Passwords are not just random texts that you need to gain access to your online accounts. These can completely change your life for the worse if they had gotten into the wrong hands.
An average person spends about 11 hours online each day. They chat with their friends, email their colleagues, play games, watch movies, or just catch up with their Facebook friends every day. And each time they open a website, they log in to their accounts, making their presence known to other people online.
If you think your password—which is probably the name of your pet, your birth date or your children’s names—is safe from prying eyes, think again. Most people don’t use a secure password, which makes them vulnerable to hacking. And when hackers are able to infiltrate one of your accounts, they may infiltrate all of them, including your online bank accounts and personal records.
And they can do all that by manually guessing your password or using automated programs to guess it. There are other ways of discovering a user’s password, but all these can be prevented, or at least slowed down considerably, by using a strong password. A strong password is your protection online.
Originally appeared here. Published with permission.
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It’s been a couple of years since the world was introduced to an exciting new career prospect. But the hype around data science still hasn’t died down. With it being predicted to remain the ‘hottest’ job for the next couple years, it doesn’t look like the buzz around data science would go away anytime soon.
And why not? Data science and its related job profiles have increasingly been the turning point of many enterprises. Quite a few companies have been hopping onto the data science band wagon in the hopes of better revenues and fresher business solutions.
However, as promising as it is, there are several challenges and difficulties which a data scientist has face in their career.
More than what meets the eye
Despite being one of the highest paying jobs in the software industry, everything is not all hunky dory in the world of data science. Studies show that nearly 13.2 percent of data scientists are looking for new jobs. As reported in an article published by the Financial Times last year, the field of data science has the second highest number of people who are unsatisfied with their jobs.
But why are data scientists so unhappy with their jobs?
At first, one tends to blame the candidates themselves. Due to the increased intertest, the demand for well qualified data scientists in industries is high. And so, individuals looking for jobs within the data science spectrum sure do have their fair pick of the crop. Which would lead them to be fickle and picky with the opportunities they explore.
However, the blame does not fall entirely on the candidate’s shoulders. A large portion of it falls on the companies who are looking to incorporate data science in their organizations.
Is the corporate world ready for data scientists?
With the interest around data science increasing, it is not surprising that every company wants to be a part of it. Data science is the cool new toy of the corporate world. And every kid on the block wants one.
However, being a relatively new field, not many companies know what to expect while setting up a data science department. Blindly following suit because everyone else is doing it, companies hire data science candidates without fully comprehending the necessity or the purpose behind it.
As Q McCallum mentions in his blog post, there needs to be a certain amount of preparation before a company should start hiring data scientists. Which includes compiling and preparing the data that must be analyzed. One of the major reasons why data scientists tend to a leave a company is the sheer amount of data that they are expected to sort through before they can begin the work their job profile calls for.
They end up dealing with poor quality of data, which include incomplete values, missing samples and poor representation of the samples they do have. This leaves them feeling discontented because their full potential as a data scientist is not realized.
On top of that, companies directly hire junior level data scientists with little to no experience in the field, since they do not expect much in terms of salary. But, without a senior to guide them, these rookies are left to navigate the large amount of dirty data on their own. This usually leads them to feel lost and frustrated. Eventually, they leave the company for more satisfying job opportunities.
The dilemma of a data scientist
Another major reason that drives data scientist to quit or change jobs is corporate politics.
Okay, admittedly, any politics in an organization can make anyone’s job a lot more difficult than necessary. However, since data science is supposed to have a direct impact on the improvement of revenue of the company, data scientists are often caught in the cross-fires of the upper management. So, it becomes extremely important for them to be on the right side of the right people.
Which means taking on a lot of additional tasks that have no relation to their job description. They become the go to person for anything related to data and numbers. And are expected to have the answers to everything at the right time.
For instance, data scientists are expected to translate the data into relevant points of action. Because, in all honesty, upper management is not interested in the numbers, but are interested how these numbers can be used to generate better revenue for the company.
And in enterprises which never had data scientists before, there would be certain amount skepticism from parts of the management. So, the data scientist must answer questions of individuals who do not buy in to their analysis and forecasts. Not to mention they are sent on a wild goose chase trying to sort and compile all the raw data in the first place. Which leads to resistance in data collection from the skeptics.
All of this put together can put a significant amount of stress on a data scientist.
In conclusion
In retrospect, the ultimate reason why individuals lose interest in their data science jobs is because the job never really lives up to their expectations.
When junior level data scientists first enter the field, they have a glorified image in their heads. They believe that they would be to solving complex problems using cool algorithms, and overall having a significant influence on business. And considering all the hype which surrounds the job description, it is not surprising that things tend to get a tad exaggerated.
However, after having mentioned some the challenges which data scientists face, it in no way means that aspiring data science candidates should be discouraged from pursuing a career in it. Borrowing the words of Jonny Brooks-Bartlett, a data scientist himself, the job can be fun, stimulating and rewarding.
If you think about it, every job available has their own set of challenges to overcome. What’s important is to find a place where you can fit in and enjoy what you do.
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Artificial Intelligence, commonly known as ‘AI’, is a technical concept that attempts to integrate human knowledge into machines. In layman’s language, it can be said that devices will now be feasible to perform those actions which generally require human intelligence. For example, lights will automatically light up when you enter the room, the refrigerator will execute operations on its own, and there will be self-driving cars.
Nowadays, it has entered to several fields and every organization is becoming more and more mechanical. It has already established its position in the market and there is hardly any enterprise or firm left whose devices are not enabled with AI technology.
Cryptocurrency can be termed as token money or virtual currency which can be used for carrying transactions on digital platforms. It has been a leading step in regulating the economic fluctuations and also creating the inflation-free economy. It has opened the new fronts to empower the individuals in economic aspects. In fact, people use it to surpass the government rules and regulations. However, if we consider the positive aspects of this new technology it will be right to say that it has become a necessity in today’s digitalized world.
With crypto traders rising day-by-day developers are now trying to integrate the AI and cryptocurrency in order to create ample opportunities in fintech industry. Moreover, cryptocurrency attributes such as decentralization, transparent processing, and faster execution rates etc are the great supporters of Artificial Intelligence entering into the field of token money.
There lies an example also where AI and cryptocurrency both have worked together. The Daneel Assistant Company made use of IBM’s Watson for providing intelligent solutions to investors in making decisions.
Here are some modular aspects which come to be absolutely true when AI and cryptocurrency gets integrated:-
1. No Biases
It is true that machines don’t bias in between two individuals. All the investors seem important and crucial to them. In fact, it appears as a blessing for those firms who need individuals to invest in their projects. But they fear that investors cannot make the decisions on their own and usually rely on others which highly impact their business. In that scenario, AI helps investors in taking rational and logical decisions. Thus, automatic trading is enabled which further leads the growth and development of several projects and ultimately heading towards increasing money-supply in the economy.
Therefore, in the cryptocurrency world which is more or less like institutional markets that generate data in megabytes, the task will be much easier if associated with AI. It is because AI enables the sorting of large data in shorter duration and investment decisions can be taken in less time and instantaneously. It is not at all doubtful that machines can perform faster than human beings.
2. Transparency
Cryptocurrency can be traded on blockchain platforms which are highly secure, reliable, and transparent. These are free from the centralized administration and are flexible in terms of executing operations. AI tools if integrated with blockchain platforms will also perform the functions following the integral approach. These tools aid in identifying the common patterns and make the executable strategies for them. In this way, these strategies can be used in making money through the tokens enabled by cryptocurrency. Also, AI and cryptocurrency integration is an integrated approach towards the creation of decentralized networks.
3. Cost Effectiveness
If the processes get absolutely mechanical then the organizations will not have to hire a large number of human resources for performing operations. It is almost like “killing two birds with one stone” in a way it will save the cost of an organization and payment gateways will also become transparent and secure with blockchain. There have been platforms which release the payments in an automatic way as soon as the task is completed. Thus it can be said that cryptocurrency is real-time money as it can be transacted within few seconds.
4. No Risks or Threats
The trading or investment processes are considered to be the riskiest task in the economic world. The cyber-frauds and malicious attacks by hackers and third-party are increasing. People can freely invest as there is no fear of cyber-attacks because smart contracts are considered to be the strongest and robust platforms where financial transactions are 100% safe. They are considered to be the best options for hastening the investment procedures. It also permits them to diversify their portfolios and earn hefty amounts in return. The unnecessary fear will not come in the investor’s minds.
All the above aspects are the common ones but the potential of AI ad cryptocurrency is yet to be seen. The change which internet brought to the world 20 years ago will now be again done through AI and cryptocurrency.
With the integration of AI and cryptocurrency, transaction execution will be limitless, diversified and much easier than ever. Also, it will be a dedicated and trustworthy network. The volatile environment of the market can be better analyzed using the machines than human brains can do. The AI enabled technologies can predict the consequences of market accurately.
There are many enterprises that are practically developing the applications capable of performing both AI and cryptocurrency. For example, Vega Intelligence Solutions is developing Vega AI and also doing crowdfunding to complete the project. It is one of the best ways to integrate both the technologies i.e. AI and cryptocurrency. Some new ICOs are already in circulation where features of both the AI and cryptocurrency are seen.
AI systems enabled cryptocurrency will be more efficient and perform transactions with efficiency. The processing speed will be doubled and the operations will be self-regulated. These technologies are the future of tomorrow. The operations are not executed by humans instead machines perform the functions which are completely based on human behavior. But one thing is distinctive that machines are more logical and less emotional which is said to be the explicit attribute of AI and that’s what makes it applicable for cryptocurrency world.
Final Words
With the Artificial Intelligence, every industry, whether it is technical, informational or deals with operations appears interconnected and interdependent. None of the zones is out of the reach from others. The maintenance of data in large sets and analyzing them in set patterns, interlinking of machines with the cryptocurrency is the real concept of Artificial Intelligence and cryptocurrency.
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Big data is the driving force behind more business decisions than ever before. As the amount of data being produced in real time continues to grow, so does the demand for people with the skill sets to help business analyze and manage all this data effectively. What does this mean for you if you’re deciding on a career path or a career move?
Big data equals big opportunities.
According to IBM, the market demand for data scientists will increase 28% by 2020. It is expected that the number of jobs for big data professionals will reach 2,720,000 in just two short years. What can you do now to get ahead of the trend and land a career with a great outlook, and a median starting salary of $120,000 a year? It starts with a great education.
Why you need a big data certification
Big data analytics has moved beyond the status of being just a trend or a buzzword. Businesses everywhere are realizing that big data analytics is crucial for success in industries that are constantly changing and learning to adapt.
Anyone with an invested interest in analytics, from the data engineer to the CEO, can only benefit from expanding their knowledge in this more important than ever field. Research these top programs and discover what a big data certification can do for you and your business.
Top universities around the world have responded to the rising need for talented and skilled data scientists by developing programs that meet the demand. Here are 10 universities that are at the top of their game with big data certifications.
Duke – Integrated Program In Big Data And Data Science
Duke University’s Big Data and Science Program, offered through the Office of Continuing Studies and Summer Session, is perfect for professionals interested in entering or boosting, a career in the field of big data analytics. This program was developed for those interested in developing and broadening their areas of expertise in the data science field.
The success of this program can be tied to an in-depth focus on the industry recommended learning path that includes Data Science with R, Big Data Hadoop and Spark Developer, Tableau Desktop, Data Science with Python and Machine Learning.
Gaining experience and expertise in these multiple data skills is essential to adapting and growing as a professional in the ever-changing field of big data analytics.
The Duke program is best suited for professionals who are looking to further their careers in
Stanford Executive Education – Big Data, Strategic Decisions: Analysis to Action
Stanford Business School offers a continuing executive education program in big data called Big Data, Strategic Decisions: Analysis to Action. According to the Stanford description, this program will allow professionals to “Harness the power of data analytics to improve decisions, gain a competitive edge, and enhance your company’s performance, products, and processes.”
Senior level professionals and major decision makers in every business can use this course to become more competitive in their fields and drive their business forward with an increased knowledge of data analytics.
Stanford’s Big Data program focuses less on concrete learning of technologies, as with Duke’s program, but instead leans toward the how and why of big data. Seminar topics include Why Big Data Matters, Using AI to Understand and Influence People, and Using Data to Make Better Marketing Decisions. Along with this, participants gain practical experience by working as part of a team with a Stanford Data Scientist in data simulation projects.
When it’s all said and done, what do you walk away with after completing the Big Data, Strategic Decisions: Analysis to Action program?
Northwestern Kellogg University – Leading With Big Data And Analytics: From Insight To Action
Northwestern Kellogg University takes the leadership approach to big data learning in their program Leading with Big Data and Analytics: From Insight to Action. The focus of this executive-level program is to help leaders make better decisions by providing them with the practical leadership tools of big data analytics. The strong theme here is real-world applications.
This program contains courses that help company and industry leaders understand their unique roles in big data and analytics. Some of the courses that participants will develop these skills through include:
And possibly, the most important course of all:
As a bonus, this program also offers optional tutoring sessions in key big data technologies, such as Tableau.
Rochester Institute Of Technology – Advanced Certificate In Big Data Analytics
With the Advanced Certificate in Big Data Analytics program at RIT, we move away from the executive leadership role in big data and focus in on the individual professional who is looking to forward their career by building their expertise in the field of analytics.
This advanced certificate program is geared towards professional with a BS in some fields where an understanding of data analytics is essential. This is the place for people with careers in computing, engineering, retail, manufacturing, and finance, that are looking to become formally qualified and increase their professional value with an important and relevant skill. All without the need to commit years to a graduate level degree program.
Program highlights include:
Plus, a choice of program electives in
Columbia Business School – Business Analytics: Identifying And Capturing Value Through Data
The 3-day program Business Analytics: Identifying and Capturing Value Through Data is laser-focused on helping business professionals of all levels understand the many ways in which big data analytics can provide valuable insights for critical thought and decision making.
Columbia Business School is ranked No. 2 by analytically focused journals due to their innovative thought leadership and research. It is obvious that Columbia showcased these qualities in developing this business analytics program.
Course structure includes:
University Of Washington – Big Data Technologies: Design And Build Big Data Systems
The Big Data Technologies program from the University of Washington was developed for professionals with a working knowledge of programming and working with big data sets.
With the list of admission requirements mentioning skills such as knowledge of SQL, experience with programming languages such as Java, Python or C3, experience with Google Compute Engine and knowledge of basic system management and configuration skills, this program is meant for those who are in the deep waters of analytics, more so than business executives.
The aim of this program is to enhance knowledge of the concepts involved in big data engineering, streaming applications, and distributed data storage. With a deepening knowledge of data engineering tools, participants will discover how new data technologies can be optimized to solve the challenges and frustrations of the data engineering field.
The program is split up into three sections that include:
The cumulation of which is a certificate that will ignite your career in the field of big data technologies.
Harvard University
The Principles of Big Data Processing course from Harvard is for those who have some experience in the field of Big Data. After taking this course, students will be able to understand the basic rules of creating distributed and easily accessible processing systems.
Using these systems will help in analyzing large volumes of historical and real-time data. The course focuses on data processing stages that are common to real-world systems. People taking this course are required to be able to work comfortably with at least one programming language like Java or Python and must also be familiar with cloud environments like AWS or Docker.
The course includes:
The course is available both online and offline, so students from all over the world can access it.
University of California, Irvine (UCI)
The Big Data course by UCI is for professionals and individuals who want to learn how to manage and analyze large volumes of data. This Big Data certificate gives individuals the skills required to gather and sort huge volumes of data effectively as well as make data-driven analysis and use algorithms to predict and extract competitive intelligence for their organizations.
Program Benefits
University of Toronto
With this Management of Enterprise Data Analytics course, students will be equipped with the skills necessary to become qualified managers in the field of predictive analysis. This course looks at the management perspective and weaves managerial practices into statistical and technological domains.
After taking this course, you will be able to use tools and techniques used by leading experts all over the world. The course comprises of case studies, demonstrations, projects, guest lectures from highly experienced instructors and will help you apply what you learn to real-life situations.
What You’ll Learn
After taking this course, participants will be awarded a certificate that is offered in collaboration with the Faculty of Engineering and Applied Science.
York University
The Certificate in Big Data Analytics course trains you to use and outline key opportunities to help your organization meet its strategic objectives. The course looks to train students on the applications of Big Data principles.
The course is great for specialists in areas such as marketing, insurance, finance, human resources, and policy deal with big data every day.
The course comprises of two certificates:
Students can take either one or both certificates. Each certificate is eight weeks long and students can earn their first certificate in six months. The university plans to release a full-time version of the course to allow students to finish in the certificates in 4 months.
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In what has been termed the biggest data theft to have ever occurred, nearly 70 million people from the United States alone have had user data leaked from their Facebook profiles. In total, as much as 87 million accounts around the world have been affected, if not more. And with the growing number of people getting entangled, both the victims and the guilty, this maelstrom doesn’t seem like it will blow over anytime soon. In fact, it has already reached global proportions.
We already know that Cambridge Analytica, the big data firm at the eye of the storm, was using user data harvested from a third-party quiz app, thisisyourdigitallife, on Facebook to build psychographic profiles of millions of American voters. In detailed reports by American newspaper The New York Times and UK’s The Observer, the full extent of the unethical data mining carried out by the company was exposed.
Soon on the heels of the initial reports, a series of under-cover videos by UK’s Channel 4, revealed that the political clientele of this firm was far more out-reaching than one would have imagined. Through the revelations of former employees of the company itself, including the CEO Alexander Nix, the data analytics company had also gathered data from British, Indian and Kenyan voters to name a few.
Through every coverage of these events, Facebook has painted an image of betrayal of trust by Cambridge Analytica. And while the methods with which the data mining firm collected user information was indeed illegal and underhanded, a large portion of the blame falls on Facebook as well.
The firm was only able to collect data from user accounts on due to loopholes present in Facebook’s data privacy policies. These loopholes made it easy for third-party app developers to gain access to the data of not only consented accounts, but their friends’ accounts as well.
The consequences Facebook will face
In the wake of this enormous data privacy leak, the founder and CEO of the social media giant, Mark Zuckerberg, has a lot of questions to answer. He has agreed to testify before the American Congress. And he would soon be facing the music in Capitol Hill on April 10th and 11th.
In a statement on the upcoming hearing, top Republican and Democrat representatives said that this hearing will be an important opportunity to shed light on critical consumer data privacy issues and help all Americans better understand what happens to their personal information online.
Several law makers have made it clear that they do not intend to let Facebook off the hook so easily. In the light of the massive breach of data privacy, it is not surprising that the American government, as well as governments of other affected countries, are extremely concerned over the Facebook’s policies for protecting user information. The social media network would most likely to face serious legal repercussions.
Sen. John Kennedy, said on Sunday that he believes the issue is “too big” for Facebook to fix on their own. On channel CBS’s ‘Face the Nation’, he said along with several other law makers have many questions for Zuckerberg. He would be asked to clarify what Facebook’s role in the trend of spreading misinformation. And the company’s policies in protecting user information from third party apps who harvest this data.
The governments of several of the other countries who were affected by the data breach have sent notices to Facebook as well, asking Zuckerberg to answer their own questions. Both the governments of UK as well as India, have asked the CEO to testify in their own countries. But so far Facebook has refused and have put out written announcements instead of testifying in person.
One thing is clear. That Facebook would come out of this ordeal as the strong independent company it was when it first started fourteen years ago. It would not be surprising if events played out the same way as they did twenty-one years ago, when Microsoft was hit with serious regulations for its aggressive monopolizing of the market.
What is Facebook doing about it?
Senator Kennedy’s fears of that this may be too big for Facebook to handle could be well founded. As it turns out the scandal with Cambridge Analytica was not a one-time incident. Another data analytics firm has been suspended from Facebook’s platform under suspicion of misleading users and unethically collecting their data. CubeYou is another data firm, which used similar methods to Cambridge Analytica to collect user information, in the form of quizzes. They informed users that the quizzes were part of “non-profit academic research”. The data, which was collected by researchers of the Psychometric Lab at Cambridge University, was being sold to marketers. Channel CNBC discovered the dupe and sent notifications to Facebook. The social media giant then suspended the firm for further investigation. They have stated that if CubeYou refuses or fails the audit, their apps would be banned from Facebook.
Earlier, in a public Facebook post, Mark Zuckerberg claimed responsibility for the massive breach of privacy. And promised to work on improving Facebook’s policies and fixing the loopholes that exist.
Starting from today, Facebook also rolled out a notification process which would alert users if their accounts had been breached, and information stolen. A link would appear at the top of an affected individuals News Feed, which would share details on the information which was stolen, and a list of apps and websites installed through Facebook. Options to delete individual apps will also be available.
#Deletefacebook Movement
Despite Facebook’s attempts at reassuring that they have learnt from their mistakes, the recent events delivered a serious hit to the company’s user base.
Many of Facebook’s users have expressed their displeasure to social network’s careless attitude to data privacy. The hashtag #Deletefacebook began trending on Twitter, with many notable personalities and organizations deleting their Facebook accounts and pages.
Among the list people who have turned their backs on Facebook is Elon Musk. In a response to a question tweeted at him, he removed the pages of both his companies, Space X and Tesla. Mozilla, the maker of the internet browser Firefox as well, announced that they have stopped any further advertising on the social networking website.
Millions of Facebook users have deleted or deactivated their accounts in anger as well.
Data privacy is the need of the hour
While deleting your Facebook account would help ensure that your own data would not fall prey of unethical data mining, it does not really solve the issue of data privacy. The case of Facebook and Cambridge Analytica is not the first time that user information was stolen and used for malpractices. And sadly, it would not be the last time.
But every cloud has its silver lining. The idea of data privacy and frightening lack of it is a discussion which had been stewing on the back burner for way too long. Due to Facebook’s massive slip-up, it has now been brought to the forefront of a global discussion. Hopefully organizations and governments who are capable and responsible for protecting internet rights of their people would be able to come up with better solutions. Already the EU has introduced the “General Data Protection Regulation” (GDPR) bill, which due to come into effect in May.
The world has already entered an era where one cannot live disconnected from everyone else. Unless one wants to live completely off the grind and in isolation. However, for every other individual who wants to stay in touch with the world, then the easiest way to do it is through the internet.
Hopefully, better data privacy laws would soon be in place, not only in Europe, but in the rest of the globe as well. And the knife wound which Facebook plunged into our backs, would soon be healed.
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The value of data-driven Customer Value Management or CVM cannot be underrated. Data and other algorithms/analytics that shape data are an imperative part of customer value management in a telecom company. With enhanced customer expectations, it is up to the ability of telecom companies to provide customers with a seamless experience and to also ensure that they help boost revenue in the process.
To understand this concept in a more functional manner, I recently interviewed the chief of CVM at Mahindra Comviva, Amit Sanyal. With so much on hand to discuss, I got to the crux of the matter straightaway and asked Amit about the pillars he considered to be important for a customer value management program being driven by analytics.
The prodigy responded to my questions by commenting that all methods of CVM being driven by the force of analytics should be dedicated towards these three pillars.
Amit also outlined that one of the key challenges facing telecom companies globally is a drop in revenue. The drop in revenue is because of numerous reasons that are making growth a very difficult option to undertake for all protagonists involved in the market. While all telecom operators are looking out for newer options in the form of fresh customers, it is imperative to note here that fresh customers are rarely found. Most geographies have network connections than the people living in it or very close to that, so there is a real shortfall of new customers coming in for new connections. Other than the shortfall in garnering fresh customers, Amit also highlighted how the revenues from current customers were decreasing. These revenues have been decreasing steadily for a while now, due to the high amount of competition between the firms present. Most over the top or data content services are free. Margins have significantly dropped, since operators cannot risk selling at expensive rates considering how there are other operators selling at reduced rates.
The solution to this problem lies in reaching out to customers in a seamless manner. Since revenues in the market can only be increased through acquiring fresh customers or earning more revenue through current customers, reaching out to the customers and understanding their data is an inevitable outcome that needs to be followed.
Since it has been mentioned above that there are limited fresh customers in the market, growth can only be achieved by bringing in customers from other operators. Simply put, you need to acquire customers from somewhere else to show your growth.
Besides bringing in new customers, you can also increase revenues from existing customers by understanding the economic concepts of elasticity and inelasticity. Operators need to know just what customers will be willing to spend their dollars on. Match the products and services they want with a price tag that gets customers to buy them.
Moreover, you can also increase the quality of your service. Subscribers tend to stay longer with an operator who offers quality. Not only will they stay longer, but they will also bring in new customers from other brands by telling them about the quality of your service.
To do all of this, you need to know just what the consumers are looking for. This is where the concept of machine learning and real time analytics come in. You should comprehend how your typical consumer behaves, and should also have a basic understanding of their preferences. You can use big data in the network systems, and implement methods such as predictive analytics and targeted communications to get the data that helps you understand them. By understanding their behavior and their preferences through real time data visualization, you can know just what will be perfect for your customers. Implementation of this method could open doors to data-driven customer value management and machine learning. Some of the stats pointing in favor of these changes are:
Data-driven marketing is key for enhancing the customer experience. Data driven marketing can help connect data points and link them together to create a more actionable context. Cases that highlight this are:
The implementation of data-driven marketing calls for a mindset change in telecom operators. Operators need to understand what customers prefer, and then they should reach out to them on a personal level through data. “Everybody loves to talk about data science, it’s a cool thing – but only a few really move towards implementing it” said Amit before concluding the interview.
Originally published here.
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