You can have all the data in the world, but if you don't know how to use it for your business benefit, there's no point in sitting on that raw information and expect good things to happen. The solution - Big Data Analytics - helps to gain valuable insights to give you the opportunity to make business decisions more effectively.
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.
The term “Data Analytics” describes a series of techniques aimed at extracting the relevant and valuable information from very large 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 in order to amplify business operation and make it as effective as possible.
Raw data is like a diamond in the rough. Data Mining takes the rough part and then Data Analytics provides the polish. That's the general description of what Big Data Analytics is doing.
There are many terms that sound the same, but they are different in reality. In case you are confused about what is the difference between data science, analytics, and analysis, it's easy to distinguish:
- The data analysis primarily focuses on processes and functions
- The data analytics deal with information, dashboards, and reporting
- The data science includes data analysis but also has elements of data cleaning and preparation (for further analysis).
Data Analysts are the specialists who control the data flows and make sense of the data using certain software.
Basic data analytics operations don't require specialized personnel to handle the process (usually it can take care of by stand-alone software), but in case of Big Data analytics, you do need qualified Data Analysts.
The purpose of a Data Analyst is to
- Study the information;
- Clean it from noise;
- Assess the quality of data and its sources;
- Develop the scenarios for automation and machine learning;
- Oversee the proceedings.
You don't need Big Data for Data Analytics since the latter is about analyzing whatever information you have. However, Big Data can be a deeper and more fulfilling source of insights, which is especially useful in the case of prediction and prescription analytics.
Data Analytics is all about making sense of information for your business operation and making use of it in the context of your chosen course of action.
It is important to understand what kind of analysis needs to be applied in order to make the most 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.
The purpose of descriptive analytics is to show the layers of available information and present it in a digestible and coherent form. It is the most basic type of data analytics and it forms the backbone for the other types.
Descriptive analytics is used to understand the big picture of the company’s process from multiple standpoints. In short - it is
- What is going on?
- How is it going on?
- Is it any good for business within a selected time period?
Because descriptive analytics are so basic, this type is used throughout industries - from marketing and ecommerce to banking and healthcare (and all the other.) One of the most prominent descriptive analytics tools is Google Analytics.
From the 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 certain period of time. In this case, descriptive analytics shows the following stats of interacting with content:
- Who (user ID);
- Circumstances (source - direct, referral, organic);
- When (date);
- How long (session time).
The insights help to adjust the campaign and focus it on more relevant and active segments of the target audience.
Descriptive analytics is also used for optimization of real-time bidding operation in Ad Tech. In this case, the analytics show the effectiveness of spent budgets and shows the correlation between spending and the campaign's efficiency. Depending on the model, the efficiency is calculated using goal actions like conversions, clicks, or views.
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. The anomaly is anything that raises the question of its appearance in the analytics, whatever doesn't fit the norm. It can be a spike of activity when it was not expected or a sudden drop in the subscription rate of your social media page.
- Anomaly Investigation. In order to do something, you need to understand how it happened. 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 may involve the following practices:
- Probability analysis
- Regression analysis
- Time-series data analytics
Diagnostic Analytics are often used in Human Resources management in order to determine the qualities and potential of employees or 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).
As you might’ve guessed from the title - predictive analytics is designed to foresee:
- what the future holds (to a certain degree)
- show a variety of possible outcomes
In business, it's often much better to be proactive rather than reactive. Therefore, Predictive Analytics helps you to understand how to make successful business decisions, which brings the most potent value for companies.
How do the Predictive Analytics algorithms work?
- Go through the available data from all relevant sources (for example, it can be one source or a combination of ERP, CRM, HR systems);
- Combine it into one big thing;
- Identify patterns, trends, and anomalies;
- Calculate possible outcomes.
While predictive analytics estimates the possibilities of certain outcomes, it doesn’t mean these predictions are a sure thing. However, armed with these insights, you can make wiser decisions.
Predictive Analytics is used in:
- Marketing - to determine trends and potential of certain courses of action. For example, in order 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 certain 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 certain scenarios, assessing current tendencies and providing diverse options for decision making.
Not to confuse prescriptive and predictive analytics:
- Predictive analytics says what might happen in the future
- Prescriptive analytics is all about what to do in the future
This type of digging into data presents a set of possibilities and opportunities as well as 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 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, for example:
- 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.
Now let’s look at the fields where data analytics makes a critical contribution.
Sales and operations planning tools are something like a unified dashboard from which you can perform all actions. In other words, it is a tight-knit system that uses data analytics in 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 aimed specifically at developing overarching plans with every single element of operation past, present or future is taken into a consideration in order to create a strategy as precise and flexible as possible.
The most prominent examples are 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.
Internal and external recommender engines and content aggregators are one of the purest representations of data analytics on a consumer level.
The mechanics behind it is simple:
- The user has some preferences and requirements, noted by the system.
- Web crawling or internal search tools for relevant matches based on user preferences.
- If there is a match, it's included in the options.
There are two types of user preferences that affect the selection:
- 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 relevancy of the suggestions of the certain products.
In addition, Google Search Engine personalization features enable more relevant results based on expressed user preferences.
The customer is always on the front stage. One of the most common usages of data analytics is aimed at:
- Defining and describing customers;
- Recognizing distinct audience segments;
- Calculating their possible courses of actions in certain scenarios.
Since the clearly defined target audience is the key for a successful business operation - this technique is widely used in a variety of industries, most prominently in digital advertising and ecommerce.
How does it work? Every piece of an information that the user produces keeps some sort of an insight that helps to understand what kind of product or content he might be interested in.
This information helps to construct with a big picture of:
- Who is your target audience;
- Which segments are the most active;
- What kind of content or product can be targeted towards which of the audience segments;
Amazon is really good at defining audience segments and relevant products to the particular customer (which helps it to earn a lot of money, too.)
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 the environment around you and get to know better what the current market situation is and what kind of action should be taken in order to make the most out of it.
Powers of hindsight and foresight can help to expose fraudulent activities and provide a comprehensive picture.
The majority of online fraudulent activities are made with an assistance of automated mechanisms. The thing with automated mechanisms is that they work 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 questionable accounts or treacherous intentions.
One of the key factors in maintaining competitiveness on the market in ecommerce and retail is having more attractive prices than the competition.
In this case, the role of data analytics is simple - to watch the competition and adjust the prices of the product inventory accordingly.
The system is organized around a couple of mechanisms:
- Crawler tool that checks the prices on the competitor's marketplaces;
- Price comparison tool which includes additional fees such as shipping and taxes;
- Price adjustment tool that automatically changes the price of a particular product.
These tools can be also used in managing discounts or special offer campaigns.
In addition to custom solutions, there are several useful ready-made data analytics tools that you can fit into your business operation.
- Excel Spreadsheet - the most basic 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 deeper insight - you need bigger guns.
- Google Analytics - standard descriptive analytics tool. Provides data on traffic, source and basic stats on user behavior. Can be used for further visualization via Data Studio.
- Zoho Reports - part descriptive analytics part task management tool. Good for project reporting and tracking progress. Works well to assess campaign performance results.
- Tableau Desktop - with this tool you can make any scope of data comprehensible in a form of graphs and tables. Good for putting things into perspective. Easy to use, light on the pocket.
- Domo - this analytics tool is good for medium sized operations with large networks to gather data from. In addition to visualization, it is capable of assessing probabilities of certain scenarios and proposing better fitting courses of actions.
- Style Scope - this tool is good for teamwork and planning. It maps data from a different source on one map and in the process unlocks its hidden possibilities.
- Microsoft Power BI - this tool can consolidate incoming data from multiple sources, extract insights, assess probabilities of certain turns of events and put them into a larger 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 the element, extract insights and calculate possibilities - all in one convenient dashboard.
- SAP ERP - this tool is primarily used 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.
These days, data analytics is one of the key technologies in the business operation. Data mining provides the information, and Data Analytics helps to gain useful insights from that information to integrate them into the business process and enjoy the benefits.
Consider Data Analytics a compass to navigate in the sea of information.