Data can help businesses increase efficiency and identify new market outlets that may have been otherwise overlooked. With a data-directed approach, the hyper-growth potential and profitability become limitless and more intelligence-based.
As we see, big data is rife with advantages. But you can’t tap into those without the right data analytics software and processes. While raw data has a lot of potential, it’s shifting the paradigm beyond data that connects insights with action.
With that being said, let’s have a closer look at the buzzword of Big Data and how you can implement it in your business operations.
Behind The Beast
In a broad sense, the data analytics definition refers to the process of analyzing raw data to identify patterns and answer big questions. This operation is aimed at fetching relevant and helpful information bits from the ocean of data pumping from various sources.
In reality, data analytics is an umbrella term that includes many techniques with many different objectives.
The main thing you need to know about data is that virtually everything on the web can contribute to its volume. Here are just to name a few:
- Google searches
- Tweets & Retweets
- Media sharing
- Online purchases
This data is then analyzed and used to propel companies forward by enacting algorithms across the organization to optimize critical business moments.
Data Analysis vs. Data Analytics vs. Data Science
Due to the similarity of words, some people share a completely erroneous impression of what these terms mean. In case you think the same about all the three, here’s a cheat sheet for you:
- Data analysis is performed to analyze and group the past events.
- Data analytics generally refers to the future instead of unraveling the past events.
- Data science is close to data analysis. However, it generates broader insights that have to do with questions to be asked.
Role of Data Analyst in the Business
A Big Data Analyst is a specialist who processes and interprets massive amounts of data. They look for logical connections and help the client identify factors of interest to the business using specific tools.
If your company doesn’t have to process massive chunks of big data daily, you don’t need a data analyst to handle the process. However, if you deal with big data suffused across your business operations, a qualified Data Analyst is a mandate.
Data Analyst duties and responsibilities typically include the following:
- Collecting and interpreting data
- Identifying the important trends
- Reporting the results to the team
- Preparing reports for executive leadership
- Pinpointing and defining new business improvement opportunities
Types of Data Analytics
Data analytics comes in various forms and shapes. Overall, four major categories comprise this notion. Let’s start with the simplest one and work our way up to the more advanced types.
Descriptive Analytics – What Happened?
This type of analytics refers to the field of statistics whose methods focus on collecting, organizing, and digesting “raw” data from various sources. This later helps discover interpretable dependencies and patterns.
Typically, descriptive analytics focuses on historical data, providing the context that is vital to understanding the current processes. This is why it is often referred to as “what-happened-analytics,” since it provides a deeper look at the data to explain the reasons for certain events, process behaviors, etc.
It is the most elementary type of data analytics that paves the way for the other models.
Descriptive analytics helps you understand how the company is doing now. It requires customer feedback, sales data, website traffic – that is, any data from the past that can be used to analyze the business to the present.
Diagnostic Analytics – How It Happened?
Diagnostic analytics is a form of advanced analytics that relies heavily on descriptive analytics and analyzes data to identify the factors that influence the target parameters.
This type of analytics compares the accumulated data with other data through a set of sophisticated algorithms.
Diagnostic analytics has made it possible to examine problems in detail, identify weaknesses, and identify pattern chains of events. Think of it as a deep dive into your data to extract valuable insights.
This type of analytics identifies what factors are influencing positive and negative performance. If a company’s descriptive report demonstrates that ticket sales are declining, the diagnostic report can expose that the problem lies in a decrease in marketing spending.
Another example involves a focal area that every company should be paying top attention to, i.e. security. Diagnostic analytics can help you locate the connection between security rating and the number of incidents. Your company can use these results to plan precautionary measures for vulnerabilities.
Using descriptive and diagnostic analytics, you can move on to predictive analytics.
Predictive Analytics – What Could Happen?
In short, predictive analytics is taking data and interpreting it in the context of historical patterns with the help of analytics techniques. This empowers companies to make predictions about future outcomes and prevent potential incidents.
For example, you can monitor customers, analyze their movements around the store and find out that in 20 minutes a stream of people will check out. To avoid long lines, you can open registers beforehand.
Application fields of Predictive Analytics:
- Marketing – you can predict the effectiveness of your marketing campaign. If you know what a particular advertising activity will bring you, it’s easier to plan your budget, ditch ineffective advertising channels or campaigns, and launch new ones.
- Ecommerce / Retail – predictive analytics helps not only to attract new customers but also to improve the customer experience of loyal customers with personalized recommendations.
- Manufacturing – you can optimize the production process. If you collect information about the parameters that affect the performance of your equipment, it is much easier to prevent breakdowns and perform the necessary maintenance.
- Risk management – If there is a risk in your business, then by analyzing past negative events you can eliminate risks in the future.
Prescriptive Analytics – What Should We Do?
This analytics type is a form of data analysis that tests data or content to answer the question ‘What should we do? ” or “What can we do to make X happen?”.
In other words, prescriptive analytics uses big data, such as historical and real-time data, to not only predict what will happen and when, but also why it will happen. The results of this analytics can be used as a backlog for recommending actions based on those predictions. By acting on these insights, companies can maximize an impending opportunity, optimize the situation, reduce future risk and gain a competitive advantage.
From a tech standpoint, prescriptive analytics is a mix of:
- Particular business rules and requirements,
- A number of supervised ML algorithms
- Modeling processes
This kind of analytics is widely used in the following areas:
- Treatment planning
- Marketing and sales
- Transportation industry
- Financial markets
- Content management
- Inventory management
Big Data Analytics Use Cases
Now let’s have a look at the areas where data analytics is no longer optional.
Sales and Operations Planning Tools
S&OP software helps organizations make projections for demand, supply, and the resulting profit. In simple words, S&OP is a decision-making process that aligns the company’s diverse functions with the overall business plan. The importance of this process is especially tangible in enterprise business since it guarantees the scope, scale, and speed of the commercial operations.
Tech-wise, S&OP software is making use of all four types of data analytics to assess data from different perspectives. In practice, this software fits in a dashboard that provides team members with easy access to specific data and reports.
The leading S&OP examples are the Manhattan S&OP and Kinxaxis Rapid Response S&OP. If companies need a solution well-suited to their unique business needs, they can also build a bespoke S&OP.
Recommendation systems have made our lives a lot easier. One example of this boon is how our online shopping experience has transformed. As we glance through items, the recommendation system suggests products we might be interested in buying. And big data behind recommendation engines is like a power behind a throne. A typical recommendation system goes hand in hand with oceans of data including past purchases, browsing history, and feedback.
Usually, a recommendation system sifts data through the four phases.
- Collection of data – data can be either explicit or implicit data.
- Storing the data – the more information is in the system, the more accurate recommendations you’ll make.
- Analyzing the data – data is processed using different analysis methods
- Filtering the data – this phase is aimed at digging up relevant data that will be later used for offering recommendations.
Overall, the scalability and power to process vast quantities of both structured and unstructured data make big data indispensable for companies to make sense of zillions of clicks.
Customer Modelling / Audience Segmentation
Big data is also changing the face of customer modeling. In the pre-big data times, marketing used to be a one-way highway. Companies did not have any feedback to course-correct their market segments and interact with their customers.
Today, it’s easier for brands to squeeze higher ROI and launch successful email marketing campaigns thanks to the proliferation of data analytics. The most popular use cases are aimed at:
- Identifying and classifying customers;
- Outlining distinct audience segments;
- Predicting their behavior in particular scenarios.
Thus, Amazon is a winner at defining audience segments and offering relevant products to the particular shopper.
Data analytics is critical to effective fraud detection and prevention, thus ensuring the safety of customers and employees. The earlier an organization detects fraud, the sooner it can limit access to a, say, bank account to minimize losses. By implementing some fraud detection schemes, banks can provide the necessary protection and avoid significant losses.
The key points of fraud detection include:
- Obtaining data samples for model evaluation and pre-testing
- Evaluating the model
- Testing stage
Since data sets are always different, each requires individual preparation and adjustment by data scientists. Translating deep theoretical knowledge into practical application requires expertise in data mining techniques such as aggregation, clustering, prediction, and classification.
The Bottom Line
Data analytics is a game-changer in the modern business world. Instead of frittering away time and resources on traditional business intelligence solutions, it allows for almost immediate answers. Being an essential asset for companies, data analytics amplifies business operations by fostering disciplined thinking, identifying key decision-makers, and reinforcing mission-critical processes.