Learning and Development experts believe that the process of learning and remembering can be broken down into four basic stages:
§ Sensory Memory
§ Working Memory
§ Long-term Memory
§ Transfer to Job Memory
Lean Principles are applied to these stages of learning for a more effective training in the following ways:
• Lean Training Techniques for Sensory Memory – The basic motive of the trainer in this stage is to remove any distractions from the room and add things that draw the trainee’s pre-conscious attention to the training.
• Lean Training Techniques for Working Memory – Better training materials with working memory on mind can be done in the following ways:
1.)  Creating a set of “Learning Objectives” and including only training materials that support those objectives.
2.) Breaking down the training into small “chunks” and organizing those in such a way that it helps in learning..
3.) Keeping it Simple and Short
4.) Keeping the training active
5.) Providing job aids when it is not necessary to memorize.
• Lean Training Techniques for Long-term Memory – When information is moved from the working memory to the long term memory, it is stored in little “packets” of related information called “schemas”. Hence it would be helpful if new information is introduced relating it to the information already known by the trainees.
• Lean Training Technique for Transfer to the Job – The final step of any training is transferring information from training and applying it on the job. This can be done by:
1.) Explaining workers how it will benefit them.
2.) Providing sufficient hands-on exercises.
3.) Providing work in real work environment.
4.) Providing realistic simulations in training when possible.
5.) Testing workers at the end of the training to see if they can satisfy learning objectives.
Read more at: http://www.industryweek.com/lean-six-sigma/applying-lean-manufacturing-principles-job-training

As the typical scenario in any data analysis includes more than one type of data source, working with large datasets, messy and unorganized data, there is a huge need of data prep required. Most data sets are relatively dirty and need to be thoroughly cleaned for the analytic result to be usable. The need to have some structure for reporting and analytical tools to grab onto resulted in a boom of data prep.

It is very imp to have the data validated in the initial stage, because if that goes wrong, then everything downstream of that becomes very problematic. Thus we need to have the data ready for analysis and to avoid any non-value add, which is achievable by big data prep.

Big data prep uses a combination of machine learning algorithm to automate most of the work that goes in sanitizing data. Read more here- http://www.datanami.com/2015/06/22/why-big-data-prep-is-booming/

Nowadays, business challenges across industries are same due to increasing commonality. Some of these challenges are customer satisfaction, market insight, cost reduction and supply chain efficiency. A solution to one of these problems in an industry can be applied to clients facing similar issue in another industry. This cross pollination can happen internally also between teams and departments. For prediction of the customer behavior we use analytical models by which we can give out the right message at the right time. These models can be used across industries to solve analytical problems because customers are often similar only and face same challenges. Read more here: http://www.callcentertimes.com/Articles/tabid/59/ctl/NewsArticle/mid/407/CategoryID/1/NewsID/1000/Default.aspx

External factors have a great influence on businesses thus understanding these factors is very crucial when building a statistical forecast. To know whether an external factor has influence on our analysis or not we should consider the following aspects-

1.Consistency- This means how volatile is the data.

2.Accessibility-This is the ease with which we get data.

3.Frequency of getting data- For yearly decision making a quarterly data would do good but for frequent decision making daily data fits best.

4.Data Granularity-The granularity of data refers to the size in which data fields are sub- divided.

To read more- http://revenueanalytics.com/blog/uncovering-external-influences-in-your-analytics/

The newest development in the technical world today is smart buildings, buildings. This development has been riding the wave of IOT. It has been expected that demand for such buildings will increase to 70% by 2050. Smart cities are also a possibility, using linking devices to communicate with each other thus making it possible to analyze huge amounts of data in real time. Efficient infrastructure and storage for the information is a necessity. This set of IOT solutions has huge potential which seek to improve quality of life and well-being clubbed with increased efficiency, productivity and economic growth. Similarly, the transport sector can also be upgraded by IOT. Smart transport for these smart cities can soon be a reality. To know more, please follow: http://www.dataversity.net/how-cities-will-benefit-from-the-smart-buildings/

Shadow BI is nothing but BI applications developed by non BI professionals. It usually comes into practice when a business user loses patience in an incumbent software solution and comes up with a solution of his/her own. Shadow BI applications might appear as user friendly and appealing but most likely it might do some damage to the organization in the long run. There has been a debate as to whether shadow BI must be removed or not. Some experts believe when shadow BI becomes successful it will contribute to the BI ecosphere. Constructive professional guidance will be required to make the solution sustainable. Sometimes, it can even lead to better alternative solutions. However, there are some risks associated. The organization might be exposed to risks if the software has poor functionality, data integrity and might run into legal issues if it violates the software license agreement. Read more at: http://www.dataversity.net/how-to-overcome-the-big-data-hoarding-monster/

Companies are constantly generating large amounts of data both from internal and external sources. They are even storing these data. But some of it are useful for future projects while others are never used again. In today’s world of big data, storing data does not create a challenge as does managing and utilizing information. Storage costs are beginning to hurt companies but connectivity allow for some relief. Timing is crucial as presenting data at the right time matters a lot. Retrieval of the data with relevancy, accuracy and integrity can sometimes pose a challenge. With storage and backup infrastructure in place, storing data becomes relatively cheap. Read more at: http://www.dataversity.net/how-to-overcome-the-big-data-hoarding-monster/

Medium and large enterprises are increasing their investment in data center facility particularly in health and financial sectors. There has been increased investment in cloud and colocation providers and more investment is expected. Most of this investment is targeted at rack and cabling, power equipment and data center infrastructure management software. Investing in data centers have become an important part of supporting growing business demands.  Enterprises are allocating more budgets in coming up with modern datacenter facilities. This increase in spending will let equipment vendors with large premium sites of their clientele gain the most. To know more, please follow: http://www.dataversity.net/enterprises-increasing-investment-in-datacenters-focus-on-upgrades-and-retrofits/

Machine learning has showed tremendous potential to transform companies from inside out. Everyday new algorithms are coming up that are being used to encounter data and tackle new problems. On the other hand, a closer look at machine learning reveals it to be nothing more than a branch of statistics for a world of big data. Business executives with a thorough understanding of machine learning have the ability to reach efficient business outcomes. In this age of data, firms have to work with large scale data. Both advanced software and hardware is needed to manage, analyze and store it. Herein lies the applicability of machine learning. To know more, please follow: http://www.dataversity.net/what-business-execs-need-to-know-about-machine-learning/

It is an interesting conjecture that data can be thought to have a life cycle. This life cycle can be further broken down into phases. The first phase being data capture, creating data values for an enterprise. There are several ways for data capture. These are namely Data acquisition, data entry and signal reception. Data acquisition is the induction of existing data of an organization, data entry is the process of creating new data values for the enterprise and signal reception is the data generation process by devices working in sync with IOT. Data maintenance is the next phase of data life cycle. It necessarily deals with data synthesis and data usage. It includes processes such as movement, integration, data cleansing, extraction, etc. Read more at: http://www.dataversity.net/the-data-life-cycle-in-7-phases/

Firms looking for potential customers should proceed with caution while deciding on the marketing strategies. The marketing strategies should be not such that annoy customers. Here are some general pointers:
• It is best to come clean about the product and its limitations. There is no sense in hiding weakness initially which might make the customer deflect later due to inefficiency.
• Promotion of the product should be done either through email or phone, not both. Some decorum should be maintained while having a conversation over phone or emailing a customer.
• Demonstrations made to the customers should be engaging and interactive.
• The discussion sphere should not be kept confined to the product only, it pays to enquire about the customer. Even if opinions differ or disinterest is shown by the customer, it helps to give value to the reasons the company got rejected.
Read more at:  http://www.business2community.com/customer-experience/5-sales-marketing-tactics-guaranteed-to-annoy-customers-01274974

Companies are always trying out ways and means to keep their employees satisfied, after all they are the work force that keeps that wheels running. Companies are often coming up with new and innovative ideas to motivate their employees keeping an eye on their budget constraints. Here is one innovative idea; a luggage party. All one needs to do is to select random employees and send them on an all paid trip for the weekend. The prospect of a trip, a chance to get away from the work pressures, that too on the house excites most.  Another idea could be to give “play time”, a chance to relax with activities like table tennis, indoor basketball. Its a sure shot way to reduce stress levels and keep employees happy. Introducing a snack bar might also help the cause. To know more, please follow:
http://www.business2community.com/human-resources/5-fun-employee-benefits-big-companies-offer-and-you-can-too-01268109

Understanding and collecting data is an important part of viable businesses nowadays. Big data helps us in that with its many applications in various spheres. Big data is not only limited to marketing applications, it can analyze structured and unstructured data searching for purchase patterns, build logs and store day to day information. Big data has helped optimize business performances, has led to an increase in productivity and thus it has made its impact felt on the profit margins. Big data empowers organizations with knowledge of their employees thus enabling interactions on an individual level. This is bound to make an impact on employee productivity eventually leading to growth of the organization in the long run. Read more at: http://www.business2community.com/big-data/big-data-a-big-impact-on-productivity-01274278

While running a business, one should consider not only internal data but external ones as well. Due to the inability to access information and integrate it, businesses are lagging behind. Though a huge amount of external data is available, it’s not always easy to find the desired information. Certain software aid the process of finding external data. Bringing both external and internal data together provides a unified view as well as helps in the process of decision-making and discovering insights. Data from all sources should be brought together and technologies that are able to perform this task and are able to put equal importance to all sources of data should only be accepted. Read more at:https://channels.theinnovationenterprise.com/articles/does-your-car-have-more-awareness-than-your-business

The present events ongoing in the world economy give us the impression that we are amidst turbulence and chaos. To become more active and adjust to rapidly changing events, Finance Planning and Analysis (FP&A) departments should consider a vast amount more of data for forecasting. A lot of this can be achieved by making use of advanced technologies like Cloud, Big data and analytics. With Cloud, necessary information can be disseminated to required destinations. FP&A organizations are now moving from spreadsheets to enabling technologies and integrated platforms thus ensuring transparency of assumptions and drivers. Big data and analytics enables faster than ever processing of data. Using software, sifting through a chunk of unstructured data and identifying patterns has become easier. Read more at: https://channels.theinnovationenterprise.com/articles/the-rise-of-the-rolling-forecast-for-fp-a

Delivering added business value to existing product and services can be done through the Internet of Things (IoT). IoT can help organizations improve relation with customers without much physical interaction. Using IoT, personalized services can be delivered in a less personal way. The key to launching IoT is to think big. Organizations should be able to identify the limitations yet be able to offer integrated services including IoT and other services and applications. After creating an understanding of IoT, companies should communicate its values, potential, risks and ecosystem roadmap implications. An example of how IoT can be used to deliver personalized services can of the car insurer who, in the event of an accident of his customer, is informed immediately through the car's on-board systems. Read more at:https://channels.theinnovationenterprise.com/articles/7726-why-a-customer-is-never-a-thing

The finance function has undergone a metamorphosis in recent times owing to financial crisis and several other events. The treasurer is now being relied upon to cut costs and add value to organizations. Treasurers, today, face the challenge of doing more with less and this is being facilitated by cloud. Cloud facilitates movement of money and information between countries. Having a centralized treasury team can be done away with as using cloud platform facilitates management of operations from every corner of the world. Communicating with partners outside the organization is also made easier with a cloud-based treasury management system. Treasurers are being prevented from using Cloud due to safety and security of data issues which can be overcome with cloud providers providing better security than in-house systems. Read more at: https://channels.theinnovationenterprise.com/articles/the-treasurer-and-the-cloud

Hadoop has grown to be one of the best large scale and batch oriented analytics tool, used by webscalers as well as enterprises. Hadoop was designed to integrate with and complement the existing business intelligence of any corporation. But, the issue with Hadoop is that the adoption rate is very slow with the data center administrators. So, most developers have been looking at possible alternatives. Today we will name a few worthy alternatives that you can look at which have a potential of replacing Hadoop in the years to come. They are:
• Disco
• Misco
• Cloud MapReduce
• Bashreduce
• Qizmt
• HTTPMR
• Skynet
• Sphere
• Riak
• Octopy
• MapReduce
• Filemap
• Plasma MapReduce
• Mapredus
• Mincemeat
• GPMR
• Elastic Phoenix
• Preregrine
• R3
• Ceph
• QFS
• Cloud-Crowd
• HPCC
• Condor
• Storm
• HaLoop
• MapRejuice
• GoCircuit
• Spark
• Stratosphere
• Gridgain
• MongoDB
• Mars
• Minceat
• Dato Core
• HPCC
• MapReduce Lite
• Gearman

For more information visit:
http://www.fromdev.com/2015/03/hadoop-alternatives.html?m=0

As people are the most important asset for any organization, hence talent management is a crucial part for a successful organization. It is critical for an organization to control its people by talent acquisition, employee engagement, performance management and leadership and development for better performance.
The 5 Step paths to People Analytics:
v Identify the Stakeholders – Whichever be the area that might have been chosen, it is essential to identify everyone involved in that function.
v Establish Goals and Objectives – Quantifiable targets should be determined so that it can be assessed whether the target is achieved or not or for tracking the progress.
v Do a “reality check” – A realistic goal for improvement is to be determined.
v Bridge the gap – This step makes sure that everything that is needed for the process is present like people, processes and technology.
v Prove success with Data – Analytics in this step highlights the successes or the areas of improvement in the function.
Read more at: https://icrunchdatanews.com/5-step-path-people-analytics/

If you keep an account of your store’s electricity consumption, you would realize that almost half of it goes to waste. Energy costs are sky high apart from the fact that more consumption leads to increased carbon foot prints. Energy use can be minimized by eliminating energy wastage. FirstFuel, a software start up monitors energy wastage using bi data. It uses the electric meter data, weather data GIS (geographic information system) mapping to create the required database. This database helps analyze energy performance without physically stepping into the building, thereby saving the time effort and cost. Read more at:

http://www.businessinsider.in/How-revolutionary-technology-is-tackling-the-200B-problem-of-wasted-energy-in-buildings/articleshow/47460842.cms#ixzz3f7Nme5ct