SigmaWay Blog

While opening a business, it’s difficult to decide whether the owner should spend money on hiring an accountant to manage the books and keep them balanced or do it on their own which can lead to mistakes causing major problems for the business. Accounting software programs came into existence which helped the owner free such costs and was easy to use. With today’s programs, anyone can be their own business accountant using the right accounting software’s. Accounting software helps the business in the following ways:

• Dealing with multiple datasets: dealing with several datasets together can be very time consuming and expensive for the owner, hence accounting software programs plays the role of an accountant reducing costs involved.

• Accurate numbers: accounting software programs work with precision and accuracy using the data entered and calculates them correctly.

• Access to data: accounting software programs allows the owner to get access to all his data any time he wants as it is stored in the program every time entered.

• Excellent records: using reliable software helps to update accounts on daily basis without paying for it every time.

• Reduced expenses: using such software programs helps the owner to lessen the costs of managing book of accounts and hiring an accountant is also not required anymore.

Accounting software is helpful for a business of any size and it helps the business to grow, cutting down unnecessary expenses required for maintaining records. Using an easy-to-use software has helped the owners save a lot of money and time and provided them with access to all their financial records.

Read more at: http://www.smartdatacollective.com/rehan-sheikh/327759/accounting-software-saves-time-and-money-businesses

A project can fail for several reasons, some of which are under our control and some are not. Every project will have certain factors that are not under our control, but with proper attention we can increase our success. The reasons being:- • No clear goals: projects lack clear objectives and also a clear vision of what is best for the project’s success. We can avoid this failure by setting out distinct goals of the project at the very beginning.

• No deadlines: without any milestones, team members do not know whether they are on the right track or not which can cause things to go out of control. We can avoid this failure by setting major milestones, and also keep a check on the progress overtime.

• Budget problems: sometimes, budget problems are out of control since leaders create unrealistic budgets on paper that can never be met in reality leading to failure of the project, avoid this failure by making sure every aspect of the budget is looked into carefully to meet expectations.

• Lack of resources: mostly project problems fall into this category because gathering necessary resources for the project’s success is not an easy task. Avoid this failure by researching, thinking through and studying the resources needed for the project from the beginning.

• Weak management: a project with a perfect plan but poor management can fail easily. Avoid this failure by understanding our own role as the manager and actively controlling the team and project work.

• Scope creep: project scope mostly gets diverted from the original objective to several other unnecessary goals leading to failure. Avoid this failure by always keeping in mind the original motive and sticking to it.

These are few such problems which can be easily avoided to prevent project failure.

Read more at: http://www.smartdatacollective.com/bernardmarr/329018/why-projects-fail-biggest-pitfalls-you-can-easily-avoid?ref=popular_posts

However developed and industrialized the country has become, still racism prevails in each and every corner of American society. And this is seen maximum in the workplace where someone named like an African American is less likely to get a call back from the company they applied for job. Also, women face a battle to succeed in the workplace which implied the gender pay gap. But HR analytics may help solve this where a data-driven department can better understand the statistics of recruitment decisions, retaining employee by evaluating employee turnover. But only if at first the attitude of individuals are changed towards others, then can analytics help ensuring that the same problem won’t return back. Read more at:  https://channels.theinnovationenterprise.com/articles/how-can-analytics-help-solve-diversity-issues

Sports analytics have become very popular now days. Predicting winners, player performance and team selection has taken a new form with the help of sports analytics. It has now become a new way of making money and building reputation in sports world. Analysts use the previous data to make predictive models and make future prediction using those models. There has been a shift from qualitative data that was traditionally used to quantitative data. Sports analytics have not been so easy in all sports. American Football, which has large number of variables that can change overtime, faces some difficulty seeking advantage of analytics. NFL teams hardly play 16 games a season implying very small sample size; it is very hard to get some pattern of data. Knowledge of the game and watching the games is equally as important as collecting data. In fact it is part of the data. Read more at:https://channels.theinnovationenterprise.com/articles/how-people-are-beating-the-bookmaker-with-sports-analytics

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

Businesses should always keep invoking new technology in their systems to keep themselves updated. A recent study has revealed that innovation occurs when business models can be matched with one or more of the six patterns where technological advances and market needs overlap, thus leading to growth and transformation. These six points of overlap are named as Six Degrees of Innovation which are as follows:

(i) Meeting customers’ individual needs with tailor-made products and services.

(ii) Achieving sustainability by minimizing waste and managing resource cost.

(iii) Possessing jointly owned assets to boost efficiency and minimize cost.

(iv) Saving money whenever possible by paying only for the services that are used.

(v) Monitoring supply chains effectively by using tracking systems.

(vi) Using data and analytics to gauge customer needs and easily adapting it. Read more at:      http://www.businessnewsdaily.com/8199-innovation-technology-adoption.html

Data is the most crucial component of every organization but lately some businesses have started to deal in data. Therefore we need to focus on some basic steps before we begin to trade data and they are as follows:

1. Selling and Accumulating data - Companies that are engaged in selling data have suffered from serious adverse criticisms. If an organization has more data, then it has to bear higher risks and management costs.

2. Making Sense - Big data improves business efficiency and has assisted in the Internet of Things but management costs have to compensate against the money made.

3. Making Money - Organizations search for suitable techniques to create money from data.

4. Risks and Returns - Data helps to improve the quality despite of the risk aspects associated with it.

Read more at: http://www.business2community.com/big-data/can-businesses-profit-data-quality-01275752

FP&A and S&OP are the two most essential processes for every organization. In modern times both have developed successfully and have started to take part in managerial decision making processes. The advantage of FP&A is that it envisages the results of the decisions that the organizations undertake. It achieves this by scrutinizing both the past performances and present activities and hence it makes the data readily accessible wherever required. While S&OP being one of the integral components of supply chain management, reduces the inefficiencies of the supply chain. It helps the firms to alleviate risks. FP&A on the other hand, manages everything from budgets to predictions. Therefore, both are equally important to a business for running efficiently.

Read more at: https://channels.theinnovationenterprise.com/articles/fp-a-or-s-op-which-adds-more-company-value

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/

Traditional advertising is what most people think of when talking about advertising or marketing. This includes the "usual" venues for media placement such as newspaper, radio, broadcast television, cable television or outdoor billboard. Online advertising also plays a fundamental role in achieving various advertising goals. Tayven James in his article in innovation enterprise.com highlighted the lucrative role of traditional advertising and listed several reasons for it which are as follows:

1. Your demographic might not be "up to speed".

2. Traditional advertising adds weight.

3. Offline ads can boost your online marketing efforts.

4. Offline targeting can lead to better online results.

To know more about traditional advertising, please follow the link: https://channels.theinnovationenterprise.com/articles/why-traditional-ads-can-boost-your-digital-presence-in-2015

Natural Language Processing (NLP) is a field of computer science, artificial intelligence and computational linguistics concerned with the interactions between computers and human languages. NLP executes various tasks by understanding the language. It converts it into a machine representable format to tell the computer what needs to be done. An important aspect of NLP is Natural Language Generation (NLG). It takes the input, processes it and thereby helps to generate natural language back to the user. NLP uses machine learning in order to determine language from bunch of data. It is crucial to demonstrate milieu and subject of each word. NLP has its applications in various fields. It is also used to categorize text into different sections and also to translate the language.

For further details on Natural Language Processing, please follow the link: https://channels.theinnovationenterprise.com/articles/a-beginners-guide-to-nlp

Road cycling had been a dominant sports form through publicity of celebrities. But mountain biking has never been so easy because of its complex technology. Effective suspension to absorb huge bumps with powerful pedal strokes are required for mountain biking. World’s top mountain bikers, Tracey Moseley and Jerome fitness, endurance as well as technical skills, all are required to be at the top of the list. Practice makes a person perfect – so better to practice at various terrain in various weather to know how to use skills efficiently.  The new technologies include increasing stiffness of bikes allows for a better power transfer from pedal strokes to the drive train. Thus basically technology is improved based on the performances. According to both, the skills learnt in mountain biking provides a broad base which can be transferred. Read more at: https://channels.theinnovationenterprise.com/articles/talking-mountain-bikes-with-two-world-champions

Mobile Apps have been at the center of customer based marketing. With the sea of options to choose from, retention of customer is not only about the attractive feature but also about the user experience it creates. Bryn Adler, content marketing manager at Localytics, has a piece of advice, which will help grow your app experience and reduce churn. Here are 7 points to keep in mind:

• No misleading Ads

• Lead with value

• User experience is paramount 

• Use data to track usage, retention and engagement

• Targeted marketing is smarter marketing 

• Feedback is the holy text

• Predicting churn 

For more on this click the link http://www.business2community.com/mobile-apps/making-customers-happy-in-the-age-of-options-reduce-churn-retain-app-users-01271973

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/