Business intelligence analysts are tasked with the design, development, and implementation of data. They use extraction, transformation, and loading processes to take various data types and turn them into something usable. Overall, they direct data flow and organization into their respective stores and data warehouses, which informs marketing.
Business intelligence analysts generally work on diverse, multidisciplinary teams. They transform business requirements into technical specifications that are used to store, drive, and warehouse data, as well as build effective access tools for non-technical staff. The use of best practices in ETL (extract, transform, load) functions and the ability to operate and manage a data warehouse are important to this position. While there is overlap with a career in data engineering, a role in BI will likely involve training teams on how to use platforms for analytics and reporting. Skills in SQL, cloud computing, databasing, programming, and computer science engineering are recommended.
PayScale reports that business intelligence analysts make an average annual wage of $67,484. Candidates for these positions are advised to have a four-year degree.
Data engineers work to provide a sturdy, reliable infrastructure for storing and accessing big data sets. Their general responsibilities include the collecting, modeling, movement, digital warehousing, storage, and preparation of data. They must also understand distributed systems, data structures, and algorithms.
In the early days, most data engineering was done via SQL, but these days, more is required of the expert data engineer. An extensive knowledge of Python, cloud platforms (e.g., Amazon Web Services, Google Cloud), Scala, Java, NoSQL and SQL databasing, and the construction of centralized repositories of data is necessary. Depending on the volume of big data, candidates’ grasp of query languages may vary. For example, data engineers at larger companies like Facebook or Dropbox are required to have experience with Hadoop, Kafkar, and/or Spark.
Marketing analytics tools help to collate and make sense of these huge amounts of data, which are often on different kinds of databases and in different forms, making their analysis difficult. A background in development and computer science is useful. Also, skills in data visualization and representation, statistical and quantitative analysis, and basic understanding of content management systems and SQL are necessary, as are standard ETL best practices.
In terms of big data and data analytics upskilling, Coursera offers a well-reviewed step-by-step program from novice to professional data engineer, and Udacity hosts a data engineering nanodegree. A business such as McKinsey Analytics hires data engineers to build what they call “mission-critical” software and back end architecture to facilitate the gathering of large data sets for clients—all of which are used to inform marketing.
A digital marketing analyst is responsible for analyzing data and discovering new means of improving a company’s online presence via its social, internet, and media presences. They work closely with digital marketers of all kinds to collaborate on best practices for online branding such as through Google Ads, sponsored or reposted content, PPC, banner ads, marketing chatbots, and more.
Digital marketing analysts help businesses make sense of large sets of actionable data. They also assist companies in coming to the best decision as to how data can be used for the improvement of an online branding strategy. Proficiency in programs like Excel is critical, as digital marketing analysts must keep a close eye on KPIs (key performance indicators). These are points in the data set that can show a relationship between one type of marketing activity and an uptick in engagement, often useful when helping companies make the kinds of decisions necessary to running an online business.
The field of market research analysis, of which digital marketing is a part, is expected to grow by a rate of 20 percent between 2018 and 2028. After pursuing an undergraduate degree in business, economics, marketing, or a related field, one can consider Emerson College’s master’s in digital marketing and campaigns. Students take advanced coursework in data analytics, social media marketing theory, big data, and other key digital marketing concepts. This training and preparation can appeal to companies like Blue Corona, which offers a wide variety of digital marketing services for businesses at all levels.
As society trends toward an ever-growing automated infrastructure, machine learning researchers are required to deploy their advanced knowledge of algorithmic, computational, and statistical learning theories. These systems can be deployed to help companies learn about the effectiveness of their marketing and match prospective customers to the right messaging.
PayScale reports that a machine learning researchers make an average wage of $112,500, and rightly so, because the job involves an incredibly intricate understanding of classic machine learning problems, data analytics, machine learning algorithms, and computer science. Some of the duties of machine learning researchers include tackling data challenges in the fields of economics, biology, health administration, public administration, and more to determine how AI and ML can streamline data management in those professional contexts.
The constant adaptation and guided growth of established algorithms and their repurposing for a variety of different contexts, reexamining classic limitations of machine learning, including what is knowable, what is learnable, and what cannot be taught. A background in statistical learning theories, algorithmic learning theories, and computational learning theories is important for professionals in this field. A deep understanding of learning principles and their expressions and functions in programming is critical, as well.
Columbia University’s renowned master’s program in computer science with a concentration in machine learning is a prime choice for those with an undergraduate background in engineering, mathematics, or information technology. Companies like Alesco Data utilize machine learning researchers to improve algorithms and machine learning methods with the goal of increasing the efficacy of critical AI systems that utilize large data sets. Skills in the areas of cloud computing, databasing, ETL, SQL, and Python, as well as a graduate degree, at a minimum, are generally what are required of machine learning researchers. Earning a PhD in a related field may allow professionals to work as machine learning scientists or theorists.
Social media analysts are responsible for all things existing on the social media end of a business’s activity. Their duties include maintaining, researching, and creating a company’s presence on social media sites such as Facebook, Instagram, Twitter, Digg, Flickr, Snapchat, and more.
The purpose of social media analysis is to determine how to grow a company’s audience by keeping tabs on how, why, and when users engage with the company’s app or website. To do this, they use social media analytics tools or third parties to design a marketing plan that integrates brand and audience identity. They often work closely with public relations professionals to workshop ways to approach their target audience and may find themselves in roles that involve audience sampling and study.
Social media analysts may also be expected to write reports for presentation to colleagues or clients, detailing various social media marketing strategies and their effectiveness. Open lines of communication between social media analysts and the customers they serve is critical. Skills in data visualization and representation, statistical and quantitative analysis, and basic understanding of content management systems and/or SQL are necessary.
PayScale salary data from 296 professionals who currently work as social media analysts and discovered that they make an average annual wage of $49,394. Candidates should consider a four-year degree. Southern New Hampshire University hosts a totally online BS in marketing with a focus in social media marketing that may be ideal for those looking to earn a credential as a social media analyst.
While there are dozens of companies that offer tools for social media marketing and analysis, there will always be those entities that continually innovate. For example, Brandwatch offers data analytics services and a variety of flexible, easily-integrated services (e.g., user activity intelligence, content strategy, etc.) for digital and social media assets. Also, Sprout Social boasts an impressive client portfolio of 20,000 brands and uses a ground-up platform integrating analytics from across each of a company’s commercial social media channels.
By reading a select number of engineering blogs, university students can gain access to the thoughts of some of the best engineers in the world, and get on the path to becoming one themselves.
Diversity and inclusivity aren’t purely idealistic goals. A growing body of research shows that greater diversity, particularly within executive teams, is closely correlated with greater profitability. Today’s businesses are highly incentivized to identify a diverse pool of top talent, but they’ve still struggled to achieve it. Recent advances in AI could help.
The ability of a computer to learn and problem solve (i.e., machine learning) is what makes AI different from any other major technological advances we’ve seen in the last century. More than simply assisting people with tasks, AI allows the technology to take the reins and improve processes without any help from humans.
This guide, intended for students and working professionals interested in entering the nascent field of automotive cybersecurity, describes some of the challenges involved in securing web-enabled vehicles, and features a growing number of university programs, companies, and people who are rising to meet those challenges.
Unlike fungible items, which are interchangeable and can be exchanged like-for-like, non-fungible tokens (NFTs) are verifiably unique. Broadly speaking, NFTs take what amounts to a cryptographic signature, ascribe it to a particular digital asset, and then log it on a blockchain’s distributed ledger.