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deepakgupta312 last won the day on December 4 2013

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About deepakgupta312

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    Deepak Gupta
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  1. Hi Adit Thanks for your appreciation Almost everyone today considers data collection by organizations as a breach of privacy. As a result of which data governance is considered to be emerging discipline with an evolving definition. The discipline is a converging point for data management, data quality, data policies, business process management and risk management surrounding the handling of data in an organization. It’s through data governance that organizations and governments are looking to exercise positive control over the process and methods in use by their own data steward and data custodians. The Personal Data Protection Act has also been implemented by various countries. The PDPA establishes a data protection law that comprises various rules governing the collection, use, disclosure and care of personal data. It recognises both the rights of individuals to protect their personal data, including rights of access and correction, and the needs of organisations to collect, use or disclose personal data for legitimate and reasonable purposes. The PDPA provides for the establishment of a national Do Not Call (DNC) Registry. The DNC Registry allows individuals to register their telephone numbers here to opt out of receiving marketing phone calls, mobile text messages such as SMS or MMS, and faxes from organisations. A process know as de-identification(DeID-T) has also been implemented by many organizations and government agencies where in the organizations remove or obscure links between an individual’s identity and the individual’s personal information. This process involves deleting or masking personal identifiers, such as names and social security numbers, and suppressing or generalizing quasi-identifiers, such as dates of birth and zip codes. By using technical de-identification, organizations can transform sensitive information from being fully individually identifiable to being unconnected to any particular person. With publicly disclosed datasets, DeID-T provides the sole line of defense protecting individual privacy. De-identification is a process used to prevent a person’s identity from being connected with information. Organizations de-identify data for a range of reasons. Companies may have promised “anonymity” to individuals before collecting their personal information, data protection laws may restrict the sharing of personal data, and, perhaps most importantly, companies de-identify data to mitigate privacy threats from improper internal access or from an external data breach. Hackers and dishonest employees occasionally uncover and publicly disclose the confidential information of individuals. Such disclosures could prove disastrous, as public dissemination of stigmatizing or embarrassing information, such as a medical condition, could negatively affect an individual’s employment, family life, and general reputation. Given these negative consequences, industries and regulators often rely on de-identification to reduce the occurrence and harm of data breaches. Though I agree with you many laws have been enacted by various governments but are yet to be implemented in a comprehensive manner also what is required is two way understanding between people and organizations so that every stakeholder’s concerns are taken care of. I hope this helps Regards
  2. Hi Rupak Thanks for the appreciation It is known fact that educators and education system have forever worked under the assumption that there are only certain problem areas. These problem areas were not backed by actual data or statistics but were based on the assumptions and perceptions of our educators. Often the team responsible for school improvement would end up addressing up a problem that may or may exist, thereby wasting precious resources and time. Data driven decision making here comes to the rescue. Data provides the educators with an overview of the strengths and weakness in a targeted area. Once the targeted areas are identified , educators can commit their resources towards producing more efficient and effective programs. With the help of data analytics programs can be streamlined towards the specific areas. For example, in a school that does not use data-driven decision making, professional development may look scattered and unintentional. The staff may seem uninterested, or find the information not useful because they see no use to their particular area. If the school uses data-driven decision making to determine professional development, the staff will benefit because professional development will be focused toward their needs. When educators are able to draw inferences from data, they can, not only see the need for change, but can also identify the direction in which the change is needed, pinpoint the students needing intervention, and identify approaches offering promising solutions to help the respective students succeed. The use of multiple, and sometimes creative, sources of data enables school leaders to make mid-course corrections and continuous improvement toward academic success by their students. School that engage in data-driven decision making, have the information that not only measures students' progress in meeting standards, but also enables them in assessing current and future needs of students, parents, staff and the community; determine if goals are being met; ensure that students are not falling through the cracks; improve instructions; identify the root cause of problems; and, engage in continuous school improvement and help the students in their overall and all round development. To summarize I can say that data driven decision making can help in the following ways:- · Narrow achievement gaps · Improve teacher quality · Improve curriculum-development · Find root cause of problems · Share best practices · Communicate more effectively with key stake holder · Motivate students and increase parental improvement For example:- St. Paul is an urban district serving 43,000 students in Minnesota. With 34 percent of its students identified as Limited English Proficient and a high mobility index of 22 percent, analyzing student achievement proved to be difficult. To better meet the needs of its students, the district piloted a data-driven decision making system at one of its schools, Arlington Senior High School. The trial was set up as a targeted assistance model, serving the “highest” needs students who receive the most assistance. To do that, staff needed to examine test data on a regular basis and use it to identify the most academically challenged students in order to improve their test scores and provide them with an opportunity to graduate. Prior to implementing the data-driven decision making system, teachers would start the year with little or no knowledge of their students’ past performance. Data was only available for students who had taken tests in their building the previous spring, and it was not easy to use and required extra time for the teacher to retrieve. As a result of these obstacles, teachers used only overall test scores and pass/fail status. Data-driven decision making changed all of that. Upon implementation of data analysis techniques, teachers were able to look at scale score improvements on standardized tests to evaluate their curriculum, instruction and services. They could look at strands within a test to determine trends in performance and adjust curriculum and instruction accordingly. They also looked at strands to identify and assist students through the school’s comprehensive tutoring program. Rather than having to key in data manually, the student information was updated nightly, giving teachers real-time information about the students sitting in front of them. They could review classes by overall test score/performance and drill down to the strand/sub skill level to see how students performed. As a result of looking at data by class and grade level, Arlington began curriculum mapping and a building-wide literacy initiative. Seeing the data in "black and white" and taking the time to understand and interpret the data helped Arlington solve critical building-wide curriculum and instructional issues. The project was so successful that efforts are underway to expand the data-driven decision making system’s use across the district. Hope this address your query Regards
  3. Hi Utkarsh Thank you for the response Utkarsh allow me to bring to your notice the fact that we have moved from a world where mass consumption and mass production was the order of the day and the consumer was more obsessed with consuming the product rather than worrying what more it could derive the product . Today a customer looks more at what intangible value the product provides along with intangible value. People today want value for money, more for less, mass customization is the order of the day. There was a time when a single product satisfied a large customer base but today every customer wants a customized product for himself/herself. Today you cannot afford to make decision based on gut alone , you need statistical data to support your ideas, innovation. With gut based decision making you might succeed or fail but with the help of data analytics esp. predictive data analytics you will be able to forecast future demands, initiate innovation where none exists because data analytics are able to find a pattern within customer behaviour, which might end up increasing your chances of success. Data driven decision making is not fool proof but is tool to aid your decision making. So that you make an informed decision rather than going solely on the basis of your gut. Companies have been forced to shut down because they did not embrace data driven decision making. Data driven decision making is what has made companies like Tesco, Wal-Mart, and Amazon to name a few. How did they beat competition??? , How did they increase their sales? The answer to these two questions lies in the fact that they collected customer data, quality control data and used it to improve their processes and marketing techniques. Today with the rapid pace of technological development, a person surfing the net leaves his/her footprints all over the internet in the form of small amounts of data. Companies collect this data, process it and then use it target customers. For example, by recognizing who the customer is and what purchases they have made, Tesco is attempting to predict what they are going to do next and intervene with relevant offers. It is a simple premise – and one that, if successful, could attract customers and lead to greater profits. Consider retailing for example. Traditionally, booksellers in physical stores could always keep a track which books sold and which did not. If they had a loyalty program, they could tie some of those purchases to individual customers. And that was about it. Once shopping moved online, though, the understanding of customers increased dramatically. Online retailers could track not only what customers bought, but also what else they looked at; how they navigated through the site; how much they were influenced by promotions, reviews, and page layouts; and similarities across individuals and groups. Before long, they developed algorithms to predict what books individual customers would like to read next—algorithms that performed better every time the customer responded to or ignored a recommendation. Traditional retailers simply couldn’t access this kind of information, let alone act on it in a timely manner. It’s no wonder that Amazon has put so many brick-and-mortar bookstores out of business. Big data has the potential to transform traditional business also as it can offer them even greater opportunities of competitive advantage. We can measure therefore can manage more precisely than ever before. Data driven decision making helps us by making better predictions and smarter decisions. Today we can target more effective interventions, and can do so in areas that have been so far dominated by gut and intuition rather than by data and rigor. I hope I have been able to satisfy your query Regards
  4. Hi Vishwadeep Thank You for the review In today’s world , innovation is the key to success but how far it will be successful depends on the amount of under served needs of the customer it address to. We all are capable of innovation but the key differentiator is the amount of success the individual innovation is capable of achieving. This is where data comes into picture. Data analytics can uncover patterns where none exist and help us in giving a direction towards the same. Every innovation we make can only be successful if it addresses the customer’s needs and wants. With copious amount data available today, it is not impossible for us to uncover the under served needs of customers. Data driven decision making can also be used in developing real world models virtually on which multiple scenarios can be tested with the help of predictive analytics. The data being generated today has created a transparency between the various stakeholders involved and usable at high frequency. Data has helped in bridging the gap between customer and manufactures. This can be seen from the fact the today companies are being increasingly focused on collection of data which further helps them in customizing their product for the customer. What we are looking at is customization on a massive scale given the fact that big data allows even narrower segmentation of customers and thereby enabling development of much more precisely tailored products or services. Another example of innovation and data analytics is Amazon has recently come up with an innovative of delivering goods within 30 minutes of their purchase. A futuristic mini-drone delivering plan, which are programmed to operate autonomously and drop items at the target locations according to GPS coordinates fed into them. Now how did they come up with such an innovation? The answer to that lies in predictive data analytics. They analysed data and came up with such an idea. Today organizations can create and store more transactional data in digital form, they can collect more accurate and detailed performance information on everything from product inventories to sick days, and thereby exposing variability and boosting performance and innovation accordingly. Big data can be used to improve the development of the next generation of products and services. For instance, manufacturers are using data obtained from sensors embedded in products to create innovative after-sales service offerings such as proactive maintenance (preventive measures that take place before a failure occurs or is even noticed) and also offering products similar to the ones purchased by the customer. For example Tesco has come with an innovative idea by merging innovation and predictive analytics. It has initiated offers, in real time, products related to existing purchases. A loyalty card is inserted into a device that is fixed to a shopping trolley, through which tailored offers are made to the shopper as they place items in their trolley. For example, a shopper may place a barbecue chicken in the trolley and then receive a 3-for-2 offer related to the item, such as charcoal for barbecues. By recognizing who the customer is and what purchases they have made, Tesco is attempting to predict what they are going to do next and intervene with relevant offers. It is a simple premise – and one that, if successful, could attract customers and lead to greater profits. Some financial companies have started recognising that a certain type of customer responds negatively to payment reminders – if left alone they will make the payment, but if nudged they will dig their heels in and delay payment. By identifying these behavioural trends, companies are able to optimize their operations and improve customer relations. Now who would have thought that customer relations, which involve human contact like face to face communication, could be merged with data analytics and used to improve the same? Another example would be of the Gaming company Bigpoint , creator of Battlestar Galatica using predictive analytics to monetize players and increase revenue by a projected 10 to 30 percent every year. Seeing all these examples we can see that innovation in whatever form today needs help of data driven decision making to make it successful. It is responsible for increasing the success rate of an innovation by helping the maker in deciding how to market the innovation and reap huge profits.
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