Mediastreet
Tag

Data Analytics

Browsing
In News

10 High-Paying Jobs That Require a Knowledge of Data Analytics

No Comments

By Charlie Custer

There’s never been a better time to learn data analytics and enter the workforce as a data scientist. The job landscape is promising, opportunities span multiple industries and the nature of the job often allows for remote work flexibility and even self-employment.

Plus, many data analytics experts boast a high median salary, even at entry-level positions.

With technology reaching new heights and a majority of the population having access to an internet connection, there’s no denying that Big Data and data analytics have become hot topics in recent years – and a growing need. According to IBM, the number of jobs for data professionals in the U.S will increase to 2,720,000 by 2020.

Demand for knowledgeable data analytics professionals currently outweighs the supply, meaning that companies are willing to pay a premium to fill their open job positions.

But the skillset and job opportunities within data science go beyond the tech and digital spaces. Let’s take a look at what you need to know as a data scientist – and what you’ll learn when you take our courses.

What Skills Are Required for a Job in Data Analytics?

As you delve into the 10 jobs we have here and start applying for positions in the data analytics field, you’ll notice many of them require the same foundational skills. Make sure you’ve mastered these before you start sending your cover letter and portfolio to potential employers.

And, if you find a skill that you still need to learn, remember that you can take an affordable, self-paced data science course that will help you learn everything you need to know for a successful career in data science.

Python

Python is currently one of the most commonly used programming languages.

Having a solid understanding of how to use Python for data analytics will probably be required for many roles. Even if it’s not a required skill, knowing and understanding Python will give you an upper hand when showing future employers the value that you can bring to their companies.

If you’re ready to advance your programming language proficiency, learn how to manipulate and analyze data, understand the concept of web scraping and data collection, and start building web applications, consider enrolling in our Python for Data Science: Fundamentals Course.

SQL (Structured Query Language)

Working with data sources is a necessary aspect of data analytics.

Early in your career, you’ll need at least a basic understanding of SQL. SQL (pronounced sequel) is often a major component of these positions. When you go to interview, listen for hiring managers’ mentions of this programming language when asking about your work with databases.

The experience you’ll get in our SQL courses will give you a good foundation. Like Python, SQL is a relatively easy language to start learning. Even if you are just getting started, a little SQL experience goes a long way.

Knowing the basics of SQL will give you the confidence to navigate large databases, and to obtain and work with the data you need for your projects. You can always seek out opportunities to continue learning once you get your first job.

Data Visualization Skills

Knowing how to visualize data and communicate results is a huge competitive edge for job seekers.

On the job market, these skillsets have high demand (and high pay)! Regardless of the career path you’re looking into, being able to visualize and communicate insights related to your company’s services and bottom line is a valuable skillset that will turn the heads of employers.

In this way, data scientists are a bit like data translators for other people in the organization that aren’t sure what conclusions to draw from their datasets.

At Dataquest, students are equipped with specific knowledge and skills for data visualization in Python and R using data science and visualization libraries.

10 Jobs that Require a Knowledge of Data Analytics

Before you take the time to learn a new skillset, you’ll likely be curious about the earning potential of related positions. Knowing how your new skills will be rewarded gives you the proper motivation and context for learning.

Lots of employers are hiring for these positions, both remote and onsite, worldwide. Here are a few positions worth looking into – and their median incomes, according to popular job search websites.

1. IT Systems Analyst

Systems analysts use and design systems to solve problems in information technology.

The required level of technical expertise varies in these positions, and that creates opportunities for specialization by industry and personal interests. Some systems analysts use existing third-party tools to test software within a company, while others develop new. proprietary tools from their understanding of data analytics and the business itself.

A typical systems analyst in the US makes around $68,807.

2. Healthcare Data Analyst

Healthcare data analysts have the opportunity to improve the quality of life for many people by helping doctors and scientists find answers to the questions and problems they encounter on a daily basis.

The amount of data coming from the healthcare industry is growing rapidly, be it with the increased popularity of wearables like Apple Watch or through enhanced medical testing in clinics, hospitals and labs. Plus, with a rise in regulations and restrictions on how that data can be stored, retrieved, and processed, demand for proficient data analysts is on the rise as well.

The median salary for a Healthcare Data Analyst is $61,438.

3. Operations Analyst

Operations analysts are usually found internally at large companies, but may also work as consultants.

Operations analysts focus on the internal processes of a business. This can include internal reporting systems, product manufacturing and distribution, and the general streamlining of business operations.

It’s more important for professionals in these roles to have general business savvy, and they often have technical knowledge of the systems they’re working with. Operations analysts are found in every type of business, from large grocery chains, to postal service providers, to the military and can make upwards of $75,000 annually. Due to the versatile nature of this data analytics job and the many industries you may find employment in, the salary can vary widely.

4. Data Scientist

Much like analysts in other roles, data scientists collect and analyze data and communicate actionable insights. Data scientists are often a technical step above of data analysts, though. They are the ones who are able to understand data from a more informed perspective to help make predictions. These positions require a strong knowledge of data analytics including software tools, programming languages like Python or R, and data visualization skills to better communicate findings.

These positions are challenging – and rewarding, with an average salary of $91,494. The demand for data analytics experts with technical backgrounds is at an all-time high.

Dataquest has multiple learning paths that are tailored to provide you with everything you need to hone your technical skills, including the Data Scientist Path that will help you become a certified data scientist.

5. Data Engineer

Data engineers often focus on larger datasets and are tasked with optimizing the infrastructure surrounding different data analytics processes.

For example, a data engineer might focus on the process of capturing data to make an acquisition pipeline more efficient. They may also need to upgrade a database infrastructure for faster queries. These high-level data analytics professionals are also well-paid, with median salaries being comparable to data scientists at $90,963.

6. Quantitative Analyst

A quantitative analyst is another highly sought-after professional, especially in financial firms. Quantitative analysts use data analytics to seek out potential financial investment opportunities or risk management problems.

The median salary for quantitative analysts is $82,879. They may also venture out on their own, creating trading models to predict the prices of stocks, commodities, exchange rates, etc. Some analysts in this industry even go on to open their own firms.

7. Data Analytics Consultant

Like many of these positions, the primary role of an analytics consultant is to deliver insights to a company to help their business. While an analytics consultant may specialize in any particular industry or area of research, the difference between a consultant and an in-house data scientist or data analyst is that a consultant may work for different companies in a shorter period of time.

They may also be working for more than one company at a time, focusing on particular projects with clear start and end dates.

These positions are best for those who like change, and those who have a narrowed interest and expertise in a field of study. Analytics consultants are also in a great position to work remotely, another enticing factor about this role to consider.

Compensation varies widely by industry, but $78,264 is a representative salary for the role.

8. Digital Marketing Manager

Digital marketing also requires a strong knowledge of data analytics. Depending on your other complementary skills and interests, you could find yourself in a specific analytics role within a company or agency, or simply applying your data science expertise as a part of a larger skillset.

Marketers often use tools like Google Analytics, custom reporting tools and other third party sites to analyze traffic from websites and social media advertisements. While these examples require a basic understanding of data analytics, a skilled data scientist has the ability to create a long-term career in marketing.

A lot of money could be wasted on campaigns that do not drive traffic, so marketing professionals will continue to need analysts to make smart decisions about how to leverage existing resources.

While digital marketing positions have a wide range, the higher end salary for an advanced digital marketing manager is $97,000.

9. Project Manager

Project managers use analytics tools to keep track of a team’s progress, track their efficiency, and increase productivity by changing processes.

Project managers need at least a working understanding of data analytics, and often more.

These positions are found internally at large corporations, and frequently in management consulting. Another example of a career trajectory for project managers could be moving into product and supply chain management, which companies rely on to maintain profit margins and smooth operations.

A typical salary for a project manager is around $73,247.

10. Transportation Logistics Specialist

A transportation logistics specialist optimizes transportation of physical goods, and could be found in large shipping companies, like Amazon, UPS, naval transport companies, airlines and city planning offices.

A data analytics background is especially helpful in this job because transportation logistics specialists need to reliably identify the most efficient paths for products and services to be delivered. They must look at large amounts of data to help identify and eliminate bottlenecks in transit, be it on land, sea or in the air.

With seasoned professionals in this industry making around $79,000 per year, a transportation logistics specialist is an appealing career path for individuals who are detail-oriented, technical and forward thinkers.

A data analytics background also helps transportation logistics specialists, among others, to focus on the most important issues, seeing potential problems and solutions in context and communicating those effectively.

Data Analytics Opportunities Around The Globe

These are just a few of the many high-paying jobs which require knowledge of data analytics. Specific figures from this article are for the median salaries in the United States, all cities included.

Salaries in each city may vary and reflect local demand and general cost-of-living expenses. Boston, Portland, and Denver, for example, have become hotspots for data analytics positions.

While the numbers included in this article represent a typical salary in the United States, opportunities for data analytics professionals can be found all around the globe. Many of them can even be done remotely, allowing you the highly-desired opportunity to work from anywhere in the world on a competitive US salary.

Whether your goal is to get a full-time job in a new industry, advance in your existing career, or work for yourself in the data analytics field, Dataquest can prepare you for the opportunity. With the portfolio-building missions and projects in Dataquest’s Data Analyst path, a community of mentors, and a strong alumni network, you’ll have all you need to become a certified data analyst and be set up to get the job of your dreams.

By Charlie Custer

Sourced from DATAQUEST

Read More
By
In News

5 ways data and text analytics improve customer retention

No Comments

By  Agi Marx

Customer retention strategies fuelled by data ultimately influence how your team will approach customers — it’s proven to drive profit. In fact, “executive teams that make extensive use of customer data analytics across all business decisions see a 126% profit improvement over companies that don’t” (McKinsey, 2014).

This is no news. Among 334 executives surveyed by Bain, more than two-thirds said that their companies are investing in data and analytics. And the expectations are high. 40% expect to see “significantly positive” returns, with another 8% predicting “transformational” results (Bain & Co, 2017).

While the intention is there, according to Forrester, “only 15% of senior leaders actually use customer data consistently to inform business decisions” (“The B2B Marketers Guide to Benchmarking Customer Maturity”, Forrester, 2017). So, companies do realize the need for data but expect some sort of magic to happen in order to implement?

“Influencing customer loyalty […] doesn’t require magic, it requires data – usually data that you already have but aren’t using to full advantage. Regardless of industry, most organizations today generate mountains of data. In fact, many customers tell me that they have so much data that their biggest problem is how to manage all the data they have”, says Mike Flannagan, vice president, and general manager of Cisco.

5 Ways Data and Text Analytics Improve Customer Retention

1. Develop a data roadmap and stick to it

As many as 30% of the executives in the aforementioned Bain & Co study said that they lack a clear strategy for embedding data and analytics in their companies. McKinsey’s findings show that taking an integrative approach, meaning seeing analytics as a strategic driver of growth instead of using it in a silo or only as a part of IT, ultimately leads to achieving the desired result (McKinsey, 2014).

Successful companies do two things differently: First, they make use of the data they have. Second, they implement the organizational changes once they understand what the data tells them. So, you have the data – make sure you actually use it and enforce any changes needed in the business to make it happen quickly.

A good approach is to develop a data roadmap and stick to it. Steps that you take within the organization can be to:

  1. Ensure corporate KPIs are automated, scalable and repeatable.
  2. Gather key stakeholders and define the top 3 business problems you want to solve.
  3. Categorize the issues into data vs. systems issues (often you’ll find that the issue is not with “data” at all, but with how people use it or manage it).
  4. Prioritization of tasks is required along with assessing the technical feasibility of your plan.
  5. To stay on track, reassess progress every 3 months.
  6. The human factor – ensure behavioral change

Another key factor is hiring senior executives who take a hands-on approach to customer analytics. Not only do they need to understand the importance of analytics but also have the skills to analyze it themselves, so use this as a benchmark when hiring.

Although 70% of companies have data strategies in place, many will fail to deliver what’s needed due to one factor alone: people. You may have the most advanced tools and excellent data scientists; however, all efforts fail without the correct behavioral changes needed internally to ultimately take action (Bain & Co 2017).

Employees may not be committed to using data analytics, internal teams may not be communicating with each other, or the data solutions adopted aren’t user-friendly. Behavioral change, continuous monitoring of results, along with a “one-team approach” is needed to ensure that advanced analytics within an organization can survive and prosper (Bain & Co, 2017). No surprises here, behavior change being the hardest part of any performance improvement plan and why as many as 38% change efforts fail (Bain & Co, 2016).

2. Only focus on high-quality leads

Customers are less likely to churn if they are similar to your primary target customers. If you have access to data about both your customers and a list of potential customers, this is a great opportunity to focus on only those who are less likely to churn.

How? By applying algorithms comparing the features and characteristics of your customers to those of your potential customers. Those that have similar characteristics (FTE size, annual spend, job title, type of industry) to your existing customers are probably those most likely to want your product, to find it valuable and therefore stick around. Your segmentation now becomes crucial. Each customer segment provides you with distinct features that help easily identify your next customers.

For example, tools like HubSpot provide this type of information in an integrated way, where you can see characteristics and patterns easily.

3. Use machine learning methods to create predictive models

Companies analyze data using different types of analytics, including predictive analytics, which is used to look at the relationships among different metrics.

To create solid customer retention strategies, we can use predictive analytics to make predictions about the future, by looking at historical data, to learn what customers may like or dislike.

Often, you might be overwhelmed by the number of variables you have to manage and analyze all at once. Although you may have a highly skilled data analyst at hand, it’s still time-consuming and labor-intensive to manually and quickly sift through the sheer volume of data to find the optimal predictive model.

To create the best predictive models of retention, rely on the power of machine learning to quickly and accurately uncover the underlying reasons why customers are churning or why they’re loyal to your brand.

Machine learning uses math, statistics and probability to find connections among variables that help optimize important outcomes such as retention. These models are then applied to new customer data to make predictions.

Machine learning algorithms are iterative and learn on a continual basis. The more data they ingest, the better they get. Compared to human performance, they can deliver insights quickly thanks to the processing capability of today.

For example, you can use analytics to identify which up-sell or cross-sell products will be the most relevant based on your customer’s past purchase or browsing history.

Often, companies don’t have employees with high-level analytics (data science) skills. Third party providers can provide a solution that automates data integration and analysis.

4. Get data-driven insights with text analytics

To get deep, data-driven insights, don’t forget to analyze your free-text responses to your open-ended survey questions. If you don’t you may well miss them!

You can do this with text analytics solutions. With a text analytics tool that uses sentiment analysis, it’s easy to spot customer pain points.

And, if you collect lots of data, make sure you actually use it. One study found that only 15% of senior leaders actually use customer data consistently to inform business decisions (Harvard Business Review).

At Thematic, we have developed an AI algorithm that automates analyzing free-text feedback in surveys using machine learning and natural language processing, and in essence, simplified the way businesses are getting insight from their customer data.

5. Segment to focus on retaining the right customers

Using data analytics to segment people into different groups means you can identify how each segment engages with your brand and product. This then allows you to look at each subgroup and draw insights, followed by adopting different communication and servicing strategies to increase retention of your most wanted customers.

Analyze data such as your customer demographics, lifestyle, products purchased by each category and type of customer, the frequency of purchase and purchase value. In this way, you’ll discover which type of customers are driving the most revenue. Some cost too much to deliver revenue, so you’ll know if you want to focus your efforts on.

Understanding the difference between these types of customers, can in some cases make or break a business, especially if you’re just starting out. Knowing customer value is crucial to be able to make critical decisions. You can segment by historical value, lifetime value, value over the next year or the average customer value by segment. Using the right segmentation, you’ll then create highly targeted product recommendation offers. Segment your customers to offer relevant discounts for different channels (in-store, online, mobile). Mix it up a bit, every customer doesn’t have to receive the same offer.

Another useful way to use segmentation is to monitor the time-sensitivity and seasonality of your promotional codes. By monitoring sales data, you can see whether these codes are redeemed more often in the morning or afternoons or perhaps straight after a sales communication. The more you know about what a demographic responds to, the more you can focus on taking the right actions.

Top 3 Tips for Analysis

Gather multiple data points to be able to make relevant recommendations.

Be pragmatic and avoid making assumptions from solely one piece of data. Because someone living in California buys winter boots doesn’t mean they want to be bombarded with similar product suggestions. Maybe they bought them for their sister who lives in Chicago!

Leverage social proof where you can.

If your customers don’t respond to certain products, maybe all they need is a little reminder that others similar to them are using them and are happy with them. Pull in positive testimonials from surveys and social media comments to your marketing communications and website.

Remember: it’s the ability to swiftly translate insightful data into concrete action that counts.

It’s a fact: better data means better results. If you don’t have good data now, you can test your way to better data. Just by improving your internal data collection, you can often arrive at better data. In other cases, you might have to purchase better data. Good data is not static, it’s a continual process of observing, acting and learning.

Finally, the challenge of the vast data volume that large businesses have, is also the opportunity. Bringing together structured and unstructured historical data across organizational silos, and combining it with key data about ongoing customer interaction provides a compelling opportunity to influence customer experience in real time.

This article was published here first.

By  Agi Marx

Sourced from Digital Doughnut

Read More
By
In News

Data Analytics Most Instrumental in Enhancing Customer Experience and Lowering Risk for Organizations

No Comments

Sourced from BW CI World

Innovating new business models and maximizing revenue and profits are the next set of priorities for data analytics

Infosys published a global research on data analytics from the Infosys Knowledge Institute. The survey titled, ‘Endless possibilities with data: Navigate from now to your next’, reveals that a majority of organizations are deploying analytics to enhance customer experiences and mitigate risk.

This research tries to understand how data analytics is becoming core to driving digital transformation for enterprises and makes an assessment of enterprise expectations in a world of endless possibilities with data. It also explores a range of challenges, opportunities, and the role of new technologies in the analytics world.

Highlights of the survey
* 31 percent of respondents identified the use of analytics with experience enhancement. This includes using intelligence generated by listening to internal and external stakeholders to drive extreme personalization and high quality customer service

* 28 percent respondents were interested in leveraging analytics for risk mitigation – predicting risk to enable better decision making, and detecting anomalies that could disrupt business effectiveness.

* Developing new business models by unearthing the latent needs of customers and offering innovative products and services was seen as the primary analytics requirement of 23 percent of respondents.

* Revenue and profit maximization through increasing channel effectiveness, and thereby, enhancing profitability across processes, channels and stakeholder ecosystems was the analytics priority for the remaining 18 percent.

* The majority of respondents in the U.S. (32 percent) and Europe (34 percent) stated they would like to use analytics for experience enhancement whereas in ANZ about 31 percent respondents consider it for risk mitigation.

Functions across organization are benefiting from the possibilities of data. Finance and accounting was found to use analytics the most at 32 percent, followed by marketing and operations at 20 percent and 17 percent, respectively. In terms of the emerging technologies, Artificial intelligence was perceived to deliver increased outcomes when combined with analytics at 37 percent followed by IoT and Cloud Technologies at 19 percent and 16 percent, respectively.

The survey found that enterprises in every industry encountered several challenges that prevented them from implementing their analytics initiatives fully. The biggest challenges stemmed from a lack of expertise in integrating multiple datasets (44 percent of respondents) and failure of understanding in deploying the right analysis techniques (43 percent).

This is where enterprises are looking up to their partners to help industrialize their analytics capabilities by creating an analytics strategy, build an operational framework, and define a process for executing and governing analytics initiatives.

Sourced from BW CI World

Read More
By