Top Data Analytics Techniques

Collaborate to asses needs

Establish questions

Data democratization

Data cleaning

Determine KPIs

Omit unnecessary data

Construct a data management roadmap

Integrate technology

Answer questions

Visualize the data

Interpret the data

Considering autonomous technology

Build a narrative

Sharing the load

Robust Analytics tools

Data Analysis and Data Reporting Defined

Data analysis is the process of evaluating data and drawing conclusions. Data analysis is standard for any business, but it's important that it's done correctly so that internal issues can be identified and quickly resolved.

This is where data reporting is useful. Data reporting is the process of collecting data and manipulating it to make it clearer for the audience that is analyzing it. Data reporting is an essential aspect of data analysis.

To understand the distinction better, see this page on data analysis reporting.

In an in memory-based environment, data access is much faster, because the information is cached and available through a fast I/O exchange, which offers a very fast, speed-of-thought, kind of interactivity with the data. However, in-memory only solutions are often limited to department-sized datasets of up to a terabyte. For this reason, they are insufficient for a large scale enterprise analytical environment.

In-memory data modeling can be done via a graphical user interface on the fly, whereas with a disk-based solution tends to require more data summarization to make the environment work properly.

Another feature of some in-memory solutions is associative indexing done at the user level, allowing you to assign a relationship between tables with a drag and drop tool, pulling data from relational or non-relational data sources, including flat files, spreadsheets, or almost any type of data source.

This is another common advantage of an in-memory BI solution. Read more about types of data analysis technologies here.

Read more about InetSoft's in-memory database technology to learn how it works and what its advantages are over a pure in-memory solution.

In the long run, adopting an effective analytics strategy will increase the success of marketing, helping you target new potential customers, and also avoid targeting the wrong audience with the wrong message. Customer satisfaction can also be tracked by analyzing reviews and customer service performance.

Managers can also benefit from data analysis, as it encourages business decisions to be made on facts and not simple intuition. With analytics, you can understand where to invest your capital, detect growth opportunities, predict your incomes, and tackle uncommon situations before they become problems. Data from your payroll can help you understand what has the biggest cost impact on your business and help you evaluate which departments produce the biggest return on your investment.

These are just a few examples of how relevant information can be gained from data analysis performed in all areas in your organization. With the aid of an effective dashboard software, you can present these data insights in a professional and interactive way.

Descriptive Analytics

Describing the past

Descriptive analytics is where any analysis of data begins, with the use of data to describe what has already happened. This is done by ordering, manipulating, and interpreting data, possibly from a plethora of different sources, to glean a valuable understanding of what has been going on in an organization.

Descriptive analytics are essential for the meaningful presentation of data. And although descriptive analytics do not predict future outcomes or explore causes, they lay the groundwork for these more advanced types of analysis.

Diagnostic Analytics

Exploring causes

Diagnostic analysis is one of the most powerful kinds of analytics. This kind of analysis goes beyond simple description to provide understanding as to why and how things happened the way that they did. This kind of understanding is often the primary goal of an organizations analytics strategy and its value cannot be understated.

Diagnostic analytics provide actionable insights in response to specific business questions. It is the foremost style of analytics used in scientific research, and is also a vital aspect of analytics in retail.

Market conditions might change in the performance of the insurer at the point of claim and might influence how the customer penalizes the insurance company. For example, if an insurance company performs particularly badly at the point of claim., then the customer may well penalize them in some way.

Very interesting metrics are emerging. It is important also that we take into account the policy position. For example, if a customer was declined coverage on the basis of something they may or may not do, then they have an obligation to declare that insurance had been declined to all future insurers.

Fraud analytics help predict policy holder fraud, but there are also quite big issues around supplier fraud, which is generally referred to as claims leakages. Predictive analytics has a major part to play in how companies manage their supply chain and optimize it, ensuring insurers pay no more than the absolute requirement.

Predictive analytics can guess these outputs that are being generated either around historical data or the actual data that is being given to the call center agent. The technology guess these outputs back to the agent in real time to make a difference at that inflection point based on APIs integrated within their system to ensure that the scoring mechanisms are relayed back to the contact management system in a real time fashion. They can be generally relatively easy to develop, and it just takes the right people and the proper building blocks to be able to get the output at one end, to the presentation of those outputs and on the screen for the agent on the other end.

To learn more about how predictive analytics can be used in the insurance industry, see this article.

Below are seven different ways to analyze data, along with practical real world applications.

Cluster Analysis

To perform a cluster analysis is to group (or cluster) data elements by similarity. This aim of this method is not so much to test a variable but to uncover hidden data patterns. This provides additional context to traditional analysis.

One application of cluster analysis is in marketing. Since there are usually too many customers to analyze individually, they must group the customers into clusters based on money spent, purchasing behaviors, demographics, and other relevant factors. This enables insights to be gained about different types of customers and how a business can appeal to them and serve them better.

Regression Analysis

A regression analysis uses historical data to understand how one or more independent variables influence the value of a given dependent variable. This type of analysis helps users understand different variables affect each other. This is an important component of predictive analytics, since the relationships of past variables aids in the prediction of future outcomes.

One common example of a regression analysis would be the breaking down of annual sales by variables like, marketing campaigns, product quality, customer service, store design, and sales channel, to see try and determine how the aforementioned variables affected overall sales.

If any one of these variables changed from year to year (stores closed during lockdowns for example) a regression analysis could be used to measure whether it had a negative overall impact on sales, or whether the shift to online orders was enough to compensate. In this example, store closings would be the independent variable and annual sales the dependent variable.

Factor Analysis

Also called "dimension reduction," factor analysis is used to investigate variability among observed, correlated variables to uncover unobserved variables called factors. Uncovering these independent latent variables, an ideal analysis method for streamlining specific data segments.

One example of the factor analysis method is a customer evaluation of a product. This initial assessment would be based on different variables like current trends, materials, comfort, color, shape, wearability, frequency of usage, and where the product was purchased. There is no limit to the variables that can be tracked. In this example, a factor analysis would be used to summarize all of aforementioned variables into homogenous groups, into a brother latent variable of design.

Information attained this way allows for the discovery of historical patterns and trends that can help predict future performance. Data mining is used to determine relationships amongst controllable variables that enable managers to visibly view impacts of certain factors on a business. It also allows users to drill down and discover correlations in the information that makes up the data. Information attained this way reveals historical patterns and trends that help track key indicators that are integral to forthcoming operations.

One great example of data mining is InetSoft's custom notifications, which enable the user to set automatic alerts based on particular conditions being met within an interactive dashboard or report. For example, in this supply chain dashboard, the user could set an alert to trigger when supplies dip below a certain quantity. Upon seeing the alert, the user can drill down into the data to explore the root cause of the issue.

To learn more about data mining, click here.

How Is Data Analyzed? Top Analytic Techniques for Business Users

Collaborate to Assess Needs

Before the analysis of data can begin, it's best to meet and collaborate with all key stakeholders within your business to determine your primary campaign or strategic goals, thus gaining a comprehensive understanding of the types of insights that are needed to steer management directives and grow your organization.

Establish Questions

Once core objectives have been outlined, the next step is to determine what are the questions that need to be answered to meet those objectives.. This is a crucial aspect of all data analytics strategy since it shapes every aspect of your approach.

To get the right answers, you must ask the right questions.

Data Cleaning

Harvesting data from a multitude of sources leaves one with a vast amount of information that can be overwhelming. The data can often include incorrect data that can color the analysis. For these reasons, the next logical step is to clean the data. This process is an essential step to take before visualizing data, to ensure that insights extracted from the data are accurate.

There are some key things to look out for while cleaning the data. The most important thing to watch for and eliminate are duplicate observations which occur when using multiple internal and external sources of data. Other things to look out for and fix are missing codes, empty fields, and incorrectly formatted data.

One type of data that usually requires cleaning is text data. As previously mentioned, the most common text inputs used today are social media comments, customer reviews, and questionnaires. But for intelligent algorithms to detect patterns in the text, the text data needs to be cleansed of invalid characters as well as spelling and syntax errors.

Cleaning data is crucial is to prevent arriving at false conclusions that can steer an organization in the wrong direction. By making sure data is clean, it can be ensured that business intelligence tools create accurate reports for the organization.

Once you get started measuring and tracking the most obvious performance indicators, and you immerse yourself in them, begin to puzzle in your own head what else could be measured, by noticing what other event or activity might cause the indicator you're already watching to go up or down. That will lead you in the direction of other leading indicators.

A "hub and spoke" approach to performance management is recommended, as data silos are something to be avoided. When each department creates their own metrics, many of them will end up overlapping and create inconsistencies with other departments. On the other hand, any effort to completely centralize the management of metrics and all the associated governance will collapse under its own weight from too much bureaucracy and red tape.

Some kind of hub and spoke environment for performance metrics has its place. Anything that has to be managed and handled and monitored at a corporate level, anything that is mission-critical, anything that goes across the enterprise boundaries, all of this belongs in a hub.

But when it comes to metrics that no one outside of a particular product line or a particular department is ever going to look at, such as marketing campaign effectiveness metrics, those can be created those on the spokes. The back office, finance and HR departments probably don't need to know about these metrics. So its best to make your performance metrics dashboard more agile based on the breadth of the audience.

Omit Unnecessary Data

Once appropriate data analysis techniques and methods have been selected, and a clear analytics strategy has been defined, the raw data collected from all sources should be explored, and use the KPIs used as a reference for removing any information that is not useful.

Removing extraneous information is essential for effective analysis since it allows users to focus their analytical efforts and extract maximum value from the remaining "lean" information.

Statistics, facts, metrics or figures that don't align with business goals or fit with overall analytics strategy can be eliminated for efficiency's sake.

Read why choosing InetSoft's cloud-flexible BI provides advantages over other BI options.

Integrate Technology

There is a multitude of methods for analyzing data. A major factor in the success or failure of your analytics strategy is integrating the right data analysis software.

Robust analytics software allows business users to draw relevant data from selected sources to calculate dynamic KPIs which then offer you actionable insights. They also present the information in a visual, intuitive, interactive format in a single executive dashboard..This is a data analytics methodology anyone can use.

Choosing the right technology for data analytics and reporting helps avoid fragmenting insights, saves time and effort, and allows business users to extract the maximum value from their organizations data.

Within the data analytics and BI industry, a small number of vendors, including InetSoft, have been creating applications that allow the combination or mashup of disparate data sources to be improvised without necessarily relying on the IT heavy step of ETL and data warehousing. This is now being referred to as data mashup. While combining disparate data sources is a common application for a data mashup, even in a single data source environment a mashup can be made by combining data from different tables in a way that had not been previously anticipated.

Answer Questions

If all of the above steps are followed, the right technology is used, and a culture of pervasive analytics is established where all different kinds of employees are empowered with the different ways to analyze data, the most crucial business questions will be answered.

Experience has shown that the best way to make data analysis accessible throughout an organization is by by providing employees with data visualization tools.

With a visual display of all that complex data, users are empowered with a better understanding of the information. Numbers may look different when taken from a spreadsheet and plugged into an attractive and dynamic chart. This shift in perception allows people to perhaps see things they did not previously and therefore become more informed, spot trends, monitor key metrics more closely, and essentially become more efficient decision makers for their business.

Vital business numbers and information coupled with attractiveness and interactivity suddenly bring a simple display of information to an accessible and visible way to become more successful as an enterprise.

Visuals provide deeper insights into key metrics, which in turn allow a business to solve their problems more efficiently and quicker - perhaps a moving gradient on a chart allows a CEO to spot a declining trend that he or she would have otherwise not noticed on a static spreadsheet. Now, management is making better plans for the organization's future. Plus, it did not take hours of sifting through complicated data.

Visuals are fast becoming a highly valued tool for gaining a a true sense of business performance. Previously, a manager might have been able to see how each of his sales people were doing in a quarter. With the power of visuals, that same manager may compare each quarter, each sales person, and further, find the answers to exploratory questions such as "What are the characteristics and trends among our highest paying customers?"

Further, a manager may not remember exactly how much revenue each department made in a quarter, but he or she may very well remember how big or small a visual bubble representing each department was. Visuals lead to improved management. Data visualization tools can also reveal important patterns, help expose a weakness or issue in your business, present and compare large amounts of information at once, and allow organizations to dig deeper in a "slice and dice" manner into crucial data.

Interpret the Data

Data interpretation is a fundamental aspect of the data analysis process. Interpreting data gives meaning to the analytical information and aims to derive concise conclusions from analysis results. As data often comes from a variety of different sources, data interpretation must be done carefully and with precision in order to avoid misinterpretation and incorrect conclusions.

While going through the process of data interpretation, there are three common pitfalls to avoid.

Confusing correlation for causation

Our minds are designed to notice patterns. While this trait has been essential for our survival, it can sometimes lead to a common mistake when interpreting data: confusing correlation with causation. Although all attributions of causation begin with observing correlations, it is not correct to assume that because two events happened together, one caused the other. This is why it is important to never simply trust ones intuition when interpreting data. Without objective evidence of causation, one merely has a correlation.

Building a Narrative

Now that all the technical applications of data-driven analysis have been explored, the next step is to bring all of these elements together in a way that will benefit the organization, - beginning with data storytelling.

The human mind is extremely narrative driven. Once the most valuable data has been cleansed, shaped, and using state of the art BI dashboard tools, one should strive to use that data to tell a story, a story with a beginning, middle, and an end.

This will make analytical efforts more accessible, intuitive, and digestible, which empowers more people throughout the business to use data discoveries to improve their decision making.

The reason machine learning is needed is that there are some problems for which it is very difficult to write software program to solve, such as recognizing a three-dimensional object from a novel viewpoint in new lighting conditions in a cluttered scene. This kind of function has traditionally been very difficult for a non human system to perform. Without understanding how are brains process this, it was hard to know what program to write, and attempts at doing so were horrendously complicate.

Another example is using software to detecting a fraudulent credit card transaction, since there may not be any nice simple rules that will indicate the transaction is fraudulent. This requires the combination of a very large number of not very reliable rules, and those rules change must over time as people change the tricks they use for fraud.

With the machine learning approach, instead of writing a program by hand for each specific task, for a particular task many examples are collected that specify the correct output for giving an input. The machine learning algorithm will then takes these examples and produces a program that does the job. The program produced by the learning algorithm may look very different from a typical handwritten program.

Sharing the Load

When the right tools and dashboards are used, metrics can be presented in a digestible, insight driven format, encouraging everyone in the organization to analyze relevant data and use it to their advantage.

Modern interactive dashboards pull and combine data from a variety of sources, delivering actionable insights from one centralized source, no matter if you need to monitor HR metrics or create reports that are due across numerous departments. In addition, these state of the art tools offer dashboard access on a host of different devices, meaning that everyone within the organization can connect to data insights anywhere.

Robust Analytics Tools

For the best-quality data analysis, it is essential to have the best quality software solutions that will ensure the best insights. As the analytics industry continues to expand, a greater variety of robust analytic tools become available. Here is a brief summary of types of data analysis tools for your business.

Business Intelligence

BI software gives its users the ability to process very large amounts of data from from a variety of sources in any format. With this technology, not only can data be e analyzed and monitored to extract actionable insights, it can also be converted into interactive reports and dashboards to visualize key metrics, using them to better manage an organization. InetSoft is an incredibly robust online BI software that can deliver powerful online analytics to both beginner and advanced users alike. InetSoft Style Intelligence a full-service business intelligence solution that includes state of the art data analysis, flexible KPI visualization, live interactive dashboards, and reporting, and machine learning algorithms to predict trends and minimize risk.

SQL Consoles

SQL, or Structured Query Language, is a programming language used to handle structured data in relational databases. SQL tools are popular among data scientists since they are useful for performing analytics on SQL databases. The most popular and established SQL tool is MySQL Workbench, which offers visual tools for data modeling and monitoring, SQL optimization, administrative tools, and visualization dashboards displaying key metrics.

Data Visualization

Data visualization software is used to represent data with charts, graphs, and maps, allowing non technical users to find patterns and trends in the data. InetSoft's BI solution includes a powerful data visualization tool that can benefit any business.

This robust visualization engine can be used to deliver compelling data-driven presentations to executives and shareholders, and empower users to see their data online with any device wherever they are, an interactive dashboard design feature that enables results to be showcased in an intuitive and interactive way, and to execute online reports that can be worked on simultaneously by several other people to enhance collaboration.

To underscore the importance of big data, here are some facts about the recent growth of big data and the use of it in data analysis.

By the year 2023, the big data industry will be worth $77 billion dollars.

94% of enterprises say that data analysis is crucial for their business growth and digital transformation.

Companies that realize the full potential of their data increase operating margins by 60%.

Artificial intelligence, which has been described previously on this page, is expected to grow up to $40 billion by the year 2025.

There are many different concepts in data analysis, but experimenting with their implementation can help to grow any organization, while also increasing efficiency, improving brand, and bettering organizational culture.