Big Data is perceived to be a high tech thing that allows us to gain insights and solve problems like never before. Big Data is processed using brand new computers possessing huge processing power. To utilize data to its full potential, constantly updating systems is necessary. Contrary to our belief that data is a new age concept, data has been in use since a long time as is evident from Willard Brinton’s book Graphic Methods for Presenting Facts published in 1914. Most of the techniques discussed in the book are relevant even today. The only difference between then and now is that the size and availability of data has increased manifold thanks to our growing digital footprint. Big Data is becoming bigger with time but the relevance and use of data remains unchanged. Read more at:https://channels.theinnovationenterprise.com/articles/big-data-its-not-new

The key to achieving better efficiency in CRM is through analytics. CRM will help sales teams to identify leads and retain customers. Marketing teams can use CRM to plan strategies for future. One should keep the following things in mind to successfully leverage CRM data:

(i) Data should be clean and devoid of bad data. Clean, filtered and structured data is essential for gaining meaningful insights.

(ii) Fixing systematic failures and finding ways to improve processes is important. Methods to capture data should not be broken. (iii) Adopting use of data quality tools so that one can use self-service tools and prepare data thus leading to improved productivity with progress of implementation.  

Read more at:http://www.business2community.com/big-data/taking-control-crm-data-01253472

In our daily life we came across new technologies and making our life simpler. Today people are enjoying benefits of internet in form of online shopping, easily sending emails and transactions with friends online. Data is collected on the bases of these activities and improvements are made according to people’s preferences and tastes. Data mining is the key to collect big data and most of the businesses are dealing with these data to provide good services to consumers. Both of the large and small organizations are using big data. Earlier big data was providing benefits to retail and sales industry but now it also giving advantages to learning and development industries. Big data is also beneficial for employee training and can improve performance of current employees. Transformation of training process from traditional to modern techniques there has been a significant improvement in employees’ performance. Employers can easily motivate and inspire their employees. After getting good output from employees, employer gives more importance and satisfaction to employees. So, big data help employers to better understand their employees. Big data help companies to understand people’s requirements and providing facilities according to their preference. It also came in a form of learning and development tool for businesses to improve the performance of employees. Read more at: http://www.smartdatacollective.com/briggpatten/331869/how-big-data-shaping-future-learning-and-development-industry

Data provides valuable information to a firm to optimize its performance. Decision makers and strategists analyze data and take optimal decisions. Data analysis shows the firm its ongoing productivity and making predictions will lead to future growth. Employees also get benefit in terms of more productivity when they use data-driven tools providing more enhanced methods which they can use. Customer relations have been improved, as employees are more productive and can give more enhanced solutions to their customers. Analyzing the data collected from social media can determine how successful the conversations have been. This is possible just because of changing consumer behavior and innovations that lead to such change. Ideal business is one that reacts to social change. Analytical tools enhances culture among employees. More data is needed to improve customer service than before. Today there is a lot of pressure on employees as work is increasing with large data size. Here the data-driven technique  plays its role helping them to handle such pressure leading them to be more interactive and informative. Improvement in employee's performance will result in enhancement of sales process, training and innovation. This change will bring some excitement for employees, working with modern tools far better than those boring traditional tools. Building up the transparent system will bring good feedback from upper management for employees leading them to provide more optimal output. Read more at: http://www.smartdatacollective.com/daanpepijn/329438/data-changing-way-enterprises-optimize-employee-productivity

Data provides valuable information to a firm to optimize its performance. Decision makers and strategists analyze data and take optimal decisions. Data analysis shows the firm its ongoing productivity and making predictions will lead to future growth. Employees also get benefit in terms of more productivity when they use data-driven tools providing more enhanced methods which they can use. Customer relations have been improved, as employees are more productive and can give more enhanced solutions to their customers. Analyzing the data collected from social media can determine how successful the conversations have been. This is possible just because of changing consumer behavior and innovations that lead to such change. Ideal business is one that reacts to social change. Analytical tools enhances culture among employees. More data is needed to improve customer service than before. Today there is a lot of pressure on employees as work is increasing with large data size. Here the data-driven technique  plays its role helping them to handle such pressure leading them to be more interactive and informative. Improvement in employee's performance will result in enhancement of sales process, training and innovation. This change will bring some excitement for employees, working with modern tools far better than those boring traditional tools. Building up the transparent system will bring good feedback from upper management for employees leading them to provide more optimal output. Read more at:http://www.smartdatacollective.com/daanpepijn/329438/data-changing-way-enterprises-optimize-employee-productivity

Metrics are performance indicators for the markets. The rules in choosing the right metrics are:
1. Easy to use and understand
2. Easily replicated
3. Metrics should provide useful, actionable information that impacts the business.
With the availability of a wide variety of advanced analytics, it is easy to get sidetracked. Pressure to measure to many things makes it difficult o determine where to focus. Background data on customer is a useful metric. Effectiveness of targeting is related to marketers identifying customer personas. The right metrics such as calculating the potential lifetime values of various customers can help differentiate who is most likely to be profitable over the long term.
To know more: https://hbr.org/2015/07/identify-the-marketing-metrics-that-actually-matter

More often than not estimates, analytics, data-driven predictions seem confusing and overwhelming. But now the situation demands that benefits of data interpretation is vital. Statements such as data too difficult to access, understand or use are common and so are ignored while making business decisions. Fear of failure affects productivity and trying out new ideas. To dispel fear of data usage, managers need to promote better work ethic; data interpretation must start at the basic level with simple tools and incorporate the habit. To incorporate the total picture in a business decision, every perspective regarding data must be addressed.
To know more: https://hbr.org/2015/07/dispel-your-teams-fear-of-data

Today not only big companies are enjoying benefit of big data but also the small and medium sized companies are in the line. Many SME’s have become big data users. The size of data has categorized the type of data users. These are high data user, medium data user and small data user. While the high data users are more sophisticated in tools and more proficient in the storage and accessing of data, all of them share the same problem. Earlier SME’s were not able provide information to decision makers in a timely manner and they find difficulty in presenting and sharing information in proper formats. This problem led SME’s to access the big data technology which made it easier for them to present and share the information in easy to understand formats. Big data technology not only applies to private data but can also be applied to combination of private and public data. This is possible only due to the property of big data technology that it can handle both structured and semi structured data. Its design allows it to work on data stored on cloud and to deal with dynamic processing requirement. Big data technology is mature, flexible and affordable. It can alter the way of doing work with data, decision tools are easily accessible and information provided within the documents can have better view. When a company becomes large, its systems will become large, information and data become large leading to difficulties in handling them. Big data technology has provided a road to have more efficient systems and better techniques. Read more at:http://www.smartdatacollective.com/bruce-robbins/331325/how-business-users-will-benefit-joined-data

Like others, retailers also require advanced analytics to compete in the digitalized marketplace. With the expansion of Internet of Things (IoT), the effect of multichannel retailers will increase as they will start using advanced analytics. Advanced analytics, the analysis of data kinds using sophisticated quantitative methods that produce insights unlike the traditional approaches to business intelligence (BI).  These advanced analytics tools put information in the hands of business analysts and business users offering significant potential to create business value and competitive advantage. The need to improve real-time business decision-making will force retailers to acquire self-service and big data discovery capabilities. Read more at: http://www.analytics-magazine.org/special-articles/1352-retailers-need-advanced-analytics-to-compete-in-the-digitalized-marketplace

While you sit reading this article, if you stretch your arms, you would have electronic help all around you. A smartphone which could pay your bills, plan your schedule and tell you it’s time for a meeting or showing you the nearest food joints when you are hungry. These interruptions imply that we are surviving on advanced, analytics driven machine intelligence. All this said, for machine intelligence to be more powerful, we should be ready to accept a higher level of intrusion. For example, a patient detected with a heart disease, his device could suggest him to take a nap or hit the gym, or could wake him up when he is feeling anxious or stressed. Read more at: http://www.forbes.com/sites/teradata/2015/07/16/why-machine-learning-is-the-next-penicillin/

Flash vendors advocate the use of de-duplication and compression technologies combined with smaller space and lower power consumption. This means that when you buy 20 TB of raw flash capacity, the effective capacity is actually greater. But according to Michael Gunton, GM for Data Center Services, flash does not really reduce data footprints. Apparently data Scientists have seen trends of higher density infrastructure from the cloud providers. The benefits of consolidation are not apparent to ordinary business customers so they are not buying much flash to put in data centers. To know more, read: http://www.forbes.com/sites/justinwarren/2015/07/16/flash-not-reducing-data-center-footprints/

 After big data and internet of things, the new buzzword is the Analytics of things. Though on a similar note, we do not have an exact definition, we know what it means for the economy and the world, as a top strategic trend in technology. As better algorithms for IOT digital infrastructure are being built to index our world to every smaller level, connection based analytics can be used to better predict future conditions and prescribing future actions. AOT fuels the process as new devices are created, there is a potential for new analytics further leading to modification.  Read more at: http://www.forbes.com/sites/teradata/2015/07/15/analytics-of-things-what-does-it-mean-and-where-is-it-taking-u

Lack of planning and special interest pressure among politicians resulted in failure of building the necessary infrastructure required for adequate water retention which lead to water crisis in the state of California. Governor Brown is trying to control who can use water and for what it can be used efficiently. Charging people for water won’t be of any help but through the use of technology things like underground leaks, non-revenue water loss can be identified specially by using the ‘Night-time Flow Analysis’ solution given from Esri. This analysis works by using an optimal time to analyse for leaks mostly at night when consumption is low comparatively. It is a configuration of ArcGIS platform which includes sample data. The accuracy of leak detection depends on customer’s pipe network. The solution responds in real time and is a freely available configuration used to observe base flow conditions and control water crisis.

Read more at: http://www.smartdatacollective.com/shawn-gordon/325636/where-did-all-water-go

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

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

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/

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/