The data market is on fire. We have never produced such a high volume of data before. According to estimates published by Solink, Google processes every day at least 20 petabytes and large stores, such as Walmart, create 2.2 petabytes at their POS systems in the same time frame.

Unstructured data, such as videos, are the ones we create the fastest. On YouTube alone, 0.023 petabytes are uploaded daily, but movies made by other devices, such as Internet-connected security cameras, make up the bulk of the data generated in the same time frame.

The 413 petabytes of generated data are not as well exploited as they should be to yield detailed insights for companies, such as how often customers entered a store or what products caught their attention.

In this article, we’ll explain a little better how business are impacted by data, which tools are used to deal with them, and what the key trends for the next few years are. Curious? So let’s go!

The importance of data analysis for business

With more than 3.7 billion users on the Internet, a growth of 7.5% over the 2016 numbers, and 40,000 Google searches every second, it is easy to understand where all the data we mentioned in the introduction come from.

Interactions with users, whether in social networks, in-store financial transactions, or in capturing photos, audios and videos, help create a tangle of information that helps businesses enrich the offers to their customers.

It is no wonder that most brands, whether large- or medium-sized, are concerned with finding cost-effective and viable strategies to be able to extract as much value as possible from that data. The good news is that we already have the technology for that.

Business Intelligence — a resource for extracting patterns from data sets — is one of the main solutions currently used to deal with data within the business. Big Data, Business Analytics, and so many other resources try to find a way to get this data organized and intelligible. Much has already been done, but there is still plenty to do.

Organizations use data for everything. They look at them to weigh how much they have earned in the past, and to predict how much they will earn in the future. They also use data to get to know the customer better and understand what he or she wants. Also, data is applied daily for answers to your problems.

The big challenge is to distinguish which of these data are relevant for operations. Of all those petabytes of information generated per day, only a few are pertinent to each business model, and the data scientist is the professional responsible for determining which ones are best suited for your company’s routine.

Causing impact similar to that of the discovery of the steam engine, data has become the engine of income of countless companies. They simplify processes, optimize operations, and generate intelligence so that entrepreneurs can achieve their goals.

In the following list, we select some of the most common data applications in companies and elucidate their impacts.

Acquiring and retaining customers

Every company depends on its consumers and therefore has a lot to gain from using solutions like Big Data Analytics to increase customer acquisition. Establishing a loyal public to the products you market can be very difficult and keeping them can be even more challenging.

You must always be ahead of the competition. To learn what your audience is looking for, what kind of interests it has, and what might motivate people to interact even more with an organization, data is of great help.

Big Data Analytics enables companies to look at various patterns and behavioral trends of their customers by looking only at the data they have available. This analysis generates enough information to understand which parts of behavior influence loyalty, what each consumer buys, and what modifications can be made to encourage such behavior.

Delivering what the customer expects of their products and services has always been something far away from the entrepreneurs, at least using the more traditional methods. With Big Data and the help of data scientists, this is one step away and can directly impact the acquisition and loyalty.

Want an example of how this is applied in the day to day of major brands? Coca-Cola uses a data strategy to keep its consumers loyal. Each time they share branded information on the Internet, the company analyzes that data and keeps in touch with customers about their feedback.

Contributions are used in creating ads that directly reach people interested in branded products and the company can understand which events to sponsor, which TV shows to present their ads in, and which Internet channels are most profitable.

Determining risks

Running a business is taking risks, but we rely on techniques that allow us to infer what those risks are and to find out how to mitigate them. Data analysis is used daily by financials to determine what credit options are advantageous to your customers and do not negatively impact your balance spreadsheets.

But risk analysis is not just about understanding a consumer’s profile and knowing how much he or she can spend. Algorithmic models play an important role in the evaluation of all business decisions. They calculate how each of the possible reactions of an organization will give results and indicate which one is more interesting for the entrepreneur, reducing the risks and increasing the certainties.

Digital security companies are some of those who have gone ahead in using data to restrain risks. They use the information they have to understand a customer’s buying and consumption pattern and preventing frauds. With high accuracy rates, they can determine if a card owner is the person making a purchase on the Internet at two o’clock in the morning or not based on past actions.

Innovating with Big Data

Another strength of profitable and enduring companies is their ability to innovate. Not so long ago, the process of creating new solutions was quite costly, no matter how much expertise an enterprise had. This is because there is always a great risk of misunderstanding the demand for a particular product or service and spending too much time creating a minimum viable product and losing space for a competitor.

Nowadays, no innovation company works without knowing the terrain very well. They fully understand the needs of customers, what they think about previous products, what problems they face, and how they could be better served. Innovating ceased to be a shot in the dark, even for startups that have now launched in the market.

Large organizations, however, know how to use technology to innovate like no other company. Recently, Amazon entered the food market by applying customer data and cutting-edge logistics to, in partnership with Whole Foods, efficiently serve a growing number of people who wish to live a healthier life but prefer shopping in the digital environment.

The main tools used by data scientists

The data scientist has become the most valuable professional on the market in recent years. The career, cited as the best job to have in 2017, with high earnings since the first year on the job, remains a great choice for those who want to enter the market. There are thousands of positions open to these workers in the United States alone, and the tendency is for them to be the protagonists of the digital transformation.

Whoever wants to join the field or to just know about the subject a little better needs to become familiar with the tools that the data scientist uses every day. Here are some of them.

SQL Server

SQL Server is one of the leading platforms of data scientists. It is the tool they rely on to organize databases and pull specific information for analysis and modeling. Among the many database options available to professionals, this is preferred because it is the basis of many Big Data systems, such as Haddop.

Python and R

Python and R are programming languages that everyone who works with Data Science should know.

Python is ideal for beginners and their scripts work quickly, connecting data with key web apps and frameworks to extract information from. R is a more traditional resource, but it also allows working with scaled data and constructing descriptive and predictive models.

Predictive modeling

Predictive models are the main working tool of the data scientist. They enable real-time information to be used to predict the future and understand whether business decisions can be supported by data.

Built with the help of Machine Learning, decision trees, regressive methods, and so many other strategies generate insights that can be understood even by those who are not skilled in Data Science.

The data scientist and his influence on the market

The role of the data scientist as an architect of strategy planning and execution has never been as important as it is now. The influence of this professional will point out which technologies we will use in the long run, how they will be applied in business solutions, and how teams will converge towards the right technologies to extract data.

In 2019, however, the data scientist will play the role of influencer. He will need to show companies how people are more aware of the information they provide and feel the need to know how data will be used by organizations.

It will be up to the data scientist to provide a fair path to everything that is shared with the company in which he or she works, ensuring data security to protect the interests of customers.

Recent leaks and security breaches have become a frequent concern even for users less familiar with technology. This makes 2019 the year of assuring consumers that their trust is valued and their data is used fairly.

Safer systems will be the new paradigm and it is imperative that Data Science professionals participate in building them. Building scalable solutions, which consider new legislation on data usage and consumer protection, is one of the challenges you will face.

The main trends in the data market for 2019

The sheer amount of data we are dealing with now is largely responsible for the trends we see in industry. Innovative ways of storing, processing, and analyzing information within companies have advanced at a tremendous speed as the demand for optimizations is constant.

Here are some of these key trends in the data market and we show how they will impact companies like yours.

Artificial intelligence

Artificial Intelligence is already part of the data market routine, but by 2019 it should become the main resource used by the industry. According to experts, companies that have not yet started thinking about implementing AI are lagging behind.

In Analytics, AI enables companies to make smarter decisions with great agility.

Natural language processing, for example, allows users to interact with business intelligence tools without having to know how the software helps them search for information more quickly.

Machine Learning

Machine Learning has the longest relationship with the data market. This method of analysis is present in predictive modeling and is a field of Artificial Intelligence that is based on the concept that systems can learn from data.

The partnership between the two solutions shows that it is possible to identify patterns that are significant enough to predict the future and make decisions with almost no human intervention and a high success rate.

Machine learning is at the heart of futuristic projects like Google’s self-driving car and things that have already become commonplace in our lives, such as Netflix’s recommendations.

Combined with language analysis tools, Machine Learning is one of the technologies that empowers a company’s data and turns them into actions that impact the business routine.

This year, we can expect new machine learning systems to be created with more precise, scalable, ready-to-use algorithms, both within organizations and in health, transport and government.

Internet of Things

The Internet of Things began to become popular recently through virtual assistants like Amazon’s Alexa. We now have more devices connected than ever and they are constantly exchanging network information. In order for this data to be processed, Big Data comes into play.

Just connecting two devices over the Internet does not make them smart. Neural networks, AI, Machine Learning, BI, and Big Data are what allow these things to work so efficiently. Without Amazon’s giant databases and technologies such as voice recognition, Alexa would not be as powerful as we know it.

These virtual assistants and the sensors within the devices we interact with are the future of data because they have the potential to generate lots of information in short time frames. Unstructured, they need to be processed by leading edge systems to be understood in real time and provide a response to the user.

Among Gartner’s key highlights for this year, we have some trends that will influence data management. Quantum computing, for example, is one of them. The use of computation built on subatomic particles (such as electrons and ions) provides much greater scalability than the computer systems we use today.

Therefore, according to consultancy, this is the technology with the greatest potential for disruption today. Computers capable of solving complex problems with high speed over traditional algorithms and devices are the future for industries such as pharmaceutical, military, and financial.

Other trends — such as those pointed out here on digital ethics, privacy, and development guided by Artificial Intelligence — are also big bets of the American company. The future of data is constantly changing and you need to be aware of all the news in the market.

They carry the potential to change both your company’s relationship to technology and the results you get from it. They can help you better understand customers, generate alternatives that more accurately meet their needs, and earn more by delivering enhanced consumer experiences.

By 2020, the market expectation is that each of us will create at least 1.7 megabytes of data every second. Tools like streaming services and applications will continue to have an impact on this, generating insights for every business like, click, and exchange of messages.

What is your company doing to benefit from this trend? How will the data market impact the way it relates to customers, suppliers, and partners? Share this post on social networks and keep following the contents of this blog to stay on top of the topic!