Data Analysis Software

Do you remember Michael Burry? The critically acclaimed film on financial markets- The Big Short is based on this legendary hedge fund manager's life. He successfully predicted the US housing loan bubble of 2007-2008 that triggered the economic depression of 2008. How was he able to do that? By analyzing the mortgage lending data from 2003 to 2004. When the whole S&P index rose only 3% between 2000 and 2008, the funds managed by Michael Burry returned a whopping 489.34% during the same period.  That's the power of data analysis. For the hundreds of decisions organizations make every day, data analysis provides a mechanism to identify patterns, anticipate results, and respond proactively. 

Today's businesses are luckier than Michael, as what he did manually can be done through a click of the button using a Data Analysis Software.  The data analysis technology has advanced dramatically in the past decade and has taken a leap forward with more sophisticated algorithms and massive data processing capabilities. However, deploying the right technology that provides businesses with functional expertise to use data is the biggest challenge that companies face. 

This buyer's guide attempts to guide businesses in making the right decision to invest in the best data analysis software by providing them a comprehensive view of all aspects of a data analysis software such as features, use, pricing, and types. 

Data analysis is the process and technique of analyzing data to draw conclusions, get insights, and highlight the most useful part of the raw data. Data analysis involves cleaning and modeling data to unearth critical information for business decision-making. Today, mechanical processes, preset algorithms, and software applications are used to process the data for analysis. 

Types of Data Analysis Methods:

Descriptive Data Analysis:

Descriptive data analysis, a part of statistical data analysis, focuses on past data such as profits, revenues, sales, KPIs, etc. to reveal meaningful insights about the business's performance over time. Companies often visualize their earnings and revenues over the past years (Quarterly, Yearly Reports) with trendlines formed with descriptive data analysis methods.

Diagnostic Data Analysis:

Diagnostic data analysis is the process of identifying behavior patterns and relating them with a recent business phenomenon. For example, if sales increased dramatically in a particular month, then a business may use diagnostic data analysis to determine the factors that caused the sudden surge. If a business finds out that sales data corresponded with the increased marketing spend prior to the sales surge month, then they can decide if they should increase the marketing budget for the coming months too.  

Predictive Data Analysis:

Predictive data analysis predicts future trends, events, revenues, etc. based on the current and past data analysis. For example, a company studies past ten-year sales data and finds that sales are generally down in April-May months. Based on such data, the company can revise their inventory needs for next year's May and June. 

Prescriptive Data Analysis:

Prescriptive data analysis utilizes the results of above all analysis to form a conclusive strategy to mitigate a challenge or decide on the future course of action. 

Consider the following business scenarios:

1. A manufacturer  to launch a new product in the overseas market
2. A pharma company wants to apply for new drug approval with the FDA.
3. A sales manager wants to know the reason for the product's poor performance in a particular location.

In all the above situations, data analysis is essential. The company will have to analyze the market data for a response to such products in the overseas market, the pharma company will have to rely on trial test results and its analysis before applying, and the sales manager will have to carry out a detailed analysis of the customer preference and buying behavior survey to find the reason for poor performance. Data analysis is the lifeline of businesses. 

As businesses around the world get smarter, data-driven insights become the key to competitive advantage. Web, sensors, phone transactions, cameras, POS systems, etc. create a huge array of data sets for businesses that can be used for valuable analytics. Data-driven insights enable companies to better target the market compared to their competitors.

​​​​​​Data set

The data set is the collection of data represented in rows and columns (Units of observation). Data itself is a plural form of datum. For example, you can observe toys and infer a set of characteristics such as size, color, price, shape, etc. If such patterns and differences are put into rows and columns to present the information about those toys, it will be called a data set. 

Data source

In the technological sense, data source refers to the location from which data originates. For example, a company may store the names, email addresses, purchasing history, contact details, preferences, etc. of customers in a CRM database. In this case, the database containing customer data is the data source. Data can be retrieved whenever required from its source through web accessibility or local server connection. 

Database

It is the collection of structured data. It usually consists of organized sets of information that can be retrieved and updated as required.  There are many types of databases based on their operation. For example, Relational databases such as MySQL, Oracle, Sybase, and more use the standard language SQL (Structured Query Language) for searching, inserting and updating databases. Another type,  NoSQL database, is used to store unstructured data. Nowadays, advanced Cloud databases with massive storage capacities and support to various software applications are deployed by businesses. According to Gartner, by 2022, cloud services will be essential for 90% of data and analytics innovation.

Data blending

In the data blending process, data analysts collect data from various sources and combine them to form a useful functioning data set. Data blending is done to improve the quality, functionality, and utility of data. The blended data can reveal more profound insights into the data and drive better analysis. Businesses that imbibe predictive analytics into their core functions often rely on data blending for higher intelligence. 

Qualitative data

Qualitative data refers to data collected in a descriptive or categorical form. It is the data that cannot be computed by can be put in a verbal or narrative form. It is not measurable and exploratory in nature. For example, opinion interviews, case studies, expert opinions, open-ended questions, etc. can get you qualitative data. 

Quantitative data

Quantitative data refers to data collected in numerical units. It is measurable and conclusive. In short, this data can be analyzed statistically and often provides information about the level of occurrence of some entity. For instance, responses from the Likert scale can get you quantitative data.  

Data Alerts

Data alerts are email notifications or message alerts that one gets when data changes or data levels reach a pre-specified condition. Data alerts are helpful to businesses when a huge amount of data is monitored on a day to day basis, and businesses don't want to miss out on vital data changes.

Data visualization

Data visualization is the technique to represent data in visual graphics form by using elements such as charts, graphs, and maps, etc. Data visualization helps to see the trends and patterns in any data set clearly and in an easily comprehensible manner. 

Data points

Datapoint is a unit of information, a single fact, an individual item, in the data that is being analyzed. Multiple data points create charts or trend lines.

Metadata

Metadata is the data that summarizes information about other data. Metadata is a useful tool for finding and extracting relevant information from a huge dataset. Metadata usually provides information such as title, keywords, themes, context, and utility of the data. 

Big data analytics

Big data analysis is the method of harnessing and analyzing a large amount of data for unearthing patterns, correlations, and hidden value in the massive data. Businesses use big data analytics for faster decision making, gauging ideas for rolling out new products and services, and gain competitive advantage by data-fuelled insights. Most of the Big data is generated from sensors, devices, transactions, web, and social media. 

Granular data

Granular data is the data that is derived from dividing large data into sub-divisions. For obtaining granular data, data is divided into the lowest possible extent. It helps data analysts to be more specific in confirming data facts. 

Data Mining

Data mining is done to discover patterns in large data sets using artificial intelligence and machine learning. 

Data analysis has played a crucial role in the progress that human civilizations have made so far. The inscriptions on walls and caves were analyzed to learn about our history and ancient languages. Whatever we know about the world today, the solar system, the galaxies, etc. are possible due to data analysis. When Henry Ford measured the speed of assembly lines to increase the production of cars, he was using data analysis. W.D Gann, the legendary trader, studied data of the past 1000 years of wheat and accurately predicted that wheat would sell at $1.20 in September 1909. He also used data analysis. (Source-Ticker and Investment book by Richard D. Wykoff). 

Data is everywhere, and humans have relied on data for many of their accomplishments. The data process evolved with time, and today most of it is automated and carried out by software applications. 

Let us discuss the data analysis process in detail. The process involved in data analysis involves several different steps:

Step 1: Defining data prerequisites:

Before one goes for data analysis, one has to define the prerequisites for the data analysis process clearly:

• The questions for the data analysis: One must know why the data needs analysis? Clearly define the questions for which the answers are required.
• Data segregation points: One needs to identify how data will be grouped? List down the parameters for data grouping such as age, demographics, categories, colors, size, etc.
• Method: Deciding which method will be used for analysis is also critical.
• Time Frame: One needs to decide the time frame for which the data will be analyzed.

Step 2: Data collection:

Once the prerequisites are met, one needs to collect data using data collection methods such as:

• Getting data from already existing databases such as CRM database, inventory database, sales data, ledgers, bank statements, etc.
• One can also gather data through interviews, case studies, questionnaires, surveys, polls, expert opinions, website analytics, customer reviews, etc.

Step 3: Data Cleansing

Data cleansing or data cleaning method is the process to get rid of irrelevant, duplicate, inaccurate and incomplete data that is not required for data analysis. Data cleaning makes the data error-free and brings accuracy and precision in the final results. 

Step 4: Data Processing

This step processes the data and analyzes it for various correlations, trends, and combinations to deliver valuable insights about the data. This is a very crucial step in the data analysis process, and generally, businesses use advanced data analytics tools to complete this step.  Data visualization feature is used in this step to make the data analysis easy to understand. 

Step 5: Data Interpretation

This is the final step of data analysis. The analyzed data is set for meaningful conclusions, findings, and interpretations. Data is interpreted for valuable analytics and solutions that can be applied for future action based on the data analysis results. 

• Collecting real-time data
• Getting rid of outdated data 
• Presenting data in an understandable format
• Managing data from multiple sources
• Dealing with poor quality data
• Conducting data analysis accurately
• Shortage of skilled data analysts
• Maintaining the security and privacy of the organizational data
• Sharing the analytics in real-time with team members
• Another challenge is to incorporate data-driven insights into daily workflows 

Data analysis software is an application that helps in collecting, merging, cleaning, analyzing, and interpreting data.  It also has features to present data in a visually appealing format. Data analysis software is used by managers, data scientists, statistical analysts, data analysts, etc. for reliable data analysis and insights. 

Desktop-based/On-Premise 

These types of data analysis software solutions are licensed, and businesses can install them within their IT infrastructure. On-Premise, data analytics systems are highly customizable in nature.  

Web-based

These types of online data analysis software are hosted, and third-party data analysis solution providers offer these tools. Web-based online data analysis tools are very popular applications as they don’t require any hardware installation and can run online with a login ID and internet connectivity. 

Cloud-based

These types of data analysis software solutions are hosted through third-party cloud service providers. These are popular with large organizations that require big data analytics, massive data storage capacity, and extreme flexibility. 

Open Source

These types of data analysis software allow users to customize the source code, alter inbuilt settings, and modify functions by adding extra plugins. When organizations need tailored solutions that are unavailable with any vendor, then open source data analysis software can be opted. 

Data analysis software has the features to carry all the steps of the data analysis process.  Apart from this, the other benefits are:

Timely decision making:

Unlocking the potential value of data requires analytics. Businesses can improve their knowledge and understanding of business processes, customers, campaigns, products, finances, employees, etc. through data analysis processes. Businesses can unveil the value hidden in data with the help of a data analysis software that removes the guesswork and provides key metrics required for better decision making. Data analysis software helps organizations make reliable decisions to meet customer expectations, mitigate global challenges, and stay competitive.

Provides valuable insights:

Without insights, data is of no use. Organizations that know what to do with data can capitalize it for long-term success.  Continuous data analysis with real-time data provides businesses space and time to tailor services, alter campaigns, modify strategies, and optimize current processes to improve results. 

Storing and retrieving data easily:

With quintillions Gigabytes of data produced every year in the world, storing and retrieving data from multiple sources and databases is a challenging task. Pre-planned data analysis programs with the help of analytics software make it easy to store and retrieve data. Data analytics applications support all types of databases and integrate with them smoothly to import data whenever required.  

Optimizes various businesses activities:

Using a business analysis tool, various critical business activities such as budgeting, hiring, scheduling, advertising, inventory management, customer service, etc. can be optimized and improved.

Proper planning and execution: 

Data software helps managers plan their course of action realistically and more accurately by providing essential information about the resources and time. Managers can forecast future actions from multiple perspectives and create a flexible but effective strategy that is easy to execute. 

Saves Time:

The amount of data that current-day businesses generate daily is too huge to manage manually. It is humanly not possible to study the data manually for insights. Therefore, data analysis software rescues managers from menial responsibilities and empowers them to use the data analysis application's insights directly.

Solves problems and eliminates errors:

Many times, businesses face issues of data mismatch, fraudulent transactions, errors in processes, etc. A robust data analysis software can help businesses to identify the leakages by analyzing the granular data.

Importing data

Data analysis software allows users to import data from various file formats such as CSV, excel sheets, HTML, etc. to the software for analysis. You can also import data from these sources to tables of the data analysis software. The data analysis software supports all types of databases, including NoSQL and SQL databases. Data stored in cloud servers or local databases can also be imported. 

Data synchronization

For regular data analysis, users can set periodic or real-time synchronization of the data between the data analysis software and the database of their choice. By enabling real-time synchronization, data will get uploaded to analytics software automatically.  

Importing data from popular applications

Most advanced data analysis software supports popular business applications such as CRM, Sales, Marketing, Project Management, etc. for direct database collaboration and faster analysis. 

Data blending

Data analysis software allows users to merge data from two applications or sources to create meaningful results. 

Data Visualization tools

Data analysis software has inbuilt data visualization tools to make a variety of charts, pivot tables widgets, graphs, tables, etc.  It also creates insightful reports and custom dashboards with the intuitive drag and drop interface. Other options, such as insert images, format text, create a design layout, drill-downs, contextual filters, and many more, help in visualizing data effectively. Some data analysis software come with interactive map integration to visualize your data geographically. 

Various analytics and reporting tools

Data analysis software is capable of in-depth analysis of various metrics and key performance indicators. It comes with extensive analytical capabilities and core functions such as forecasting, projection, and reporting.

Sharing and collaboration

Data analysis software enables managers to share real-time data analytics with their team for informed decision making and teamwork. 

Data security tools

Data analysis software comes with inbuilt encryption, firewalls, regular backups, etc. features for security and data usage privacy. 

Other Features: 

• Set Data alerts
• Set access control
• Inbuilt business intelligence

Augmented analytics

Augmented analytics uses machine learning, NLP (Neurolinguistics Programming), and artificial intelligence to gather business analytics. Augmented analytics produces faster results, deeper insights, and speedy visualization. 

Conversational analytics

Conversational analytics uses processes such as textual data mining, sentiment analysis, and social listening to derive insights about consumer behavior and satisfaction.

Data-Driven Culture

Incorporating data-driven culture into organizations is an important trend in the data analytics world. Business Intelligence professionals and managers are prioritizing the implementation of a culture where decisions are based on data-driven insights.  

Data Analysis Automation

Organizations are implementing automated processes to handle massive data sets. Automated data analysis and automated decision making are popular trends in data analytics

Graph analytics

Graph analytics is the process of analyzing data in a graph format. It requires managers to analyze data with a database platform that supports graphs format. Graph analytics is getting traction for solving complex problems. Graph analytics is used for revealing relationships between logical concepts and people, organizations, and related entities. 

Continuous Intelligence

Continuous intelligence (CI) uses real-time data to enable automated decision-making. This technique generates continuous insights and real-time analytics for decision-making. In June 2020, when oil prices went below zero levels, most of the data analysis systems struggled to make trading decisions as data analysts never thought that oil could go below zero levels. In such scenarios, a continuous intelligence method has a greater advantage over other data feeding algorithms as it continuously updates its data feeds to automatically make real-time decisions. 

Big data analytics

With billions of connected devices, the data created by these is massive and overwhelming. Big data analytics attempts to map this data for meaningful insights. There is a growing trend among organizations to utilize big data for providing personalized services to their customers.

Financial Sector

Banks and NBFCs can deploy data analytics software to become hyper-intelligent organizations that provide personalized customer experiences and complete regulatory compliance for its services. Data analysis helps financial organizations in the early detection of frauds, risk management, operational excellence, and increasing engagement with customers through personalized offers. Even Insurance and Mutual funds companies can provide personalized offers to their clients based on their preferences. The financial sector can use big data analytics to predict future market trends. 

Healthcare

Data integration and systematic analysis of data with the help of data analytics software enable improved clinical decisions. It also helps prevent healthcare financial frauds and improves patient satisfaction.  Data analysis of various medical, and wearables devices such as Fitbits helps physicians to monitor their patient's health remotely based on the analysis of data received from these devices.  

Manufacturing

Manufacturing data analytics solutions enable managers to balance production operations for cost reduction and risk management. In manufacturing, advanced analytics helps operations managers gain insights from historical processes to optimize the current ones for better yields. Companies can also capitalize on big data to optimize their manufacturing cycles. 

Retail 

Data analysis software can be used to optimize retail operations, merchandise planning, controlling the supply chain, and making the customer journey a pleasant experience. Using past data, big data analytics, and AI, retail businesses can predict customers' future behavior, develop personalized marketing campaigns, and improve employee training. 

Education

Educational institutes can improve learning environments, teaching techniques, and learning outcomes by deploying data analytics techniques. Data received from students' feedback, parents' feedback, evaluation, results, etc. can be analyzed to improve the learning process and reduce dropout rates. 

Logistics and Transportation

Data analysis enables Transportation and Logistics Companies to understand the reasons behind shipping delays and performance across various shipping routes. Using such insights, logistics companies can optimize delivery times and reduce costs. 

• Does it provide all features, functionality, integrations, and settings for your analysis needs? 
• Is the data analysis software user-friendly? 
• Can you customize the data analysis software or request to add more functions over time?
• Does the data analytics application deploy advanced security features such as encryption, firewall, access controls, backup plans, etc. ?
• Can it work with all types of databases to retrieve your data?
• Is your data analysis software vendor well-experienced in the data analysis field?
• Does the software provider offer a free trial/free demo of its services?
• Are you going to get free live training/demo or customer support services from the software provider?

If the answer is 'yes' to above all questions, then you have all green ticks to go ahead and purchase the software. 

The costs of the data analysis system depend on a large number of factors such as:

• The number of users
• Features and functions it offers
• The amount of data it can process in a given time frame
• The maximum data analyzing capacity
• Quality of service offered 
• Integrations it offers
• User interface and experience

These variations can change the cost factor and estimations of the data analysis software significantly.  

The enterprise version of Qlik Sense will cost you $70 a month per user, while the basic version is available for $30 per user per month. Parse.ly data analysis software will cost $1500 per month for its Growth plan and $500 per month for the Starter plan. Freemium data analysis software such as Cluvio comes with a free version for single users with a maximum of three dashboards. Zoho Analytics also offers a freemium version with a free plan for two users, while enterprise plans for 50 users will cost $495.  If you need more free data analysis software, try Anaconda, Kyubit, AnswerDock, and MeMSQL. 

For other online data analysis tools such as SAS, Stata, Solver Suite, WebFocus, you need to contact the vendor for detailed pricing. You can get the list and all information from the Goodfirms data analysis software Page.

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