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Category: Analytics

What are the Best Change Management Strategies?

Posted on by Imarticus Learning

Change is the only constant! Organizations have to introduce timely changes in their strategies, structure and operations to evolve and to match the ever-changing business environment. However, any attempt to change will have to deal with the inertia, and this is possible only with a diligently planned strategical approach. An announcement on introducing a change could stir panic in employees if they have no clue about what the change is all about. To avoid this, you need to communicate with the employees and educate them on the changes you’ve planned and why those changes are important. We are going to discuss some strategies to make changes as smooth as possible.

Change Management Strategies

Plan carefully
Planning is everything. Before introducing any change or even announcing it, you need to have a clear idea about what you want to achieve and how are you going to introduce it among the employees. Document the changes and things to do to achieve them, craft a detailed timeline and have a clear response to the potential concerns of the employees.
Be transparent
Confidentiality is a part of the change. It is a usual practice to keep the plan confidential among the top management. However, it is wise to announce the change before any rumour makes rounds among the employees. Rumours make it even more difficult to convince the employees as they have already made up their minds that the change is going to affect them and that’s the reason the management wants to keep it confidential. Announcing the change and promote discussions about it will help clear the doubts and prepare them to deal with it.
Tell the truth
Never try to sugarcoat the facts or try to be overly optimistic. This will only help to make the employees suspect the worst is to come. If there are any short-term negative outcomes, discuss them. Acknowledging the potential; drawback sand the effort to mend them will induce confidence among the employees and they will appreciate the efforts of the management.
Communicate
Communication is the key to win the game. Explain why the change is important and what benefits do the management expect from this. Be open to questions, hold team meetings to discuss the changes.
Build a Roadmap
This is important to make the employees understand the current situation of the organization and what is the organization aiming at. This will also help you communicate that the management has a clear thought and strategy to deal with the change.
Conduct Training Sessions
If the plan of change involves the introduction of new technology, make sure that you arrange adequate training sessions for the employees. Announce that the training will be available for them. This will eliminate the insecurities among employees that they will be left behind when the organization introduces the new technology because they do not have the skill or experience to use it.
Proposals for Incentives
One effective way to introduce the change is to propose some incentives to the employees. This will send out a message that the change could be beneficial for them and encourage them to engage with the plan and to adapt to the change with time.
Redefine Organizational Values
Employees would be ready to adapt and fit in with the organizational values. So, introduce a change in the cultural values of the organization and make it a culture of continuous improvement. The employees may respond positively to a new way of working if you introduce the new organizational value of a continuous improvement.
Conclusion
Change is a big thing, especially for employees. So do not expect them to change overnight. You need to help them prepare for the change and to deal with it. Ensuring their participation in every stage of the change is the best way to be transparent and to convince them that the change will bring positive outcome to them as well as the organization.

Machine Learning and Information Security: Impacts and Trends

Posted on by Imarticus Learning

Machine Learning and Information Security: Impacts and Trends

Gone are the days when we needed to patiently sit and teach computers how to perform complex tasks that were backed by human intelligence. Today, the machine teaches itself– far from ‘magic’, it’s a tool that has revolutionized industries across the board today.

For context, machine learning is as significant a change for the world as the introduction of the Internet was. The future of machine learning encompasses more than just tech and related industries. Cybersecurity– more specifically, information security– has been heavily impacted by the introduction of machine learning in a mostly positive manner, but some grey areas exist.

What is Information Security?

InfoSec is the network of processes and systems designed for and deployed to safeguard confidential information, largely business-related, from destruction or modification in any way. InfoSec is not the same as Cybersecurity, albeit it is the part of it that is dedicated exclusively to data protection.

The types of Information Security span cloud security, cryptography, infrastructure protection and detection and management of vulnerabilities. Most Machine Learning training courses brief students about these facets, not least because they’re universal in their use across industries.

Machine Learning in InfoSec: Impacts and Trends

Automate repetitive tasks

Setting up ML algorithms to take care of everyday threats can help ensure a regular check on the underlying security. This also allows security analysts, supported by more complex algorithms, to focus their strength on bigger tactical fights an set up bulletproof systems. This frees up a lot of time on the team’s hands and cuts costs on holding onto employees for repetitive tasks alone.

Endpoint security control in mobile devices

With mobile passwords being the quickest and easiest springboard to accessing information worth selling, cybercriminals are increasingly preferring to target mobile devices. To counter this, machine learning techniques include ‘zero trusts’ no sign-on approaches that eliminate passwords and cloud-based authentication systems.

Predict and preempt strikes on systems

Predictive analytics is fast becoming a core facet of InfoSec systems today because continuous analysis and correlation mean better chances of recognizing patterns and threats ahead of the actual strike. Using AI and machine learning techniques to capture, analyze and classify data in real-time is a benefit that no other system has offered so far, least of all human systems. By identifying potential threats, businesses can prepare in advance by strengthening security, putting extra authentication processes in place and running audits.

Cloud-based security systems

In place of saving millions of customer data on chunky servers prone to breach, businesses are increasingly moving to cloud-based security systems. These systems allow all information to be kept in one place with hefty security barriers in place, with the help of machine learning, to prevent breaches. These systems keep the reins of authority in the hands of a few, making it easier to trace leaks, if any, and allow for timely intervention.

The future role of machine learning

In setting up a top-of-its-class, dynamic security landscape, machine learning plays several roles, both routine and complex. Machine learning training is the talk of the town today because companies want their employees to be more than capable of using machine learning data for the betterment of InfoSec at any organization.

The Hike In Demand In Data Science Can Place The Way For Greater Youth Employment ..!!

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The Hike In Demand In Data Science Can Place The Way For Greater Youth Employment ..!!

These days the gold rush is around the oil of the digital era – data. According to LinkedIn, the career in the field of data science has seen exponential growth, becoming the Harvard University has labeled the position of a data scientist as “the sexiest job of the 21st century”.
With the AI hype in recent years, more and more companies are becoming more data-driven. This has, in turn, created a huge gap in the demand-supply curve for skilled professionals in the world of data. The demand for Analytics skills is going up steadily, but there is a huge deficit on the supply side.
There is a profoundly visible rise in data-related job profiles, like data analysts, data engineer data scientists, database developers, DevOps professionals; new profiles are being invented, like decision scientists. 2019 saw a 2.9 million open jobs requesting for analytical skills.  According to recent data from job sites like Indeed and Dice, it is a great time to be a data scientist entering the job market. The average salaries for data scientists and analysts have grown suggestively. The current demand for qualified data professionals is just the beginning.
India currently has the highest global concentration of analysts. Even with this, the scarcity of data analytics talent is particularly high and the demand for talent is expected to rise as more global organizations are outsourcing their work. In the next few years, the size of the analytics market will evolve to at least one-third of the global IT market from the current one-tenth.”, says Srikanth Velamakanni, the co-founder and CEO of Fractal Analytics.
The Google Analytics data suggests that in recent years, there has been a significant rise in people who get curious about data, and data science training. With this large scale demand for skilled professionals, there are two scenarios that are developing. It is seen that industry professionals are upgrading their skill sets in the field of data science and machine learning. Corporates are promoting this kind of skill up-gradation by means of internal training as well. Technology professionals who are experienced in Analytics are in high demand as organizations are looking for ways to exploit the power of Big Data.
Another, more interesting scenario is that more and more youngsters, from college students to fresh job seekers, are flocking to the world of data. This is a positive note, as it will increase the creative pool in the field and also provide more innovative solutions to current scenarios. New and innovative ideas and approaches are already being tried and tested in different real-time scenarios that have powered the AI hype even more.
From a career point of view, there are so many options available, in terms of the domain as well as the nature of the job. Since Analytics is utilized in varied fields, there are numerous job titles for one to choose from. According to the Indeed report, data science job searches follow a somewhat seasonal pattern. In 2017 and 2018, searches peaked in April or March, reflecting the influx of students searching for internships, or soon-to-be graduates looking for their first jobs. Organizations are using various hackathons and hiring competitions to find a suitable talent pool out of the masses.
There are more data scientists entering the job market — either from graduate programs or after getting “nano degrees” from massive open online courses. Along with the rise in demand, there was also a similar rise in various data science training institutes, certifications, and courses.
An important factor to consider in these is the authenticity of such institutions and the value of such training in the market. With hands-on projects and good exposure, courses of institutions like Imarticus Learning stand out among all courses in the job market.

What Are The Tips To Prepare For a Hadoop Interview?

Posted on by Team Imarticus

The popularity of big data has been growing at an immense rate opening the doorway to a spectrum of jobs that require skilled professionals. Noteworthy among these is the job of a Hadoop developer; challenging, technical and well paid, Hadoop is known to be one of the best segmentation of big data and analysis and a developing platform for candidates interested in a career in data science.
Learn Hadoop to pursue a career as a Hadoop analyst, Hadoop developer, or a Hadoop Architect, Hadoop tester among other job roles on the Hadoop platform. If you are looking for a career in this domain, it is highly essential to understand that a Hadoop developer not just created codes in programming but is also expected to have an expertise of multitasking while as his job, which includes programming in Java, writing scripts, reviewing log files, scheduling jobs across clusters on Hadoop amongst others.
Basic skill set for a Hadoop interview
Hadoop works with a number of other software like Ambari, HBase, Hive, Pig and more, therefore, knowledge of technologies is essential. While it is important to also have an idea about other visualization and ETL tools, SQL, gateway and edge nodes, basic cloud computing, some of the must-have skills an interviewee needs to possess during Hadoop training include JAVA, Hadoop Framework, Pig, HDFS, MapReduce, and Floop.
Tips to prepare for a Hadoop interview
Cracking a successful Hadoop interview does not essentially mean having specified skillsets but also ensuring that all of the interviewee’s questions are addressed. While Hadoop in big data is a relatively new concept, here are a couple of tips to help you prepare better for an upcoming Hadoop interview.
Knowledge of Programming Languages
Java experience is as important as it can since Hadoop is a software-based on Java. If your career path monitors progress from C++ to Java, nothing like it. Knowledge of other programming languages like OOAD, JS, Node.js, and HDFS only add to your skillset and make your resume stand out from the rest of the candidates.
Big Data experience
If you have experience working with big data, a Hadoop interview would be fairly easy to crack, since Hadoop is mostly built for the working of big data.
Technical Expertise
To crack a Hadoop interview, you not just need hard skills for Hadoop but also various other technologies that include Flume, Sqoop, Hive, Pig and more. These technologies often seem smaller, however, they make data processing easier on Hadoop.
Interview domains that are essential to prepare for
Along with a good grasp of relevant skill sets, listed below a couple of interview domains every interviewee needs to prepare for-
Practical experience
Theoretical knowledge is important, however, most interviewees are tested on practical knowledge. Expertise in the practical field subjects candidates to various degrees of exposure otherwise impossible by merely learning theories.
Communication Skills
Hadoop experts have to communicate with people in various other job roles, that often include engineers, analysts or even architects. In cases like these, good communication goes a long way.
Knowledge of domain
The interviewee is expected to know the A-Z of Hadoop along with its basic functionalities. You may be expected to back your interview answers with sufficient theoretical or analytical examples.
Conclusion
Big data is growing at an immense rate and more professionals are getting enthusiastic to work in the field. An extensive Hadoop training can go a long way in helping a big data enthusiast to master the best skills in the market and make it big as a professional.
For more such information, feel free to visit – Imarticus Learning

Who Can Do Agile Certification

Posted on by Imarticus Learning

With the constantly evolving age of digitalisation and technological advancements, software development if not longer just traditional. This is where agile steps in to address the modern software needs of most businesses or organisations. With this arises a need for agile certification among young tech professionals in the IT sector.

If your interest likes in taking your career skills a couple of notches higher, the best idea is to sign up for an agile certification course. Not only does agile teach you the latest skillsets but also lends skills that are accepted on a global level, which means your resume has a possibility of standing out in the crowd.

Why an Agile Certification

Most organisations look for experienced and skilled professionals with an understanding of project management; experience in agile certification along with working technical knowledge does the job. In case you are looking for a change in your career prospects, it may turn out to be highly beneficial for you to consider pursuing a career in the agile domain. Some of the added benefits that come with an agile certification course are:

● Better job prospects
● Fatter paychecks
● Added credibility
● Enhanced skill sets in accordance with market trends

Who can do agile certification?

Agile certification courses require a couple of prerequisites as listed below-
● You have to atleast 2000 hours of working experience on teams or set PDUs
● You need an agile practice that needs to be of 21 contact training hour duration
● An additional 1500 hours of working on methodologies of an agile team is also important
● A minimum of 12 months of project management experience is essential

Top agile certifications for you

Merely making up your mind for an agile certification course will not do the trick. There is a multitude of options in the market, some of which may confuse you for good. Listed are the top agile certifications you can consider-
ACP
ACP is commonly known as the Agile Certificate Practitioner and is amongst the best in project management certification courses. This certificate is tailor-made for professionals who are considering moving to agile and pursue project management interested in using project methodology.
CSD/ CSM
Agile Scrum professionals can also pursue a course in Certified Scrum Development or Certified Scrum Management. Both options are ideal for you if you are interested in scrum project management.
CSP
A CSP or Certified Scrum Professional is a top agile certification along with also being one of the best project management certifications. A CSP course helps you prepare for various roles and practices followed in agile along with getting you well versed with the procedures of the basic framework in scrum.
PSM
The Professional Scrum Master certificate is for professionals who already have a fair amount of working knowledge of agile. Perfect for you if you are already a scrum developer, scrum master or a scrum product owner, this certificate validates your knowledge in agile and scrum.
Conclusion
An up and coming avenue, agile certification is a good training methodology for tech professionals looking for a switch in career opportunities.

What is Agile training ?

Posted on by Imarticus Learning

In the field of software development, agile is the ability to respond to changes – changes from requirements, technology, and people. In contrast to the traditional methodology of heavy-documentation, process-oriented and back to back approach, agile offers an iterative and incremental process where a task is broken down into pieces and each task is performed for a particular time interval. Agile is termed as an approach and not a methodology.

Since agile is not a methodology, there is no specific way to implement it. It cannot be learned and implemented like any other concept. Hence, agile training is very important.

Definition 
Agile training is termed as the process of teaching agile concepts to a company or an organization for successful project management. The agile development process involves direct collaboration with customers. Each iteration of the process lasts from one to three weeks where the software is developed in multiple increments and engineering actions are carried out by cross-functional teams.

Agile implementation can be done with the help of several frameworks such as scrum, kanban, and XP. Out of all these available frameworks, scrum is the most widely used for agile implementation. More than 90% of the estimated agile team use scrum. Hence, scrum forms an essential part of agile training.

Scrum Framework
When we say that scrum is an essential part of the agile training, the question that comes to our mind is, what is scrum in agile training? how does it contribute to the agile approach?  how to implement scrum so that the agile concept is maintained?

In software development, a client asks for the product to be developed according to specific requirements. The agile team comprising of scrum master, product owner, and scrum team divides the total work into parts. Each such part is one to a two-week-long period where the scrum team comprising of the developers, testers, etc. work on the committed part of the work for that duration.

During each sprint, considerable feedback is given to the members of the agile team to produce the desired result. The development in sprints continues until the entire product is delivered to the client.

Agile training
Agile is not something that can be mugged up or by hearted. It is a way in which things can be looked at. Several certifications in agile such as CSM (Certified Scrum Master), Certified Disciplined Agilest (Exam: CDA) is available.

Agile training not only imparts knowledge about its frameworks but also teaches the importance of project management. It helps in the proper planning for project execution and adds a clear focus on the achievement of the desired goals. It improves leadership skills by helping the agile team to bring the best out in them.

What are the Different Change Management Models?

Posted on by Imarticus Learning

Change management is one of the major challenges you might encounter. It is possible that your team rebel against the changes you are trying to implement. It is a hard task indeed! No wonder that 70% of all change initiative taste failure. This is where change management models come into play. There are many change-management models for you to choose from. These change management models offer a plan of action to implement the changes, rather than jumping head-first to the crisis.
McKinsey 7-S Model
This is best used before you begin a change in the organization/team. This is used to comprehend why a change is essential at a given point of time. It helps you figure out what are the changes you should bring in. It essentially helps in introspection of a strategy, or to perform an analysis. McKinsey 7-S model is based on analysing the 7 elements: 4 soft elements and 3 hard elements.
Soft Elements
Shared Values – company values
Skills – core competencies of the employees
Style – leadership style
Staff – employees
Hard Elements
Strategy – business strategy
Structure – hierarchy
Systems – business processes and rules
This model suggests that the elements mentioned above should be aligned to ensure the success of an organization. For instance, if you are adopting a strategy to penetrate the market, you need to ensure that you have the right skills. The leadership style should be in a way that guides the staff towards the new strategy. You need a structure that supports this, and the values and systems should be in line with the goal.
Lewin’s Change Management Model
Once you decide on what change do you want to bring in, you can use Lewin’s change model to execute it. However, this is suitable for large-scale change.
The model operates in 3 steps
Unfreeze: In this step, you are going to let the employees know that you have decided to make some changes to break down the status quo.
Introduce the change: You start making the changes slowly, with proper planning.
Refreeze: You have already made the change and made those changes a part of the process to follow from here on.
Deming Cycle
This is suitable for small changes. This is done in four steps.
Plan: In this step, you identify the limitations and inefficiencies of the current process, contemplate and come up with a new strategy to improve it.
Do: You have decided what changes to make, now it’s time to implement them on a small scale so that you know how the new process work.
Check: You inspect the newly adopted process to see the improvements. You need to check if it is working better than the old one, and also if it is feasible to work in the long-term
Act: If the new process shows good results, you scale it up and implement them company-wide. If it does not show the expected results, you call off the changes and go back to the old process.
Conclusion
Changes are part of improvement. You need to revive the process and operations to derive better results and profit. Bringing in changes to replace a full-fledged running system is challenging, risky and scary. Reading about the implementation is very easy but is a herculean task to implement the changes.  You need to follow a proper plan to execute the new strategy without messing things up.  The change management models discussed above will help you plan a big change and to implement it.

AI Helping The E-commerce Stores To Dramatically Increase Conversions

Posted on by Imarticus Learning

 

In today’s era of continuously developing science and technology, artificial intelligence has touched almost every possible domain in our lives. It has revolutionized technology to mimic the human brain. From self-checkout cash counters at malls to advance security check systems at airports, AI has left its footprints everywhere. It has set a considerable benchmark in the field of e-commerce by providing a wide range of personalized experiences and creating new standards. According to a recent analysis, more than 80% of the human interactions in the future will be held by artificial intelligence. The key features that create a dramatic increase in conversions are listed below.

Chatbots

Chatbots are intelligent conversational agents that use Natural Language Processing as a base to provide the machine with the ability to understand and process human interactions. They are designed to cater to the user needs by providing product recommendations, customer support, and fulfillment of customer purchases. A large number of sales chatbots are being used to provide a personalized user experience. They interact with the customer in a simple question-answer format and provide a suggestion based on the previous purchase history or other buying trends, thereby creating an increment in the sales.

Recommendation Systems

Most of the websites dedicated to online shopping make use of recommendation systems. These systems collect data related to each customer purchase and make suggestions using artificial intelligence algorithms. The main concept used in the recommendation system is to create the sale boost either by suggesting a product based on the purchase history (personalized recommendation) or by promoting the popular products (non-personalized recommendation). They provide an appropriate product suggestions thereby optimizing the website for a boost in sales and increased conversion.

Visual Search Engines

With the help of AI, desired products can be found with just a single click. All a customer needs to do is to take an image of the desired product and provide it as an input to the search engine. The search engine recognizes the desired item based on its specifications and provides appropriate results. Providing accurate results saves the customer time and effort in going through multiple products and the relevance of the system results in increased sales.

Speech Recognition

As AI is expanding every day, it is providing great support for e-commerce by implementing user efficient techniques like voice assistants. By analyzing the sentences used by the customer for the desired product, accurate suggestions are provided thereby raising the bar of user convenience in online shopping. Thus, voice assistance has been proved as one of the most successful tools in providing a smooth, personal and more humane touch to the user experience.

Conclusion

The application of AI in the e-commerce sector has greatly succeeded in providing a better user experience and significant sales increment. It has directed the trend from the traditional shopping approach of visiting the shops to a more advanced yet convenient method of online shopping. It has created a platform for increased conversions with the help of technologies like speech recognition, visual search engines, recommendation systems, better user experience and much more. In simple words, it has made the process of buying and selling swift and efficient for the users.

Artificial intelligence, through its attempts of increasing conversions, is creating rapid advancements in the field of science, technology, and e-commerce. This can be considered a key reason for the recent rise in the number of individuals veering towards artificial intelligence training and opting for machine learning courses.

E-commerce firms are continuously working in the direction of improving the artificial intelligence tools to meet the modern-day market trends. SEO (Search Engine Optimization) integrated with proper marketing strategy and artificial intelligence features like personalized recommendations and sales intelligent chatbots is continuously creating wonders in the e-commerce sector.

The Common Data Science Interview Questions To Remember..!!

Posted on by Imarticus Learning

Data science interviews are often considered to be difficult and it might be difficult for you to anticipate what questions you will be asked. The interviewer can ask technical questions or throw you off guard with questions you hadn’t prepared for. To pursue a full-fledged data science career, it is important for you to be up to date on an array of questions that might be asked during the interview, ranging from programming skills to statistical knowledge, or even field expertise and plain communication skills.
Here is a segmentation of the various categories along with the list down of the possible questions you can expect in each category as an interviewee during a data science interview.
Statistics
As an interviewee, it is essential for you to be prepared on statistical questions since statistics is considered to be the backbone of data science.

  • What are the various sampling methods that you know of?
  • Explain the importance of the Central Limit Theorem.
  • Explain the term linear regression.
  • How is the term P-value different from R-Squared value?
  • What are the various assumptions you need to come up with for linear regression?
  • Define the term- statistical interaction.
  • Explain the Binomial Probability Formula.
  • If you were to work on a non-Gaussian distribution, what is the dataset you would use?
  • How does selection bias work?

Programming
Interviewers may ask completely general questions on programming to test your overall skills or may try and test your knowledge on big data, SQL, Python or R. Listed are a couple of questions that may turn out to be relevant for you to crack that interview like a pro.

  • List the pros and cons of working with statistical software.
  • How do you create an original algorithm?
  • If you were to contribute to an open-source project, how would you do it?
  • Name your favorite programming languages and explain why do you feel comfortable working in them.
  • What is the process of cleaning a dataset?
  • What is the method you would take for sorting a large list of numbers?
  • How does MapReduce work?
  • What is Hadoop Framework?
  • If you are given a big dataset, explain how would you deal with missing values, outliners and transformations.
  • List the various data types in Python.
  • How would you use a file to store R objects?
  • If you were to conduct an analysis, would you use Hadoop or R, and why?
  •  Explain the process using R to splitting a continuous variable into various groups in R.
  • What is the function of the UNION?
  • Explain the most important difference between SQL, SQL Server, and MSQL?
  • If you are programming in SQL, how would you use the group functions?

Modeling
While a Data Science Course will teach you the basics of modeling, at an interview you may be asked technical questions like building a model, your experiences, success stories and more.

  • What is a 5-dimensional data representation?
  • Describe the various techniques of data visualisation.
  • Have you designed a model on your own? If yes, explain how.
  • What is a logic regression model?
  • What is the process of validating a model?
  •  Explain the difference between root cause analysis and hash table collisions.
  • What is the importance of model accuracy and model performance while working on a machine learning model.
  • Define the term- exact test.
  • What would you rather have; more false negatives than false positives and vice versa?
  • Would you prefer to invest more time in designing a 100% accurate model, or design a 90% accurate model in less time?
  • Under what circumstances would a liner model fail?
  •  What is a decision tree and why is it important?

Problem Solving
Most interviewers will try and test your problem-solving ability during a data science interview. You may be asked trick questions or be subjected to topics that evoke your critical thinking abilities. Listed are some questions that will help you prepare for an upcoming interview.

  • How would you expedite the delivery of a hundred thousand emails? How would you track the response for the same?
  • How would you detect plagiarism issues?
  • If you had to identify spam social media accounts, how would you do so?
  • Can you control responses, positive or negative to a social media review?
  • Explain how would you perform the function of clustering and what are the challenges you might face while doing so.
  • What is the method to achieve cleaner databases and analyze data better?
    For more such articles, feel free to click on the below link:
    How To Build A Career in Data Science?

How Big Data Is Changing The Way Marketing Teams Strategies?

Posted on by Imarticus Learning

Big data in today’s world
Big data is transforming the way how the world thinks. Corporations have an appetite for data and they churn out almost everything from it be it vital or useless information, segregate the analysis on different parameters and draw out multiple conclusions. Today, we live in a data-driven world where everything from schools to offices, amusement parks to movie theatres runs, etc. runs on data.  Big data has become a very prominent turning point in the history of the world economy, therefore, knitting the world together faster and better.
Importance of marketing
Any company can produce a product or plan out to provide a specific service. The challenge is to take that product or service to millions of people who will then buy those products or avail those services, thus helping the companies to fulfill their ultimate objectives. In such a situation, Marketing comes into play. Marketing is a set of activities which are brought together to increase the mass reach of any company and its offerings. With everything shifting to an electronically operated platform, there is a strong gap that is constantly being filled with online marketing. Businesses are promoted online using multiple online channels such as search, videos, emails, ad campaigns, etc.
Big data and marketing
With the movement of the marketing function to a digital platform, its dependency on big data has become inevitable. Marketing teams of various companies analyze the trends prevalent in marketing using consumer data and come out with various new marketing campaigns to fill these gaps thus helping the businesses to meet and beat their targets. Companies are increasingly spending on mobile advertisements thus catering to a huge audience in a short period. These ads are individual-specific as Big Data Analytics Courses use the residing cookies in your system and display only those products and services which garner the attention of that particular individual.
Marketing and big data: a perfect blend
The advent of big data capturing the market, it has affected the marketing function drastically. It has made Marketing an interactive as well as a very insightful process. Marketers use tools like Google Analytics to know how their websites are performing and how many eyeballs their particular products are turning. Then accordingly they work on the marketing strategies of those range of products and services which are not performing well and also on those avenues which are outperforming to further increase revenues from them.
People spend long hours online thus making online marketing the only resort to reach the audience of this era. People are increasingly responding to online marketing campaigns thus bringing in more and more personal information into the picture. Big data helps in transforming these inputs into final sales and thus converting the desires of people into the business. The data collected in the online platforms will be the deciding factor for your marketing campaign’s success. Businesses will have no clue what’s going wrong in the absence of data.
Digital marketing and big data together have helped in improving the users’ product viewing experience. Marketing teams are constantly looking for new opportunities. Big data provides to be an effective tool in doing so. Also, it gives insights on when a company should pull the plug if something is working against its success. Using big data, every action can be tracked.
By reviewing data analytics, companies can find out how users perceive their business and its products. Using tracking codes, a lot of data can be collected and then segmented into various sections. Data can give minute details such as which ads are making the most revenues, which ads need improvements and which ads are working negatively. Also, companies need a record of conversions i.e. how many ad views are resulting in final purchases.
Conclusion
Big data is playing a major role in formulating marketing strategies. Data provides valuable insights that are further analyzed and developed into a strategy map on how the marketing function has to be taken up. Establishing concrete goals and measuring the fulfilment of such goals has become much easier with the use of big data.
For more details, you can also search for – Imarticus Learning and can drop your query by contacting through the Live Chat Support system or can even visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Bangalore, Hyderabad, Delhi and Gurgaon.