SigmaWay Blog

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 TV and print advertising pales has been replaced by digital advertising and social media.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

Data blending means combining data from multiple database sources into a single worksheet or table of a given solution in a relatively quick and straightforward way for discovering pattern between data. Big Data blending is gaining importance in businesses as data blending allows uncommon types of data to coexist in a model. New and better strategic decisions can be made by bringing together unusual feeds into one repository. Data blending and data joining are not similar and have significant differences. Joining involves combining data from the same source whereas blending involves combining data from different sources.
Read more at: http://www.cmswire.com/analytics/how-data-blending-can-sweeten-your-insights/

Increase of a site's traffic is normally taken as a brand's visibility and relevance. But often things are not what they appear to be. Hence for making out whether the visitors are real or not data, leveraging is required. Establish What “Normal” Means for Your Business – The data and tracking should be made by such a source that in case of a drastic change the problem can be isolated and identified. In order to recognize what’s normal and what’s abnormal a baseline to work from has to be established. The performance data has to be reviewed consistently and methodically.
Not All Spikes Are Bad Spikes – Identifying whether abnormal spikes in the site traffic or analytics is good or bad is difficult. Logistics issues can be alerted by sudden spikes and dips.
Common Sense and Logic – Looking for twists and reviewing behavioural flows to identify pattern of users can be employed. This helps in understanding the key attributes of the traffic or the keywords. The emerging patterns can be used for proper optimization of the flow of the site.
Read more at: http://www.cmswire.com/analytics/ask-your-data-is-it-a-bot-or-real-traffic/

In recent days, since some data is sensitive and cyber crime has also progressed rapidly so it is essential for companies to ensure right data for the right people. As a result, new laws are coming forward to ensure the safety of data. Accidental leaks are regarded as one of the major cause of data breaches. Firms should make sure that they are closed to data hacking and vindictive fraud cases. Organizations use graph databases to recognize problems in the most efficient manner and to enquire about connected data. With the consequence of graphs, businesses have essential information to recognize the right people for the data and help the companies to secure their data.

Read more at: https://channels.theinnovationenterprise.com/articles/graphs-granting-access

Data breaches have always been a broad level concern, but what is more important is the impact data breaches have. Usually after a company suffers a data breach, strict regulations are enforced curtailing employee use of data. Thus the entire workforce loses data flexibility which in turn affects productivity. At times, hasty decisions can demoralize employees. Having a data-led approach to business throughout the company is definitely a welcome change. There are a number of ways to dispel fears surrounding data. Risks surrounding data can be avoided through three elements namely policy, training and education, and technology. Policy should guide through using data and devices. Training and educating employees is relevant in avoiding breaches. Technology will protect the business in case of a data breach. Having an intelligent, flexible and secure approach to information management will ensure effective use of data while guarding against risks. Read more at:https://channels.theinnovationenterprise.com/articles/don-t-be-scared-of-your-data

In the years, succeeding the financial crisis, the banking sector was restructured ranging from changes in regulations to customer service. The major problems that banks are facing include customer dissatisfaction, fraud, increased competition and regulations and all these issues can be solved using Big Data analytics. By leveraging transactional, behavioral and social data, banks can provide a hyper-personalized customer service. Risk management is another area where analytics can be of help. Big Data analytics can detect cybercrimes and predict the location of attack. It can also identify deviation in customer behavior which is indicative of fraud. Big Data technologies can be used to integrate external watch list screening system and unstructured emerging data sources. There is a huge scope for newcomers in Fintech sector to exploit Big Data as they are built keeping analytics in mind. The big banks should adopt new technologies to leverage the wealth of data they possess and maintain a competitive edge. Read more at:https://channels.theinnovationenterprise.com/articles/analytics-in-banking