Modern business is data-driven – organizations rely on it to make decisions that make a difference. However, you might have heard the phrase, ‘Data is the new oil.’ This statement is open to two interpretations. The first one is that it is a precious commodity. The second interpretation is that data is not useful in its crude form, just like oil. It has to be refined and processed to become useful. That introduces us to the concept of data analytics. Data analytics is the process of using specific techniques and tools to reveal trends and metrics that would otherwise fade in the mass of information. In this article, we shall discuss the various ways companies use data analytics, their tools and techniques, best practices, and so much more. Keep reading to find out!
Types of data analytics
Before going deeper into the uses of business analytics in the business world, it would be a good idea to lay the foundation by looking into various types of data analytics. These categories are based on the scope of analysis conducted on a data set. Take a look.
Descriptive analytics answers the question, “What happened?” It uses historical data from a single source to explore the relationship between variables. One might argue that it is the simplest type of data analytics since it does not go beyond describing trends and relationships. A good example is a financial statement analysis. For instance, a company can compare its ratios against the industry’s to see whether it is underperforming or otherwise.
Diagnostic analytics is a deep dive into a data set to explain why something happened. It comes after descriptive analytics, trying to uncover the reason behind specific outcomes. While this step can be done manually, automated tools make it faster, easier, and more accurate.
As the name suggests, this type of data analytics tries to discern what might happen in the future based on what has happened in the past. When a business knows what is likely to occur in the future, it can take the proper steps to minimize risk exposure and maximize profit.
The last and most complex type of data analytics is prescriptive analytics. It combines data from various sources and machine-learning techniques to provide recommendations for business decisions. Data scientists must test prescriptive analytics models repeatedly to ensure that the recommendations are meaningful.
Uses of data analytics in business
There are several applications of data analytics in the business world. If you want to play an active role on this front, you can advance your education by pursuing an SBU master of business analytics online. St Bonaventure University’s online degree program is designed to prepare you for leadership roles working with big data. Let’s take a closer look at some of the applications below:
Enhancing operational efficiency
As the business world gets more competitive by the day, businesses are looking for ways to streamline their operations to stay ahead of the competition, and data analytics is right in the middle. Businesses use data analytics to enhance operational efficiency by identifying market needs using data analytics tools. For instance, a business can analyze data from a particular region to understand what consumers in that market need. Therefore, it can tweak the production plan as needed. A business can also analyze historical data to identify and seal loopholes in the supply chain.
Besides, the management can use data analytics to determine which processes are draining the budget or redundant. Consequently, they can take necessary actions to enhance efficiency.
Helps with team productivity issues
Above everything else, the success of a business relies heavily on the team’s productivity. This starts from the hiring stage. Recruiters must find top talent and cannot rely on luck or karma to land the best people. Data analytics comes in handy. A recruiter can use data like years of experience and performance to decide the right candidate for a role within a shorter time. Looking at data can also help HR managers predict top performers in the company. In the same breath, the management can uncover reasons that could be driving productivity down and fix the problems. That pushes employee satisfaction rates up, leading to a higher retention rate.
Enhancing cyber security
As businesses continue to rely on the internet, the risk of cybercrime goes up. Fortunately, data analytics is a powerful weapon against cyber security threats. A business can use data analytics to predict the likelihood of an attack and then find solutions before the issue escalates. Automated data analytics tools work around the clock, so nothing goes undetected. They can send alerts to the relevant stakeholders even without manual supervision. Besides quick and effortless detection of threats, data analytics for cyber security can also minimize false alarms.
Enriching customer experience
Businesses have to enhance customer experience at various touchpoints to be successful. And what better tool to turn to than data analytics? It provides concise information on customer behavior, needs, and preferences. This is valuable data that businesses can use to create products that meet consumers’ needs.
There are several ways of using data analytics to enrich customer experience. One of them is gathering, organizing, and analyzing customer feedback. This is a good way of identifying bottlenecks. Businesses can also use data analytics to predict customer behavior, so they can always be a step ahead.
Notably, there are several customer experience metrics a business can track. These include customer churn rate, customer engagement score, and customer effort score.
Improving product launch
When a business is launching a new product, it is very easy to get swayed away by the excitement and neglect the critical part – studying the market to see if it is the right move. A business needs to find answers to complex questions like how much money the new product will need and what the competitive landscape looks like. Think of an investor in the 1970s that wanted to open a new restaurant down the street. They would probably have to physically visit a few restaurants within the area to get an idea of the market. But we live in modern times, and data collection has gotten easier. However, the raw data cannot help much with a product launch.
Enter data analytics.
A business can answer these questions using data analytics tools and launch a successful product or service.
Assessing the performance of marketing campaigns
Data analytics is a critical component of digital marketing. It acts as the reference point when creating a marketing plan. For instance, a marketing team can collect descriptive data about their target market to determine which messages – written or visual –resonate with them. The team can also monitor campaign results to determine what is working and what is not.
Data analytics tools used in business
Most companies deploy multiple data analytics tools, depending on their goals. One of the widely known tools is Microsoft Excel. Others include Power BI, Apache Spark, R and Python programming languages, and Apache Storm.
There are several other analytics tools available tool. So, how does a business settle on one? The most important thing to consider is the business objectives. For instance, if a business wants to enhance customer experience, it would need a tool that can analyze natural language to analyze metrics like social media posts.
The other important thing to consider is the pricing. Costs could include subscriptions, growth, and hidden fees. It is also essential to consider the user interface, ease of integration with existing systems, and agility and scalability of each tool.
Data analysis techniques used in business
So far, you have seen the importance of data analytics in business and the various tools used to bring these concepts to life. Perhaps it is time to take a look at how it all happens. Let’s break down the various data analysis tools used in business. But first, it is worth noting that there are different types of data. These include big data, metadata, real-time data, and machine data. Data can also be categorized as either quantitative or qualitative. The former entails working with numerical variables, such as percentages and measurements. On the other hand, qualitative data is usually non-numeric, such as labels.
Now, let’s look at some of the most popular data analytics techniques:
Regression analysis identifies variables that impact a topic of interest, also known as the dependent variable.
This technique takes a large data set and shrinks it into smaller ones. The aim is to uncover hidden trends that would otherwise be difficult to see in a large data set.
The cohort analysis technique breaks a big data set into groups with similarities. That makes it easier for data analysts to discover patterns.
Time series analysis
This technique tracks data over time to solidify the relationship between a data point’s value and occurrence. It is commonly used to project financial forecasts.
Monte Carlo simulations
This technique models the probability of different outcomes taking place. It is an ideal tool for risk mitigation.
Stages of the data analysis process
Data analytics is a very systematic process. The first step is identifying the objectives. What business problem are you looking to solve with data? Once you have mapped that out, you can proceed to data collection. It could be first-party, second-party, or third-party data depending on your goal.
The next step is data cleaning. This is where you eliminate any incorrect or inconsistent data that could compromise the results. For instance, you might want to remove duplicates for accuracy. Once your data is clean, it’s ready for analysis. This is where the different data analysis techniques come into play.
The fifth and final step is interpreting the results. You will need data visualization tools like Tableau to present the data for interpretation.
Careers in data analytics
As you may already know, there is a high demand for roles in data, and with a master’s in business analytics, you are spoilt for choice. As a professional in data analytics, you play a crucial role in helping businesses make data-driven decisions, taking them closer to their goals. You will also use cutting-edge technology in your work, so if you love a challenge, you are on the right path. Take a look at some of the analytics jobs you might consider.
Business intelligence analyst
A business intelligence analyst works with internal and external data to identify potential issues that need addressing. Besides, a BI analyst translates the results of data analysis into actionable strategies that can improve business operations.
This role might sound very similar to a BI analyst. But that is not true. Typically, business intelligence is anchored in descriptive analytics. Conversely, business analytics leans toward diagnostic, predictive, and prescriptive analytics.
A data architect designs the structures a business needs to acquire, analyze, manage, and utilize data. It is the job of a data architect to define how data will flow through a given framework and to oversee the implementation of that framework.
Are you drawn to the consumer side of the business? If yes, you might consider a career as a marketing analyst. The primary responsibility of a marketing analyst is developing strategies to help the business better connect with new customers while retaining old ones.
Machine learning engineer
You can take your career in data analytics a notch higher by becoming a machine learning engineer. You would be tasked with creating algorithms, models, systems, and frameworks machines use to learn and perform functions independently. You must be fluent in various programming languages like Python for this role.
The future of data analytics
Judging by how things are going, data analytics will rapidly change the business landscape in the future. It is already at the core of several business decisions, and the trend is here to stay. Therefore, businesses will continue investing in sophisticated data analytics tools. The demand for data analytics professionals will also go up.
However, businesses must be on the lookout for the risk of losing their personal touch with their consumers. While data is crucial, the human experience should not be reduced to metrics. Therefore, there are interesting times ahead as businesses find the perfect balance between reliance on data and being in touch with the human side of their customers.