users available on the website. It is a very unique and ubiquitous feature provided by the section fronts and home page on websites. Considering from a technical upfront, it is a game of turning and transforming log data that is consistently cascading in from innumerous public facing web servers into comprehensible and logical browsing history, building a reading profile entirely based upon the Metadata associated with each article in that history. Further, compilation could be done of all the recommended lists used by each user and make those list available through a service that can manage umpteen requests per second applicable by millions and zillions of different users. Afterwards, an automated solution has to be designed and implemented on a huge cluster of boxes using a particular tool for primary storage and consolidation. The big data recommendation engine system uses Hadoop technology through elastic MapReduce with tailored Java based logs to reduce and help them to transform the logs, C procedures to read and write highly optimized information and data files and a node. The recommendation algorithm takes benefit of the rich Metadata associated, along with to ascertain that there are many potentially and useful methodologies to discover and browse more. Recommendations version 2 is already in the staging process featuring dynamic and dramatic improvisations in both the time a web page hit takes to influence the technology and effectiveness of the algorithm in terms of computational resources. If resources as well as time will allow, there would definitely be a revamp in the recommendation algorithm that is applied to improvise the quality and effectuality of the recommendations. There is no as such best and ideal way to do the same.

Recently Google has broadened its reach and horizon of its Google+ marketplace, and the company is introducing a new mobile content recommendation technology service empowered by Google+. Such recommendations will not only appear as small widgets at the bottom side of the screen but also be browsed by users as a news website that has enabled this service. The launching partner of the company this time is Forbes, and other people can easily integrate this functionality by simply adding a single line of code to their mobile websites. Publishers and advertisers can manage and very well handle recommendation widgets from their respective Google+ accounts. From that platform only, they can figure out and decide when should the widgets should appear and handle a list of web pages and where the widgets should not appear along with the list of pages that should never ever come up in recommendations.

With time, more and more firms are making use of recommendation engines. For example, Apple has its own engine helping users to find out applications to enjoy and discover more about Apple’s large inventory. Many of the recommendation algorithms used in engines as well as for machine learning process are not at all new and unique. The well known and acknowledged constraints along with the appropriate usages include decision trees, regression, support vector machines, K nearest neighbor, naïve Bayes and neural networks etc. Many of these methodologies have been applied and used to affirm data driven business decisive making for a long period of time. So, if this is the case what is the actual scenario because of which more and more companies are choosing to implement recommendation engines to affirm effective and sound decision making. There are key trends drifting the focus from robust and scalable to enable big data recommendation engines to scale economically, efficiently and technologically. Basically, they put data on an immediate basis to work for consumers as well as for business purposes. The opportunities for their implementation have been improvised and obstacles have reduced down. Adding more, more and more firms are figuring out ways to cost a small fraction of average advertising campaigns, bringing in directly attributable revenues as well as render amazingly short payback results if done in an apt manner.

Advanced Version of Data Management – MDM Solutions

The truth and the basic fact is a business can enhance and improvise its process performance, effectuality and could make better decisions with consolidated data. This is the main reason that firms and companies worldwide are looking for solutions that can not only cleanse, harmonize, integrate and synchronize the enterprise master data but also could efficiently manage the data on different categories including customers, suppliers and products. Organizations and businesses are mainly looking for ways to annihilate data flaws, errors and redundancies. In reality, they are looking for accurate, reliable and authentic enterprise data to flow through their systems, processes and applications. Using a process and result oriented approach assists companies to analyze and decide the apt master data and ongoing control changes occurring on. Following a process driven and result driven master data management solution, businesses can actually get trustworthy and reliable data they can count upon. The few major key capabilities to be considered while applying master data management solutions for life sciences, pharmaceutical and healthcare domains are mentioned in brief in below.

1.       Analysis and Modeling – A well defined and planned methodology and strategy should be taken further on data, processes, procedures and business rules. During the pre – master data management phase, a firm on one hand must quickly consolidate and retrieve the data and on the other hand, should analyze and report Metadata from existing repositories to get a broader and comprehensive view of the data.

2.       Reporting – Always look for a master data management solution having an open and non proprietary repository. Along with fully capable to develop reports by making use of third party reporting tools, it is easier to develop customized reports on Metadata content including workflow rules and associations, and user/role allocations. Make sure that you can handle and manage all domains of master data including information available with products, customers, partners and suppliers. The best-in long run master data management solution will provide the truth behind all analytical and operational systems. In order to easily manage and adjourn remote databases as well as applications, a well structured approach assures that you will be able to determine the control ongoing modifications and right master data.

3.       Acquisition and Deployment – Look for a solution that both imports and exports the data in a robust, effective and scalable way. A wizard should be there that can help in loading data and structured from other files and applications, fixing and maintaining all the dependencies during the entire import data process. The deployment capabilities and niches helps to distribute the repository content to subscribing networks and speedily align all other applications without any need to do programming. Structural changes induce to downstream systems and fully capable to distribute content through web services, XML services, direct updates, JMS messaging, file transfer and e-mails.

4.       Repository – Emphasize on that master data management solution blended with three different database schemas including an authoring area, Metadata area and release area. The Metadata arena stores application related configuration settings and internal Metadata for the user accounts, user interfaces and authorizations including process flow and workflow. Both authoring and releasing arenas comprise an enterprise data and information. Data changes and creation are conducted in the authoring domain and once the workflow approval and final changes are applied, then automatically all the changes get moved to the release arena.           

In all, there are many software companies helping businesses and other firms to advance, modernize and optimize existing MDM pharma technology to attain better results in a lesser period of time. Moving towards the growth and progress of technology, two out of every three world’s small, medium and large-sized corporation is trying to run their business effectively and smoothly by effectual management and synchronization of data.

Social Media Sites and Recommendation Engines

Over the last few months and years with time, social media websites have been increasingly getting popular and have impacted web masters and geeks in different ways. With the development, progress and ever growing success of websites including Twitter, Linkedin, Facebook, Digg and others, most of the developers and professionals have decided to use such websites to introduce and promote their business offerings including products and services. In general, it is simple and easy to develop your own portal, with the use of key coding techniques, libraries and manuals. If thinking and browsing to grab more information about few coding strategies to assist you remain productive and fruitful on social media websites for yourself as well as your customers, you can really find informative content on the same in the post.

Activity Drift

Since Facebook introduced the usage and importance of activity streams that could be customized on every user profile, the idea has gone and spread like anything else. It seems like people are damn interested and fascinated about reading their friends and work colleagues updates in their timelines here and then. Being familiar with each and every activity that friends and loved ones bring so much engagement, keeping up and holding clients to look at a specific web page for longer period of time is quite a benefit.    

Profiles, Authentication and Testimonies

The varied ways through which one can set up a user management system were introduced online and the procedure of picking one solely depends on the customer’s preferences. Would you think to create your own technique or use the existing one? Which language will you prefer to use? What is the degree of security and privacy that your web page will need and demand? There is only little stuff you can handle and adjust according to your preferences on social websites mentioned in below.

1.       Friends – Everyone whether your friend, loved one, special one, work colleague, peer, anyone who joins Facebook or Linkedin including other social media websites has the uttermost desire to become popular and viral instantly. One of the most effective and popular strategies used to become viral on such platforms is add a friend option that goes along with the user’s e-mail address book. On one hand, it helps one to get in touch and connect with your contacts that are already registered on such sites and on the other hand, it helps to entice and persuade other acquaintances to become members of those sites. Having clients promoting your products and services in such social media websites is one of the best forms of marketing and you can cherish and enjoy all such privileges at absolutely free of cost.

2.       Recommendation Engine – Showcasing big data recommendation engine and its related content is the ideal way to boost engagement and action on every web page. Social media sites, e-commerce portals and online sites such as Reddit, Amazon, Netflix, Digg have been great examples of recommendation engines since far and the blogs also provide a sheer idea of how effectively one can use recommendation engines and empower a firm’s overall performance level, recognition and reputation. If you can create an effectual, sound and useful recommendation engine for your web page, you will for sure fetch more number of clicks along with the higher engagement ratio.

3.       Messaging – Instant messaging is another solid and effective feature addressing to the needs and desires of people that must be possessed by all social media sites. By other means through which you can build and enhance communication among your friends, peers and followers, it would be better. Private messaging is a great way to keep your customers updated and keep coming at your web page again and again. One of the best things of messaging functionality is that it is built over different CMS scripts and many of the websites do not even grant the usage of them. Always try to maximize the use of this functionality and keep your clients happy so that they can still talk to each other.

Few companies are already taking advantage of big data recommendation engine technology and if you are not undergoing product recommendations, hopefully you don’t want to become extinct species. Try to move speedily and define your strategy for a new era for better outcomes.

MDM Solutions for Data Synchronization

With the major focus drifted towards assuring consistent regulatory and reporting compliance, promotion of SaaS (Software as a Service), SOA (Service Oriented Architecture) and company growth through mergers and acquisitions, firms and enterprises all over the world have evolved at a consistent pace. A flawless and well defined master data is created, sustained and maintained because of its major and essential role it is playing in business growth as well as success. This need for an authoritative and effective system to look after all the enterprise data aroused the adoption of MDM i.e. Master Data Management framework.

Unlike the consistently moving transactional information, master data is a fixed reference data describing products, customers, vendors, employees, materials and other information properties to be shared, exchanged and distributed all throughout the entire work system process in an organization or a line of business. In fact, it is an integral asset of a business adhered by different departments or units that could support transactional operations and processes. Usually, firms worldwide used to deposit and store data in separate data systems across an enterprise and may not be addressed centrally. With an absence of central reference platform, data moves to systems independently heading to faulty, unsynchronized and inapt information distribution causing an immense problem on business efficiency and performance. In availability of error free and de-synchronized information, master data management solutions serve as the much needed and desired solutions.

Along with acting like a centralized filter used to cleanse and consolidate the data saved, shared, stored and distributed, MDM assures that all the components of your business are presented accurately, consistently and in a well represented manner so that the flaws, errors and discrepancies could be avoided. MDM solutions grants for a smooth and consistent flow of business operations that in return saves huge amount of money including both costs and expenses. MDM is basically a system that assists to synchronize and aggregate all the information from multiple business processes into a central place also known as master life. This well known master file enables all units, employees, suppliers, departments and decisive makers to make a single and cohesive view of all their data, information and assets gathered without forcing every department, division, staff or external companies to use the similar data format and system if not necessary. The system annihilates the requirement for individually managed data archives along with assuring a single copy of the data across the supply chain information management system. Leaving a less room for misinterpretation of data, the feed of the information is consistent and at all time precise and accurate. The use and significance of MDM systems has had an outstanding impact on business productivity, efficiency and performance. In addition, it has improvised the capability to integrate, analyze and distribute information on both national and at a global level resulting to enhanced profit, user satisfaction, regulatory compliance, and improvised market share and inter dependent uniformity. Being an efficient tool for drilling down unstructured data, MDM Pharma solutions for pharmaceutical, life sciences and healthcare domains helps in rendering the following advantages mentioned below.

·         Enhance the accuracy and preciseness of the data

·         Effective consolidated collaboration of the data from varied sources

·         Deliver a generic kind of model for management

·         Follow data centric strategy to management and handling of the data

The aggregated and consolidated information can be further used for SCM, CCRM, HRMS, and ERP modules. The resulting outcomes could be used as source or master for other systems. MDM solutions provides great source of power in the hands of operator to get deep insight about the patterns so that favorable and beneficial information regarding business dynamics could be fetched and utilized in the long run. The information in return can be used to enhance business processes and operations effectuality, making sure businesses is heading towards the right direction with reasonable certainty whether or not a specific move can bring out the desirable changes in the bottom line. 

Pharmaceutical Firms Blend Master Data Management Solutions to Improve Compliance

Deliberation and contemplation continues to arise regarding the federal government following the lead of American states and make new regulations and norms specific to the pharma and healthcare industry on physician marketing spend. Following the footsteps of recent Vioxx drug recall, elderly and senior citizen belonging to American are buying drugs from Canada and other drug manufacturers, heading to hike in prices along with the facet of Fen-Phen anti obesity and AIDS epidemic drug lawsuits. Though the pharmaceutical sector continues to yield favorable and strong gains in spite of the public relation crisis, the sector is chasing off a hail of new regulatory risks and flaws in what is already one of the highly overrated and regulated verticals.

After investing like millions of dollars on Sarbanes Oxley conformity and compliance, pharmaceutical firms worldwide have made essential investments to affirm privacy management, product recall requirements and marketing solutions for both physicians, patients and none but the spend management reporting solutions. The existing and uncertain environment, IT managers and compliance executives strive to battle to find solutions to address existing and new regulatory needs and prerequisites. Outstandingly all such regulatory issues share one thing in common and that is the requirement to control master data and other prime business information related to users, products and locations. Existing and precise knowledge of client demographics for patients and physicians and their marketing priorities and communications with the enterprise are no longer efficient tools for reaping and building new business, but also needs the data required for critical reference for handling regulatory reporting and compliance.

Master Data Management Solution Hubs – Apt Regulatory Framework

Pointing and addressing to the common data integration issues spanning compliance management, firms and organizations are thinking to invest at a technological platform, which is not able to support complex and multiple compliance initiatives but also could address in the long run each and every regulatory mandate with an end to end solution. These days, firms need to consolidate the available data and information and safeguard more authentic and relevant data to adequately fulfill state compliance mandates associated with sales spend. The similar kind of requirements also apply to other strategic actions and punches including marketing opt-in, pedigree compliance and product recall for handling both product master data as well as customers.

The new realm for chasing and fighting the umpteen complex regulations and standards within the pharmaceutical industry is master data management and MDM solutions enable enterprises and companies to effectually manage the entire lifecycle of employees, physicians, patients and product information. Master data management solutions for pharmaceutical and healthcare industry establishes a foundation for legal and trustworthy compliance management that on one hand efficiently lowers the cost of managing new and existing regulations and on the other creates an ongoing and substantial competitive benefits. Creating a foundation for handling regulatory compliance is one of the instances where it needs to have a sensible investment having the potential to garner mandatory and ongoing financial and business advantages in the long run.

Master Data Management Success and Progress – ROI

The increasing demand and overture of next generation MDM Pharma arenas is that they can be randomly deployed with flexible and robust data models could be tightly integrated with emerging and evolving technologies and cost factor can be justified with proven reliable return on investment. One of the very good examples of a MDM success could be of a pharmaceutical firm that determined $12 million annual revenue boost from improvisations in data quality feeding their CRM (Customer Relationship Management) system. Return on investments outcomes grab the attention of senior executives and people and can be utilized to build an aggressive and compelling business case for a master data management platform that can address more than only just compliance, as it is also reckoned and well known for addressing other strategic and well defined business initiatives of an enterprise.

Big Data Solutions: Building a Recommendation Engine using Hadoop, Pig and Mahout

With time and over the years, recommendations have become expected and important as an integral part of the user experience worldwide. From a consumer’s point of view, marketing interactions could be favorable and useful and even time saving, rather than just being generic, annoying and out of the context. If you shop from leading and pioneering vendors and retailers like Amazon or Flipkart or Jabong or Bluefly, you might think that somehow they have gotten inside our mind while presenting and showing recommended items relevant to your search results. It is a consequential improvisation of the conventional psych demographic profiling as well as targeting of the old world. It has always been a point of discussion how Mahout can be used to build a recommendation engine with minimal coding and programming needs. Moreover, it has also been discussed a lot how the machine learning capabilities and open search potential of Mahout and Apache Solr can be blended to empower huge scale data driven applications that efficiently combine real time access with global scale discovery, enhancement and enrichment.

Recommendation engines are everywhere assisting firms and enterprises all across the world to provide a kind of ‘artificial intelligence’ to help put down filters through numerous options to pick handful of selections most apt and authentic for you. For example, when Netflix advises you movies based on the reviews and your preferences, after thoroughly searching history of you and zillions of other subscribers, you are actually benefitting from their recommendation engines. Many other big internet marketing companies makes use recommendation engines to boost their online services powered by big data solutions. In fact, for your record and knowledge, Datameer Company is going to have a seminar on the same specialized in offering data analytics solutions by making use of Hadoop technology.

Business problems being targeted and focused – A new suite of business issues were difficult to think and handle before including customer churn analysis, modeling true risk, recommendation engines, ad targeting, loyalty pricing, threat analysis, search quality fine tuning, trade surveillance etc. To address and solve above mentioned issues, a flexible infrastructure for data warehousing and big data analytics emerged heading to below mentioned endeavors and support public and private cloud deployments.

·         Capability to determine structured, unstructured and transactional data at a single platform

·         Solid state or lower latency in-memory devices for high chunks of web and real time applications

·         Slice out low cost commodity software, workloads and distributed processing

The interesting part is that the fundamental things of big data have been drastically changed from the core endeavors of BI and analytics to the ability that end users perform while analyzing, reporting and handling tasks over consistently growing chunks of structured as well as unstructured information like sensor data, log files, sales transactions, streaming data, emails, research data and images collectively known as ‘big data’.

Big data recommendation engine for e-commerce retailing is helpful in multiple things. Quite favorable and beneficial in raising average order size by recommending complementary items based on the predictive and foreboding analysis for up selling and cross selling of products, the cross channel analytics cover different attributes like average order value, sales attribution, lifetime value and event analytics cover a vast series of steps induced to a desirable outcome including registration and purchase.

Big data Existing and Emerging Firms to Watch –

·         Pioneering Leaders - Few of the leading firms catering big data solutions to medium and large-sized enterprises all over the world include HP Vertica, IBM, Microsoft, Teradata, Oracle, Netezza etc.   

·         Evolving Players – Few well known emerging leaders catering big data recommendation engine solutions include IB Technology, Splunk, Cloudera, Hortonworks, Datameer, DataStax etc.

All the enterprises are following two distinct approaches in general, including serving big data solutions in the public cloud or in the private cloud. It is becoming clear with time that there is going to be a tremendous and growing push towards an adaptive and flexible infrastructure for big data, master data management, data warehousing, and data analytics. All in all, with the increasing and consistent focus on mobility and better decision making, the businesses are going to push faster and quicker than corporate IT can ever react and respond.

Big Data Recommendation Engine for ecommerce

Becoming an integral part of many e-commerce websites and online shopping carts, recommendation engines use complex algorithms to determine big chunks of data and analyze the products that potential users wish to purchase as based on their online shopping preferences, stated choices or the purchases of individuals with similar kind of tastes and demographics. In addition, recommendation technology must be capable enough to reach small, medium and large sized businesses and most important be robust, scalable and cost effective. 

Most of the consumers who have used Amazon, Flipkart, Jabong and other leading e-commerce websites must be familiarized to receive recommendations for apparel, gadgets, books or any other thing in which they might be interested in purchasing in the long run. Priory, recommendation technology was somewhat filthy and savage. It used to recommend those items only which the purchaser has bought. Though, the technology has become more sophisticated and advanced and is now a vital part of many online merchandisers and vendors economic models. The strategy focuses more on determining big volumes of data and analyzes what products that targeted customers might want to purchase based on their online shopping choices, preferences, people purchases depending on their tastes and demographics. It is into building new revenue opportunities as well as booming both, the number of prospects who become purchasers and the prospect retention. Few of the top notch recommendation technology merchandisers are Google, Mavice, ChoiceStream, AgentArts and ExpertMaker. In addition, the big branded users are Apple Computer, Amazon.com, Netflix DVD rental website etc.            

The CEO of ChoiceStream Michael Strickman stated, the technology is assisting to drive and generate online sales, especially in the music industry. With such a large client centric and favorable market, it is easy to see that customizing and personalizing stuff makes sense. The customers used to download almost four times songs and music than before implementation. With the improvement in recommendation technology with time, the more concerns will be there. For example – Analytical and integrated approaches entrust on big chunks of data that are not available to other smaller merchandisers and vendors. The technology’s use of information from online trade activity and the constitution of consumer profiles have ignited privacy related enquiries and concerns.

In the early 1990s, recommendation technology began to shape up and one of the earliest pioneers was the University of Minnesota’s GroupLens Research Project. Amazon.com and Net Perceptions were amongst the first recommendation users well known as vendors for website personalization and customization software. The key forces for enterprises and organizations to drive and integrate big data recommendation engine into their e-Commerce websites is the wish not only to get the users purchase more products as well as return on their websites in the long run. They will return because these engines will make the process faster and quicker to find items they want and provide personalization that could yield favorable and useful suggestions, as said by Mavice’s chief founder and technology officer. Most of the vendors want to showcase and recommend customers with items in which they could be interested in but didn’t purchase. 

The business models usually employed by the recommendation technology vendors includes either hosting services for a company or licensing their engines basically for e-commerce businesses to run themselves. In general, there are four types of licensing engines including the ones mentioned in below.

·         Implicit Engines – These engines provide recommendations based on the activities of multiple customers while browsing a company’s website. They tell users information about the customers who have purchased ‘A’ have subsequently purchased ‘B’ too.

·         Explicit Engines – They make recommendations based on the users entering phrases or words to connote the type of products they are looking for.

·         Content based systems – They used to recommend those items to customers based on their preferences in the past. Such systems collect information about customer’s preferences through questionnaires or the past history stored in their databases.

·         Collaborative based engines – These engines provide recommendations based on the buying customers preferences with similar kind of interests, demographics, questionnaire responses or the profiles picked from customers online actions.      

In shell, as estimated and said by analyst Patti Freeman Evans with JupiterResearch, a market based research firm, about 25% of online clientele make unplanned and random purchases, a smaller percentage than users at traditional stores. Adding more, online third parties also make use of recommendation tools as a medium to optimize and promote their sites along with to generate revenue from less demanded and older stuff, for example CDs and DVDs. Businesses and enterprises use the big data recommendation engine technology to grab the information related to e-Commerce websites, along with the customer data to make better sales and marketing along with the available stock and promotional products.