Businesses have realized the value of digital data for their operations as well as the fresh business prospects that data collection and analytics offer. They’ve also begun to investigate machine-learning applications that aid in the resolution of challenging issues that can be resolved through knowledge gleaned from vast amounts of data.

They have begun to comprehend and recognize the vital info that they have. To enhance their operations and company, they are gathering, analyzing, and exploiting this data.

The two most obvious approaches for businesses to monetize data are as follows:

1. Data is collected and analyzed for product development purposes, used to create better products, which are then to customers. This results in increased sales, products with higher added value or more closed deals.
2. Data is used to identify problems and bottlenecks in internal processes, which are then eliminated to improve business efficiency and profitability.

Companies must think outside the box and be innovative in their approach to the marketing of data. They often excel at the organized engineering-like data collection and analysis process, but fall short on the creative and business fronts. They fail to utilize and profit from all of the data they have.

The five motivational strategies for businesses to monetize their data are listed below, along with examples from real-world situations to show how they actually work.

1. Selling insights to customers

It makes sense to take already-existing data, aggregate and enhance it, and then sell it to clients as fresh, worthwhile insights. The price of the bundle that is sold to clients can be raised by bundling reports, online dashboards, and indices with the company’s current offering. Machine learning applications can be added to user interfaces to improve how customers engage with brands or get the services they need. New insights are developed “on-the-go” during the consumer experience.

Leading Finnish job portal Oikotie.fi has developed a clever strategy for turning its data into a profit. For its B2B clients, Oikotie.fi developed a solution that is chargeable: after making a payment, recruiters can view how their job postings compare to those of the other (anonymized) players in their sector. This information generates fresh money for the job board and aids B2B clients in optimizing their recruiting ads.

2. Empowering the sales force with data

A sales organization’s role in a company is to maximize sales. Data can be a highly effective tool in reaching that target. Smart companies empower their sales force with rich customer data sets that help them easily identify customer problems, potentially churning customers and sales leads. With data, the sales force can give better product presentations, improve customer service and use more tailored sales argumentation when meeting customers. Smart companies position themselves as outstanding data leaders, and sales people play an important role in delivering that message to customers.

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The Finnish elevator and escalator company Kone is a good example of a firm that has given its sales force access to valuable data. When a sales rep meets a customer who has a Kone elevator or escalator installed, he/she is well informed on the condition of the device, including any potential problems, and can help the customer extract the most value out of the device. Often this leads to additional maintenance services and/or spare parts sales.

3. Using data in marketing and advertising

Marketing and advertising strategies can be developed using information about consumers and their interests. You have two choices: either the business optimizes its own marketing and advertising using its data, or it sells its data to other businesses so they can do the same.

Retailers buy data from the search and discovery app Foursquare so they can tailor their outdoor and online marketing to correspond with the paths people take to go around the city. Media firms gather digital information on people’s interests, such as their love of food, sports, or fashion, and then sell it to internet marketers.

In order to maximize sales of umbrellas, swimwear, and winter jackets, apparel merchants can use weather forecasting services like Foreca to enhance their advertising. eCommerce businesses like Zalando gather information on abandoned shopping carts as part of their services and then target these potential customers’ Facebook feeds and other online channels with their own online advertising.

4. Selling data to players up and down the industry value chain

Companies often view their business as a “silo” and the data they have, they’ve derived from their own operations and own customer interactions. They use it for their own purposes only. The reality is that they’ve been operating in network environments and value chains where the final customer delivery is the result of the joint effort of several collaborative companies.

In recent years many companies have woken up to the fact that these networked business environments create opportunities for sharing and capitalizing data from company to company. Data can be an important asset in optimizing the operations and cooperation of the players in the value chain. Companies can monetize their data by selling it to their suppliers and vendors down the value chain or by selling it to retailers, resellers and other sales-related partners up the value chain – or both.

The provision of data from one player to another is typical in pharmaceutical value chains. Finnish pharmaceuticals distributor Tamro sells data on drug purchases by the Finnish people to local pharmacies, who can compare their own sales against their competitors and optimize their drug display and stock in stores. Tamro also sells data to drug manufacturers like GSK and Novartis, who then use the sales data to set prices for their products. Tamro very cleverly capitalizes its data both up (pharmacies) and down (drug manufacturers) the pharmaceuticals value chain.

5. Selling data to players outside your own industry

Examining opportunities outside the company’s sector or value chain can provide less obvious ways to monetize data. A number of participants might be eager to learn more about consumer trends, economic activity, or other pertinent subjects. Unexpected fields might contain these players. Companies should actively look for these participants and investigate creative joint venture opportunities.

Retail banks like Nordea and Danske Bank are now compelled to make their accounts and payment information available for use by others as a result of regulatory changes in the EU. Fintech, information services, telecoms companies, and others with the ability to develop new digital services based on consumer banking data now have access to attractive new business opportunities. Another such illustration comes from the sportswear sector, where businesses like Nike have begun to gather information from smart sports apparel.

They may start providing that information to insurance providers in the future, who can use it to forecast a customer’s demand for health insurance. A third example: export and import volumes of goods in harbors are a good early indicator of economic activity in a given country, so some harbor operators have started to sell that data to players who wish to forecast economic fluctuations, such as banks and financial institutions.

The use of data analysis is growing. Data analytics is being used by businesses of all sizes and in all sectors to analyse past transactions and events as well as to identify new patterns in user or customer behavior and make predictions about the future.

Technology devices are rapidly gathering data in real time, from physical sensors to cookies on online browsers and mobile phones, creating new potential for better informed, data-driven business decisions.

Many people who want to work in data analytics picture themselves in a full-time office job with all the benefits typical of IT businesses. Yet, there is a different option that offers more freedom and adaptability: independent data analysis. A freelancer is an independent contractor who contracts with businesses to provide their time and services. The demand for these positions is rising. Now, one-third of workers work as independent contractors, and nearly half of millennials do so.

How do Data Analysis Make Money?

Although data science and data analytics are frequently combined, they are fundamentally fairly different ideas. In order to handle and alter data, data scientists use a combination of programming and statistical analysis to create algorithms and predictive mathematical models. Data analytics entails statistical analysis of data sets to uncover insights that may be put into practice. This is frequently done to guide corporate decisions regarding marketing, pricing, sales, and product development.

Big data analysis is the term we use to describe the understanding of enormous and complicated data volumes that cannot be handled by conventional applications.

Freelance data analysis suggests that you are working for yourself as a sole proprietor, finding your own customers and projects, handling your own bookkeeping, marketing, and insurance needs, as well as controlling your own schedule, prices, and time management.

Becoming your own boss is possible with freelance data analysis. You can accept clients from anywhere that fit your project requirements and need work in your areas of expertise if you operate remotely. This can provide you more freedom to design a schedule that suits your needs and a better work-life balance.

The unpredictability of the workflow, the need to constantly market yourself and maintain a favorable online presence in order to attract job offers, the absence of employment benefits like health insurance and 401(k) contributions, and the social isolation are the challenges associated with freelancing.

Below are some other methods you can earn money with data analysis:

Content writing

You can definitely become a technical content writer if you are a data scientist with a flair for writing. You can utilize your data science expertise as a tool to produce quality articles on subjects relevant to data science, artificial intelligence, and machine learning. This position is currently in high demand.

Participating in competitions

The best course of action is to compete if you want to increase your knowledge in data science and develop your skills. They provide you the chance to study the finest practices in data science while also learning how to solve problems creatively and with a large group of individuals. Competitions also let you test the limits of data science and use your imagination to come up with novel solutions. In other words, they are the ideal way to acquire the ability to identify workable big data solutions, which will be very helpful in your data science employment.

Freelancing

Today, freelancing is a fantastic opportunity for data scientists of all experience levels. You have a lot of freedom to select your assignments, manage your time, and set your own rates when you work as a freelance data scientist. It is also a fantastic option for beginners who want to construct real-world projects to gain more hands-on experience.

Starting a YouTube channel

If there is something to learn, whether it be fundamentals or complex ideas, YouTube is a great resource. The YouTube channels you subscribe to can teach you a lot about data science. You can start a YouTube channel as a data scientist to get some additional money.

Blogging

Starting to write blogs and articles on online blogging platforms is one of the best ways to commercialize data science expertise. To continue using your data science talents, you have two options: start your own blog or write for websites that pay well. The blogging website will take some time to make money. Nevertheless, once they reach a certain point, the data scientist can start making money off each blog.

Affiliate marketing

By locating the most important users on social media and developing personalized marketing strategies for them, data science can be applied to affiliate marketing. That can most certainly be your side job if you are an expert in data science.

Ethical hacking

The practice of finding weaknesses in a system, application, or organization’s infrastructure that a hacker could use to take advantage of someone or something is known as ethical hacking. By lawfully breaking into the networks and searching for vulnerabilities, they employ this approach to stop cyberattacks and security breaches. You can work as an ethical hacker on the side as a data scientist.

Consultant

You can also apply your data science experience in consulting. You are available for consultation with businesses or employees regarding any issues they have with their data science activities. You can also make extra money thanks to it.

You can set your own pricing as a freelancer, but you should do it competitively in order to support your lifestyle and secure steady work. When setting a budget for a particular project, you might consider the market rate, what other freelancers are charging, and how long it will take you to finish the task.

There can be additional room for bargaining with some clients. Also, you should attempt to ascertain the client’s budget for a particular project because failing to do so could cost you possibilities.

The average data scientist pay, according to Glassdoor, is $113,000, with a range of $85,000 to $170,000. At the initial level, data analysts make between $50,000 and $75,000 per year. Salary ranges for big data engineers typically start at $70,000 and go as high as $165,000 for a subject matter expert. Top freelancers can eventually make more money on a monthly or annual basis than W2 employees.

Freelance data scientists on Upwork can expect to make anywhere between $36 and $200 per hour or $400 for a project. Ad hoc analyses and queries, data pipeline construction, and the development of recommendation engines are all common initiatives. You can charge more if you are proficient in more specialized languages like Scala, Spark, and Hadoop.

As you gain knowledge, experience, and more specific talents, your rates should rise.

For more complicated services like geospatial data analysis with visualization and machine learning/deep learning with computer vision, prices on Fiverr start at $5 to $10 and can reach up to $200.

How Should You Get Started in Data Analysis?

First, create an online portfolio and marketing platform in order to attract freelance data analysis work. Update your LinkedIn profile, and request recommendations from former employers and coworkers to help you stand out from the competition. Making your own website with a unique name so you can include previous clients, projects, and recommendations can be helpful.

To start looking for jobs, you’ll probably need to create an account with one or more freelance platforms.

A larger selection of assignments are available at competitive pricing on Fiverr, a freelance services marketplace. On Fiverr, you may offer a gig at a specific price and see who needs your talents without having to look through job descriptions. Data source connectivity, model documentation, model validation/testing, interactive/animated visualizations, online embedding, and toolbar integration are among the services that many freelance data analysts on Fiverr offer.

One of the most well-known freelance marketplaces, Upwork, has more than 2,000 listings for data scientists and data analysts. You might start by submitting an online cover letter and portfolio to various job openings. As you expand your client and experience list, your search ranking will then improve.

Freelancers now have a harder time getting their initial applications to work on Upwork approved. Increase the number of work subcategories you select, the number of talents you mention, the amount of specificity in your job title, the depth of your education and work experience, and the number of samples of prior work you include in your portfolio to increase your chances of being hired.

The website Toptal takes pleasure in connecting top freelancers with household names like Airbnb, Zendesk, and Pfizer. It might be preferable to develop your skill set on another site first since Toptal primarily hires freelancers with substantial experience. In-depth skill reviews, live screening, test projects, and language, personality, and communication interviews are all part of the Toptal screening procedure.

A live interview, a test project, and a 15-minute English exam are all part of Coding Ninjas’ methodical selection process. Based on skill set, availability, and project length, Coding Ninjas matches developers, data scientists, and designers to projects; they do not take a percentage of payments made to freelancers.

The IT and startup communities are the target audience for AngelList. You can go through various contract work and remote employment openings. You can link to previous jobs, projects, and businesses that you’ve worked on through the numerous personal website presences shown in your AngelList profile. The recruiting managers will then receive applications and messages directly from you. Also, AngelList offers details on various company contacts you might contact to make your own project.

An online group dedicated to data science is called Kaggle. You can enter many contests for a chance to gain credibility and cash awards, from earthquake prediction to audio tagging for soundbites.

Furthermore, there are numerous Facebook groups available now for remote workers, freelancers, and digital nomads. You may keep up with fresh prospects by joining online communities and reading recent posts from members. While other job-posting networks require a fee, Facebook is frequently a less expensive option for small firms to find talent. Also, direct communications allow you to communicate immediately.

Personal branding can be aided by beginning a blog on data analytics and engaging in online forums after you have chosen a few platforms and uploaded your profile. Also, you can network with other independent data analysts who could introduce you to business prospects or offer you guidance and emotional support.

When interacting with clients, take the time to carefully write each proposal and demonstrate how your knowledge and experience are a good fit for their requirements. It’s a competitive procedure, so you’ll need glowing testimonials from your first few customers to boost your ranking in search results and establish more authority.

To let businesses know about your services, you might also wish to create leads and send prospecting emails to companies both inside and outside of your current network. Small to midsize enterprises, or SMBs, that require data for marketing and growth objectives but may not have the budget to hire top data analysts internally, represent one of the most popular markets for freelance data analysts.

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You should probably check off a few items before resigning from your day job, like setting up a cozy home office, hiring an accountant, and saving money for a few months’ worth of living expenses in case productivity is slower than anticipated. Many independent contractors agree that filing taxes is far more difficult, so if you want to work with clients in other countries, you should probably get expert assistance.

In order to have a feel for the pace of work, the frequency of job proposals, and your own abilities at autonomous time management, you can sign up for a freelance platform and take on a few side tasks during the evenings or on the weekends.

Some Skills to Pay Attention to?

Typically, you will need to be familiar with at least one data processing language, such as SQL, Tableau, or Excel. An analytical mindset, math and statistics proficiency, business acumen, domain understanding, and data visualization abilities are additional broad knowledge and abilities needed to thrive in data analysis.

Data analysts who operate for themselves must have entrepreneurial abilities in addition to a broad understanding of data analysis, including marketing, personal branding, social networking, lead generation, sales, content authoring, bookkeeping, and budgeting.

A data science-specific degree is no longer required to succeed in the modern world. Self-learners have access to a wealth of free resources. The data analysis learning route offered by Springboard is suitable for those with little to no prior programming or data analysis experience and includes exploratory and predictive statistics, basic Python computer programming, algorithms, R for statistical analysis, Unix, and Git.

Additionally, there are more formal data science bootcamps that provide an all-encompassing data analytics education, both offline and online.