Data Analytics Explained

Insight into data is one of the most valuable assets in any industry. It is a well-known fact that data on its own isn’t much of a thing. You can gather all sorts of data from all kinds of sources, but as it is — it isn’t any good for business. That’s where big data analytics comes into play.

You can’t just sit on raw data and expect business to grow simply because you gather a lot of data. It is how one can extract value out of it and use it for its benefit and make the most available data that matters.

In a way, data analytics is the crossroads of the business operations. It is the vantage point where you can watch the streams and note the patterns.

But first — let’s explain the basics.

What is Data Analytics?

The term “Data Analytics” describes a series of techniques aimed at extracting the relevant and valuable information from extensive and diverse sets of data gathered from different sources and varying in sizes.

Here’s what you need to understand about data — everything on the internet can be its source. For example:

• content preferences,
• Different types of interactions with certain kinds of content or ads,
• use of certain features in the applications,
• search requests,
• browsing activity
• online purchases.

This data is analyzed and integrated into a bigger context to amplify business operations and make it as useful as possible.

Here’s a metaphorical reiteration. Raw data is like a diamond in the rough. Data mining takes the rough part, and then Data Analytics provides polish.

That’s the general description of what Big Data Analytics is doing.

Role of Data Analyst in the Data Analytics operation

The role of data analysts is easy to describe — it is the specialist who controls the proceedings and keeps track of its performance.

It should be noted that the primary data analytics operation does not require specialized personnel to handle the proceedings. However, in the case of Big Data scales — qualified data analysts are needed.

The purpose of the data analyst is to

1. Study the information;
2. Clean it from noise;
3. Assess the quality of data and its sources;
4. Develop the scenarios for automation and machine learning;
5. Oversee the proceedings.

Use of Big Data in Data Analytics

It should be noted that Data Analytics does not require Big Data scale for operation. It is about getting insights out of data. However, Big Data can be a deeper and more fulfilling source of insights, which is especially useful in prediction and prescription.

big data and business analytics

Data Analytics is all about making sense of information for your business operation and making use of it in your chosen course of action.

It is essential to understand what kind of analysis needs to be applied to make the most out of available information and turn a pile of data into a legitimate strategic advantage.

Data Analytics operation is divided into four big categories. Let’s look at them one by one.

Types of Data analytics

1 Descriptive Analytics — What Happened?

The purpose of descriptive analytics is to show the layers of available information and present it in a digestible and coherent form. It is a necessary kind of data analytics. The one that forms the backbone for the other types of analytics.

Descriptive analytics is used to understand the big picture of the company’s process from multiple standpoints. In short — it is

1. what is going on?
2. how it is going on?
3. Whether it is any good for business within a selected timeframe?

Descriptive analytics is the most commonly used type of data analytics. It is common in every industry — from marketing and eCommerce to banking and healthcare. One of the most prominent descriptive analytics tools is Google Analytics.

From a technical standpoint, the descriptive operation can be explained as an elaborate “summarizing.” The algorithms process the datasets and arrange them according to the found patterns and defined settings and then present it in a comprehensive form.

For example, you have the results of the marketing campaign for a specified period. In this case, descriptive analytics shows the following stats of interacting with content:

• Who (user ID);
• in which circumstances (source — direct, referral, organic);
• When (date);
• How long (session time);

These aspects, in turn, can be used to adjust the campaign and make it focused on relevant and more active segments of the target audience.

On the other hand, the same thing is used to optimize the Real-time bidding operation in Ad Tech. In this case, descriptive analytics shows the effectiveness of spent budgets. It breaks the correlation between spending and its efficiency in terms of conversions and/or clicks (depends on a chosen model).

2 Diagnostic Analytics — How it Happened?

The purpose of diagnostic analytics is to understand:

• why certain things happened
• what caused this turns of events?

Diagnostic analytics is an investigation aimed at studying the effects and developing the right kind of reaction to the situation.

The operation includes the following steps:

• Anomaly detection. Anomaly is anything that raises the question of its appearance in the analytics. Something unlike anything else in the stats. It can be a spike of activity when it was not expected or a sudden drop in your social media page’s subscription rate.
• Anomaly Investigation. This process includes the identification of sources and finding patterns in the data sources.
• Causal relationship determination. After the events that caused anomalies are identified — it is time to connect the dots. This process may involve the following practices:
• Probability analysis
• Regression analysis
• Filtering
• Time-series data analytics

The diagnostic analysis is often used in Human Resources management to determine the qualities and potential of the employees or the candidates for positions.

It can also apply comparative analysis to determine the best fitting candidate by selected characteristics or to show the trends and patterns in a specific talent pool over multiple categories (such as competence, certification, tenure, etc.).

3 Predictive Analytics — What could happen?

As you might’ve guessed from the title — predictive analytics is designed to foresee:

• what the future holds;
• show a variety of possible outcomes

With this aspect covered, your company will be ready to act without a fuss and with a certain swagger.

Predictive analytics presents the most potent value for companies as it attempts to construct what is going to happen according to the already available information.

How does it work?

• The algorithms are going through the available data from all relevant sources (for example, a combination of ERP, CRM, HR systems);
• combine it into one big thing;
• identify patterns and anomalies;
• calculate possible outcomes.

While predictive analytics estimates the possibilities of specific outcomes — it doesn’t mean these predictions are a sure thing. It is merely a set of options calculated out of available information.

Predictive analytics is used in:

• Marketing — to determine trends and potential of particular courses of action. For example, to determine the content strategy and types of content more likely to hit the right chord with the audiences;
• eCommerce / Retail — to identify trends in customer’s purchase activities and operate product inventory accordingly.
• Stock exchanges — to predict the trends of the market and the possibilities of changes in specific scenarios.
• Healthcare — to understand possible outcomes of disease outbreak and its treatment methodology. It is widely used for scenario simulation studies and training.
• Sports — for predicting game results and keeping track on betting;
• Construction — to assess structures and material use;
• Accounting — for calculating probabilities of specific scenarios, assessing current tendencies, and providing diverse options for decision making.

4 Prescriptive Analytics — What should we do?

Prescriptive analytics is probably the most exciting type of big data analytics. It is a logical continuation of predictive analytics. However, if predictive analytics is focused on what will happen in the future, prescriptive analytics is all about what to do and how to act in the future.

This type of digging into data presents:

• a set of possibilities and opportunities;
• options to consider in various scenarios;

Tech-wise, prescriptive analytics consists of a combination of:

• specific business rules and requirements,
• selection of machine learning algorithms (usually supervised
• modeling procedures

All this is used to calculate as many options as possible and assess their probabilities.

Then you can turn to predictive analytics and look for further outcomes (if necessary). It is commonly used for the following activities:

• Optimization procedures;
• Campaign management;
• Budget management;
• Content scheduling;
• Content optimization;
• Product inventory management.

Prescriptive analytics is used in a variety of industries. Usually, it is used to provide an additional perspective into the data and give more options to consider upon taking action.

Among the most prominent are:

• Marketing — for campaign planning and adjustment;
• Healthcare — for treatment planning and management;
• eCommerce / Retail — in inventory management and customer relations;
• Stock Exchanges — in developing operating procedures;
• Construction — to simulate scenarios and better resource management.

Use cases for Data Analytics

Now let’s look at the fields where data analytics makes a critical contribution.

Sales and Operations Planning Tools

Sales and operations planning tools are like a unified dashboard from which you can perform the entire process. In other words, it is a tight-knit system that uses data analytics on a full scale.

As such, S&OP tools are using a combination of all four types of data analytics and related tools to show and interact with the available information from multiple perspectives.

These tools are explicitly aimed at developing overarching plans with every single element of operation past, present, or future. This aspect is taken into consideration to create a precise and flexible strategy.

The most prominent examples are the Manhattan S&OP and Kinxaxis Rapid Response S&OP. However, it should be noted that there are also custom solutions tailor-made for the specific business operation.

Recommendation engines

Internal and external recommender engines and content aggregators are among the purest representations of data analytics on a consumer level.

The mechanics behind it is simple:

1. There is a set of requirements imposed by the user;
2. According to them, a web crawling or internal search tool is looking for relevant matches;
3. If there is a match — it is included in the selection.

The selection can be affected by user preferences. There are two types of these:

• Direct feedback via ratings;
• Indirect via interacting with the particular types of content from the specific sites.

All this combined enables the engine to present the user with the content he will most likely interact with.

One of the most prominent examples of this approach is used by Amazon and Netflix search engines. Both of them are using extensive user history and behavior (preferences, search queries, watch time) to calculate the relevancy of the suggestions of the particular products.

Also, Google Search Engine’s personalization features enable more relevant results based on expressed user preferences.

Customer Modelling / Audience Segmentation

The customer is always on the front stage. One of the most common usages of data analytics aims at:

• Defining and describing customers;
• Recognizing distinct audience segments;
• Calculating their possible courses of action in specific scenarios.

Since the clearly defined target audience is the key for a successful business operation — this technique is widely used in various industries, most prominently in digital advertising and eCommerce.

How does it work? Every piece of information that the user produces keeps an insight that helps to understand what kind of product or content he might be interested in.

This information helps to construct a big picture of:

• Who is your target audience?
• Which segments are the most active?
• What kind of content or product you can be target towards which of the audience segments?

Amazon is good at defining audience segments and relevant products to a particular customer.

Market Research / Content Research

Knowledge is half of the battle won, and nothing can do it better than a well-tuned data analytics system.

Just as you can use data analytics algorithms to determine and thoroughly describe your customer — you can also use similar tools to describe an environment around you and get to know better what the current market situation is and what kind of action should be taken to make the most out of it.

This practice is universal for every industry.

Fraud Prevention

Powers of hindsight and foresight can help to expose fraudulent activities and provide a comprehensive picture.

Enter Data Analytics.

The majority of fraudulent online activities are made with the assistance of automated mechanisms. The thing with automation is that it works in patterns, and patterns are something that can be extracted out of the data.

This information can be integrated into a fraud detecting system. Such approaches are used to filter out spam and detect unlawful activities with doubtful accounts or treacherous intentions.

Price Optimization

One of the critical factors in maintaining competitiveness in eCommerce and Retail is having more attractive prices than the competition.

In this case, data analytics’s role is simple — to watch the competition and adjust the prices of the product inventory accordingly.

The system is organized around a couple of mechanisms:

1. Crawler tool that checks the prices on the competitor’s marketplaces;
2. Price comparison tool which includes additional fees such as shipping and taxes;
3. Price adjustment tool that automatically changes the price of a particular product.

These tools can also be used in managing discounts or unique offer campaigns.

Data Analytics Software

In addition to custom solutions, several useful ready-made data analytics tools can fit into your business operation.

Basic data analytics tools

• Excel Spreadsheet — the most fundamental tool for data analytics. It can be done manually and show enough information to understand what is going on in general terms. However, if you need more in-depth insight — you need bigger guns.
• Google Analytics — standard descriptive analytics tool. Provides data on traffic, source, and basic stats on user behavior. It can be used for further visualization via Data Studio.
• Zoho Reports — part descriptive analytics part task management tool. Useful for project reporting and tracking progress. Works well to assess campaign performance results.

Sophisticated data analytics tools

• Tableau Desktop — with this tool, you can make any scope of data understandable in the form of graphs and tables. Suitable for putting things into perspective. Easy to use, light on the pocket.
• Domo — this analytics tool is useful for medium-sized operations with large networks to gather data from. In addition to visualization, it can assess the probabilities of specific scenarios and propose better fitting courses of action.
• Style Scope — this tool is useful for teamwork and planning. It maps data from different sources in one map and, in the process, unlocks its hidden possibilities.

Heavy Artillery data analytics tools

• Microsoft Power BI — this tool can consolidate incoming data from multiple sources, extract insights, assess probabilities of precise turns of events, and put them into broader context with a detailed breakdown of possible options. In other words, it turns Jackson Pollock’s painting into Piet Mondrian’s grid.
• Looker — multi-headed beast of a tool. With its help, you can combine data from multiple sources — track overall progress, break it down to element, extract insights, and calculate possibilities in one convenient dashboard.
• SAP ERP — this tool is for sales management and resource planning. With its help, you can map out the goals, combine information, assess performance and possibilities, and decide what to do next.

In Conclusion

These days, data analytics is one of the critical technologies in business operations.

Suppose data mining is providing with the resources to dig up the insights. In that case, data analytics makes it possible to comprehend these insights and integrating them into the business process to its fullest capacity.

It lets one navigate in the sea of data and stay on the right course.