Marketing is heading towards Big Data and analytics. Marketers are experiencing increased complexity in both their departments as well as daily pursuits. Data complexity is increased with campaigns on social media combined with other data sources from sales and finance. So companies are now resorting to marketing analytics for data aggregation, analysis and making data-driven decisions. Before going ahead with marketing analytics, companies should identify their key objectives based on a definite analytics platform which is divided into milestones in sync with the existing framework of the company. Once this is done, one can start the analytics journey and hold key stakeholders responsible for results all of which are based on the company’s goals and vision. Data techniques to be used should be decided solely on the basis of the company’s objective. It is important to focus on the correct segment of data within the entire data set. This way one can increase the chance of gaining actionable insights on data and thus pushing the company towards growth. Read more at:https://channels.theinnovationenterprise.com/articles/marketing-analytics-do-you-have-a-framework

The value of data is no longer unknown to the business world.  Both structured and unstructured data are important because they are unprocessed raw materials which go into analysis. Many business leaders and IT professionals prefer structured data, while it is currently being observed that unstructured data also has a lot to offer. Unstructured data coming from various social network sites help business leaders to gauge customer sentiments and grievances. It also helps to reduce costs and adapt to a changing market situations. Sometimes there can be challenges handling unstructured data such as collection, organization, integration and analysis. text analytics, auto taxonomy generation, auto tagging and other techniques are vital when it comes extracting value from unstructured data. To know more read: http://www.cio.com/article/2941015/big-data/solving-the-unstructured-data-challenge.html

Over a decade ago, High frequency trading (HFT) used to be part of very few financial firms, but now it is an integral part of every major financial firm and is key to drive the success of these firms. Many industry experts in the field are of an opinion that big data has started entering into the financial sector at a minute level for now and it will follow the same trend like the HFT in expanding into ever major company. It will be the major decisive factor in taking many calls in near future. On the technical level, many experts feel Machine Learning (ML) will take a dominating role in areas where Statistical techniques are now used for finance and risk management. ML with its ever increasing algorithms/techniques is an ideal replacement to humans in trading scenario, though it has its own caveats. It is seen that ML and Big data is going to lead a new revolution into the field of Finance. To read more: 

http://www.automatedtrader.net/headlines/153852/gdtrm15-machine-learning-is-the-new-c%2B%2B

Today data and analytics wave has replaced the HR and people management. Human resources are one of the most data rich functions in most business. Earlier the data were mostly ignored or just kept in tables and charts but now data analysis has used such data and helped predicting when will employees leave, from where they can grab most suitable candidates and how to identify and retain most talented employees. HR departments are now crunching their data and easily taking decisions for recruitment. Earlier it was not so easy for HR's to look around the globe for hiring but now they can do just by sitting in a room, analyzing data without looking at the globe. Read more at:http://www.smartdatacollective.com/bernardmarr/334941/how-hr-can-use-big-data-smart-way-hint-most-are-not

In the gambling industry, the house with most information wins the game. And it’s no wonder that Big Data is changing the world of gambling. Betting firms are now making extensive use of Big Data and analytics to manage their business and stay at the apex of the game. Big Data services empower gamblers by giving them more information and helping them to plan strategies more effectively. Big Data is also transforming sports gambling. With lots of data to collect and analyze, sports organizations are using it to study players and their tactics. Gamblers are now using Big Data as a way to get the odds in their favor and this has led to the growing popularity of fantasy sports betting. Gamblers are not only using Big Data for their games but also to improve their marketing efforts for example the casinos are analyzing customer data to develop personalized marketing campaign. Read more at:https://channels.theinnovationenterprise.com/articles/how-big-data-is-changing-the-gambling-world

Data quality has not been so good so far. Huge amount of data can be found that is incorrect or duplicated. It is very difficult to setup an improvement program. One should know what kind of errors are there to allocate the correct resource. To solve data quality problems complex and strategic approach is required which is not same for all problems. Most of the company's decisions are taken after analyzing the data of the company, but if data quality is poor then the result of the decisions can led the company to suffer huge losses. Detecting the data quality is very important because it is positively correlated with business efficiency. Read more at:http://www.smartdatacollective.com/martindoyle/335289/defining-your-data-quality-problems

CMO, the Chief Marketing Officer, has played an important role in the company's boardrooms. They always provided the right direction and techniques to sell products and services. In past 10 years, there has been renovation of techniques. TV and print advertising pales has been replaced by digital advertising and social media. Today use of technology and data by marketers is not limited as previously it was. The increased dependence on technology and data has led to foundation of Chief Marketing Technologist (CMT). They are the strategist, creative director and technology evangelist. Some economists believe that due to data centralization replacing CMO with CMT is not feasible. Both the CMO and CMT have played their role which is important part for business. So neither can replace the other.  Read more at:https://channels.theinnovationenterprise.com/articles/will-the-cmt-replace-the-cmo

A recent survey on customer retention revealed that insures lose as much as 20% of their customers each year. For the insurance agency this corresponds to approximately £500,000 to £1 million in lost premiums for every 1,000 policies. Customer retention is thus an important issue facing the industry. Three key points can help insurance companies to eliminate customer churn.
• Firstly, they need to properly understand their customer, which means determining overall market segmentation to understand cross sell and up sell opportunities at individual policyholder level.
• Secondly, insurance companies must be able to provide tailor made policies for each individual and also have a unique selling point in comparison to their rivals.
• Lastly, companies need to realize that they have to listen to their customer’s preferences rather than the customer having to choose from the list of policies the insurer has to offer.
Should the above be kept in mind in a company’s planning and operations customer churn can certainly be minimized if not completely eliminated.

For more information visit:
http://blogs.sap.com/banking/2015/06/19/preventing-customer-churn-with-better-data-analytics/

In order to increase standards of the capital markets post-crash and other unprecedented events, few regulations enforced by the market regulators are forcing the market participants to have a more thorough background research. Thus demand for data in capital markets is increasing as never before. The analysis reports are now being made at real time to increase the efficiency, and quality. But the problem at hand is how well firms can handle this data. Can they afford the data within their profits? Can data can be made available to all in proper usable format to all the participants at minimum price. How can the data made available be kept clean and free from corrupting? To read through some of these issues go through-http://www.automatedtrader.net/articles/feature/153959/the-data-deluge

The term Internet of Things was coined in 1999 by Kevin Ashton, cofounder of the Auto-ID Center, an RFID research project at the Massachusetts Institute of Technology. IoT is about intelligent devices talking to enterprise apps. Its important to focus on the basic building blocks as the success of an IOT initiative depends on them. However, you must overcome  challenges before the IoT can be fully realized. A lack of unified standards , Complexity are some of the challenges. If you're starting from scratch, it's important to ask, "What's the No. 1 business problem you want to solve?" Once that's answered, it's time to look at the systems you need to connect and the capabilities you must put in place. The most important point is to bring your structures and unstructured data together in a meaningful way. 

Read more here- http://www.cmswire.com/internet-of-things/get-ready-for-the-iot/

During the past decades, it has been seen that farming industries have made use of big data in a very efficient way. Many attempts were made to improve the crop yields of arable farming using big data. One of the evidence is that diary industry in Netherlands made huge margins after adopting big data. The data from farmers, soil type, soil analysis, and also analyzing weather can be used to predict the degree of accuracy in time for applying seeds and, fertilizers and chemicals. Read more at:https://channels.theinnovationenterprise.com/articles/how-can-big-data-positively-affect-farming

Big data will play a major role in shaping the future of businesses. The two major areas that are going to undergo significant changes are as follows:

(i) Previously, once data had been used, it used to be deleted. But now, as predictive analytics makes use of historical data, so data needs to be stored for an indeterminate period of time. This is against the current laws and thus requires amendment. In future, well-defined laws about collection, distribution and storage of data are going to be the key to success for businesses using Big Data.

(ii) With technological advancement, data is being collected at a faster rate than before. Also new and improved technology is being used to sort it. In future, frameworks will need to be robust in nature and analytical processes should be improved in terms of speed and accuracy. Read more at: https://channels.theinnovationenterprise.com/articles/big-data-and-the-future-of-business

Data mining has taken a new form termed as a Dark Web. Law enforcements have made use of dark web to analyze data on websites and concluded that in 2010 over 100,000 sites contained extremist and terrorists' content. Today, dark web has become popular in finding and shutting down websites that can be used to facilitate criminal activity. Big data is also making use of such technique to collect data to better understand criminal behavior. Dark web has made it easier to keep an eye on illegal activities. Making use of dark web can stop these criminal activities which would ultimately results in favor of people. Read more at:https://channels.theinnovationenterprise.com/articles/mining-in-the-dark

Does your analysis show more revenue than what is actually being generated? Or you are getting results that are too skewed to make actual sense? Make sure you follow these 3 ways to avoid making data driven mistakes

1) Get right data: make use of latest data and look for duplicate, missing values and outliers. It is also imperative that you have enough data while drawing an analysis.

2) Ask questions: Sometimes analysts apply techniques they haven’t really examined. Asking questions makes you better understand the problem at hand

3) Make productive use of spread sheets: if while looking at data, you are looking at spreadsheet, remember to keep data and formulae separated.

Read more at: http://www.forbes.com/sites/teradata/2015/07/08/how-to-make-a-bad-data-driven-decision-in-three-easy-steps/

With the analytics boom, businesses are treading the data-driven path. But organizations should realize that not every data is useful- one needs to sift through a huge amount of data to find out the useful ones. Often having a large database doesn't indicate a successful data program, instead it implies a program requiring considerable amount of work. The problem with a large database is that the data does not possess a reasonable degree of accuracy hence making it difficult to make accurate business analysis. Knowing more about the data- what it is and what it represents- will enable companies to filter, categorize and analyze data. Cleaning up data isn't something that can be easily achieved through technology. Data gathering system should be developed keeping in mind what data is being collected and why it is being collected. Read more at: https://channels.theinnovationenterprise.com/articles/your-data-is-garbage

Most of the big companies are now giving more weight to big data expecting that they could generate large revenues. Studies have shown that with the help of big data, companies have improved customer experience and attracted new customers. But lack of trained professionals and the need for better infrastructure has created some barriers for companies. Also, resources required to hire new professionals raise the cost of a firm. But this problem has been tackled by Data-as-a-Service (DaaS). This is a tool that mines the big data ecosystems and delivers the right data to your marketing channels and automation programs in real times. DaaS can be implemented across a number of industries, for both B2B and B2C.  Read more at: http://www.smartdatacollective.com/lbedgood/332896/how-generate-big-data-revenue-without-big-investment-team-data-scientists

Big data is playing an important role in football during these days. Football is continuously making use of performance analytics and making use of available information more efficiently to achieve competitive advantage. Most of the top level European football clubs adopted structured analysis process and dedicated performance analysts. Now clubs are able to analyze their data at just one click of mouse. Today players are wearing GPS trackers during training, acceleration sensors and heart rate monitors which records their data of performance and after analyzing those data optimal steps can be taken to improve performance. Also analyzing the opponent's data can give the team a lead ahead to win the game. Today performance can be judge easily on and off the field. Selection of players can be done by analysis of performance. Finally we all are seeing a completely different game during these years. Read more at: http://www.smartdatacollective.com/bernardmarr/332906/how-big-data-and-analytics-are-changing-football

The idea of using information and data for various purposes is not new and has been there since the first web connection was developed. Computer scientist John McCarthy had developed the idea of cloud computing. The internet has grown at a very small pace since the 70's but got revived in the early 90's when first move towards cloud computing was taken. Heavy investment and development in cloud computing shows it is predicted that cloud computing is soon to go mainstream with 34.1 percent of IT sector already running applications in the cloud and 66.4 percent are looking into the cloud, planning to implement installations. Server performance tuning has become easier and finding query performance is now rapid through database analyser, all due to the cloud where our future lies. Cloud computing has become a global phenomenon and people are choosing it for solution in a hybrid manner. Soon companies have to fully turn to cloud computing as it becomes mainstream and lots of other companies shall create their own unique framework including internal cloud adoption. Read more at: http://www.smartdatacollective.com/rick-delgado/324781/foreseeable-future-cloud-computing-explained

Financial managers find it very difficult to reconcile goods received against invoices not received. This issues called 'GR-NI' is time consuming to manage. The problem hence gets reduced to lowest of the priorities and leads to increase of financial liabilities and risk for the business. Without proper automation to monitor the discrepancies, paperwork gets piled up which leads to supplier connections suffering and credits being misused. However, with software based supplier reconciliation process in place, the organization can get rid of this huge task and also improve its audit processes solving several risk issues as they arise. A simple overview of the new 'GR-NI' process says that creation of a database with financial and orders data is to be integrated followed by different other steps. The key elements of this solution are methods to get statements directly into the system regardless of format and source and many others. To maximize the value, operational areas should be looked into that can benefit from better processes. Read more at: http://www.smartdatacollective.com/keith-peterson/329860/using-procurement-analytics-simplify-your-supplier-reconciliation

Today marketers know how to enhance customer experience and implement new strategies to attract new customers. Big data, social media marketing and customer relations have influenced the marketing behavior. There will be no insights without data. But by managing data and analyzing it will lead to achieving marketing goals. Analysis of data should be such that it provides helpful conclusion to businesses to make strategies and reach goals. Documentation of business objectives helps business to know what type of data is required and how to manage data. Data should be integrated and structured in such a way that it is reliable to customers. Data validation is required to check the validity of data.   Read more at: http://www.smartdatacollective.com/lbedgood/332008/ultimate-beginner-s-guide-data-quality-and-business-intelligence