What separates the top-performing companies from the pack? A winning approach to data with a focus on strategy, modern business analytics tools and a consistent push for data literacy with a “when you know better, do better” mindset.

Make data work for you by getting to know it better. This article discusses the types of analytics with software examples and use cases.

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What This Article Covers

• What Is Data Analytics?
• Market Data Maturity Trends
• Descriptive Analytics
• Diagnostic Analytics
• Predictive Analytics
• Prescriptive Analytics
• Software
• Use Cases
• Platform Comparison
• Software Selection Strategy
• Next Steps

What Is Data Analytics?

Data analytics is the systematic collection and thorough examination of information for corporate decision-making, research and academics.

Data is a guiding tool for business analytics, informing everything from line-of-business to strategic planning. It involves gauging business performance by systematically exploring sales, revenue, profit and cost figures.

Data analysis helps quantify success and identify improvement areas.

Types

Awareness of the types of analytics puts data results into perspective, whether you’re a business user, report consumer, student or business owner.

As a buyer, it can help you select the right software.

• Descriptive analytics tells you what happened.
• Diagnostic analytics informs why it happened.
• Predictive analytics conveys what’s likely to happen.
• Prescriptive analytics helps you decide what to do next.

There are several data analytics software catering to enterprises and students.

In keeping with market trends, vendors offer predictive analytics with their BI and reporting tools, or they may launch dedicated modules. Prescriptive analytics is new and niche, so it’s available with a few leading software.

• Data analytics solutions may include but aren’t limited to statistical, visualization, dashboarding and BI tools.
• Out-of-the-box data analytics tools may serve students and researchers, but business enterprises need tailor-made solutions.
• Enterprise data analytics tools may be a single software program or a suite of tools.

Market Data Maturity Trends

Does everyone do data analytics? Not quite and not well enough.

According to a 2019 McKinsey survey, the market includes an equal mix of top-performing companies and stragglers, and this gap is growing. Many companies fail to achieve data maturity because they’re in a hurry and want results fast.

Data analytics can’t be a knee-jerk reaction to lagging performance and missed milestones — it requires going all in and staying there.

As Alation cofounder and CEO Satyen Sangani says, “Companies that invest in data and build a culture of data literacy do well. Those that don’t fall behind.”

The survey highlights another interesting fact. Respondents from top-grossing companies are three times more likely to credit analytics for at least 20% of their earnings.

A three-pronged strategy differentiates the leaders.

• They prioritize long-term data and analytics strategies.
• They push for data literacy.
• They arrange for the right technology at the right time.

Are you there yet?

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Descriptive Analytics

It’s the fundamental data analysis stage, directly reporting metrics in their original form. The process entails extracting critical insights about your organization’s operations and transactions.

What are the cash flow trends this year? Are we on track with the manufacturing goals for this month? Answering such questions with ad hoc reporting counts as a descriptive analysis technique.

• Data visualization involves using tables, charts, graphs and plots in these reports.
• Calculations involved in descriptive analytics include mean, median, mode, range, variance, standard deviation and measures of position and frequency.
• Every data analytics tool will generate simple to complex reports on demand. Excel, Google Sheets and spreadsheet tools are examples.

Diagnostic Analytics

It’s the process of identifying the reasons for patterns in market trends or organizational metrics. It involves determining the correlations between various metrics and how they impact the business.

Footfall in certain stores is dropping consistently. Why?

Your marketing campaign is a hit. What are the takeaways?

Diagnostic analysis techniques like data mining, exploratory analysis and statistical calculations can help spot the reasons.

• Data mining tools provide specialized techniques for extracting relevant information from millions of data points.
• Exploratory data analysis (EDA) involves studying mined data to surface dataset correlations and testing your hypotheses using visualizations.
• Statistical techniques include calculating the mean, median, mode and skewness.
• Advanced diagnostic methods include correlation and causation, principal component analysis (PCA), probability theory, and sensitivity and time series analysis.

Predictive Analytics

It’s the process of building, training and applying predictive models using machine learning (ML). Forecasting requires analyzing time-series data to determine what to expect based on historical trends.

• Benchmarking involves curating a standard dataset representing the solution and backtracking with regression to establish a baseline performance goal.
• Create a predictive model, feed it sample data and validate it against the benchmark dataset.
• Do it enough times, and you can train the model to forecast the outcome. It’s why you need to create and automate a data flow that does it continually. Automated workflow authoring is a critical predictive software requirement for accelerated analytics.
• You can generate up-to-date predictions by scoring incoming data against this predictive model.

Can you get by without learning Python, R and JavaScript? Programming language skills give you a competitive edge, particularly when troubleshooting glitchy queries and workflows.

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Prescriptive Analytics

It’s the process of deciding upon the best course of action using machine learning models. Like predictive modeling, prescriptive analytics is about training ML algorithms with sufficient data to generate automatic recommendations.

Optimization is a primary prescriptive technique to generate the best way forward. Though new, industries across the spectrum find it helpful, including manufacturing, supply chain management, retail and marketing.

Prescriptive analytics helps retailers save costs and provide a satisfying user experience with effective inventory and warehouse management software.

Scenario: Retail Management

• Objective: A retailer wants to minimize production, transportation and holding costs while meeting the predicted demand.
• To Decide: Production and allocation quantities.
• Action Items: They must decide how much stock to produce and allocate by store.
• Constraints: The production quantity must not exceed the warehouse capacity. At the same time, it must meet or exceed the predicted demand for each store.
• Available Information: It includes the number of warehouses and stores, the predicted demand by store, and costs of production, conveyance and storage.
• Example Values
  • Warehouse X capacity: 1200 units
  • Warehouse Y capacity: 1000 units
  • Store A demand for Product 1: 250 units
  • Store B demand for Product 1: 350 units
  • Store C demand for Product 1: 200 units
  • Store D demand for Product 1: 300 units
  • Store A demand for Product 2: 150 units
  • Store B demand for Product 2: 200 units
  • Store C demand for Product 2: 150 units
  • Store D demand for Product 2: 250 units
  • Production cost per unit: $9
  • Transportation cost per unit from distribution center to store: $1.5
  • Holding price per unit per period: $0.3
• Result: The objective is to satisfy product demand without exceeding the holding capacity. The prescriptive analytics model uses integer programming to calculate
  • The optimal production quantities for each warehouse
  • The allocation amounts from a warehouse to each store by item

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Software

This section briefly introduces software solutions for each type of data analytics with their notable features.

Descriptive Analytics Systems

Microsoft Power BI is a software suite with separate modules for preparing, querying and reporting data. Its Quick Insights and Analyze features allow natural language queries for data exploration.

Buyer reporting enables segmentation and cohort analysis. It supports real-time data streaming to monitor social media campaigns, incorporate IoT data and embed videos into dashboards.

Tableau is a business intelligence tool known for its interactive visualizations on its online platform, Tableau Public. It’s a performant software suite capable of handling massive datasets and complex calculations.

Enterprises require Creator licenses to generate and publish content. Tableau Server is part of the Creator module and enables controlling published content from a centralized hub.

Read our Power BI vs. Tableau article to learn more.

Diagnostic Analytics Tools

SAS/STAT is the statistical modeling component of SAS Viya, the vendor’s data management, visualization and analytics platform. The program is for data engineers, statisticians and business analysts out of the box.

The vendor bundles it into a business-friendly analytics solution with a web-based programming interface, SAS Studio. You can build R, Python and Jupiter models and run them on SAS Cloud Analytics Services.

IBM SPSS is statistical software with drop-down menus and a command syntax language, 4G, for linear and non-linear modeling, simulation modeling, regression and Bayesian calculations.

Initially, the software was a social sciences tool for academics, marketing and health research. Its direct Marketing module helps launch and test marketing campaigns, upsell and cross-sell. Propensity scoring and cluster analysis enable lead profiling.

Read our Best Statistical Software article for more.

Predictive Analytics Platforms

Minitab was a teaching tool for statistics students. Available on the desktop and cloud, it provides statistical analysis capabilities through the command line and drop-down menus.

With a click-friendly interface, Minitab enables rapid reporting and senior-level decision-making. Report cards highlight anomalies, and graphical summaries aid data interpretation.

Its Assistant feature automates complex workflows, including powerful statistical functions and guided analysis. A 30-day free trial with full functionality is available.

TIBCO Data Science is a software suite with pipeline authoring and deployment modules. The modules include TIBCO Statistica, Spotfire Data Science (Team Studio) and TIBCO Enterprise Runtime for R (TERR).

You can write and integrate R code into TIBCO using Jupyter Notebooks or its built-in drag-and-drop actions. TERR is a highly performant statistical engine for predictive analytics.

Prescriptive Analytics Software

IBM Decision Optimization helps businesses find the best solutions to complex problems. Scenarios include figuring out the best delivery routes and making manufacturing processes cost-effective.

Optimization can help doctors choose the proper treatment for patients. Automated data preprocessing, feature selection and model tuning accelerate decision-making. You can build, train and share models with others while maintaining data integrity.

RapidMiner has a visual workflow builder with many machine learning algorithms and predictive analytics features. It supports data preparation with over 1500 transformation operators. Its Model Simulator enables optimization via regression analysis.

The software is extensible via open APIs, and the RapidMiner Marketplace offers additional functionality. With PMML support, RapidMiner is open to predictive model import/export from external systems.

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Use Cases

Descriptive Analytics

As a data analyst at an eCommerce company, your task is to suggest sales process improvements by analyzing the conversion funnel.

Scenario: Sales are dropping, but website metrics say visits are high. Your manager wants you to devise ways to increase conversions by identifying bottlenecks in the user journey.

Steps

1. Collect Information: Obtain metrics on website visits, product views, cart additions and completed purchases from the company’s CRM system, sales database and Google Analytics.
2. Prepare Data: Clean and blend user data across the website to get a clear picture.
3. Analyze
   1. Visualize the Funnel: Using descriptive analytics techniques, create visualizations for the various funnel stages — landing page visits, item views, additions to cart and completed purchases.
   2. Analyze Historical Insights: Compare the current metrics to previous data. At which funnel stages did customers drop off noticeably?
   3. Evaluate Website Traffic: Which sources drive the most traffic — organic search, social media or paid ads? How does buyer behavior differ within the funnel based on their entry point?
   4. Flag Abandonment Points: Identify items, pages or workflows that cause users to drop off.
   5. Check Location Data: Factor in the user’s geographical placement to identify if it’s a localization issue in your application or website.
4. Present the Results: Discuss your findings with the team, highlighting the drop-off stages, changes in user behavior and the most profitable traffic sources.
5. Provide Actionable Recommendations: Suggest potential solutions like personalized outreach via email, push notifications and messaging, or more intuitive checkout workflows.
6. Learn and Apply: Monitor the conversion rates after solution deployment. Update your observations consistently with real-time data.

Diagnostic Analytics

Root cause analysis is critical to add value to improvements. You can perform a deep-dive analysis to identify improvement areas and devise data-driven solutions.

Scenario: Regarding the above example, let’s assume your manager wants you to uncover why customers move away without completing the purchase.

Steps

1. Identify the Churn Stage: From the descriptive insights, flag funnel stage(s) where users viewed items but didn’t add them to the cart.
2. Segment User Groups: Perform user segmentation based on device, visited pages and buying history. Is the drop-off rate higher for mobile users? What are the buying trends for first-time and returning buyers?
3. Analyze Buyer Behavior: Check the recorded user activity in the problematic funnel areas. Did they abandon specific items? Could slow-loading pages be the issue?
4. Perform A/B Testing: Apply and test different solutions. Using different page layouts can help resolve frustrating navigation issues.
5. Conduct Competitor Analysis: Try out and test the conversion funnels of your competitors. What are the takeaways? Does your product bundling strategy need a fresh look?
6. Analyze User Feedback: Evaluate surveys, feedback forms, and sales and support calls from your CRM database. Recurring feature complaints can indicate glitchy features.
7. Recommend Solutions: Based on your findings, share actionable suggestions like making the mobile app more user-friendly or improving product descriptions.
8. Learn and Improve: Track how the solution impacts conversions and keep an updated record of the changes.

Predictive Analytics

Business scenarios are dynamic, and systems ingest new information all the time. The market is constantly in flux, and analytics objectives are a moving target.

Decision variables and limitations change constantly, and predictive analytics helps you manage expectations and stay in control. Hardcore data takes the guesswork out of forecasting.

Serco is a bike-sharing service and partners with IBM to devise a cost-effective solution for managing 800 bike stations and 12,000 bikes in London. Using IBM Decision Optimization and machine learning, they can track routes, anticipate potential issues and schedule bike allocation.

It helped them optimally distribute bikes to each station and schedule repairs without affecting services. The company recorded a 15% decrease in replacement and repair costs and a 10% increase in productivity for the users.

Prescriptive Analytics

Guided marketing, selling and pricing aren’t the only benefits of prescriptive modeling. Prescriptive systems support aviation, transport, IT, business analytics and planning, besides telling who to sell to, what to sell and how much to charge.

Inputting business rules into machine learning algorithms can help you manage risk, particularly in the banking and financial sectors.

Scenario: A bank struggles with its loan approval processes. Delays, frustrated customers and operational overheads make it a rough experience for staff and clients.

Objective: The bank wants to improve its loan workflows while managing risk.

Steps

1. Gather Data: Collect information from loan application forms, credit scores, financial statements, employment history and past loan approval records.
2. Anticipate Risk: Predict the probability of an applicant defaulting on payment using ML algorithms. Calculate risk using credit scores, income, employment history and the purpose of the loan.
3. Design Loan Approval Workflow: Model an optimized loan approval workflow by incorporating the bank’s rules and policies.
4. Deploy a Decision Engine: Implement a real-time decision engine that recommends approving, denying or further reviewing the loan application based on risk assessment and bank rules.
5. Automate the Workflow: Delegate routine tasks like document verification and background checks to software. This step isn’t exclusive to prescriptive analytics but can be part of a prescriptive solution.
6. Allocate Resources: Ask your prescriptive solution for suggestions on task delegation to improve productivity and outcomes.
7. Ensure Compliance: Automated ML workflows can ensure the data adheres to regulatory requirements and follows business rules. It’s not a core prescriptive task and can be part of any analytics pipeline.
8. Monitor and Improve: Continuous learning and improvement involve periodic model optimization based on real-time data.

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Platform Comparison

Here’s a comparison of the abovementioned software on four counts — ease of use, integration, scalability and customer support.

Power BI vs. Tableau

Features	Power BI	Tableau
Ease of Use	It has an intuitive interface, though new users may need training.	It’s user-friendly with interactive visualizations.
Integration Capabilities	Power BI connects to on-premise and cloud sources. These include web APIs, Salesforce, Dynamics 365, Oracle, JSON and text files.	Native and custom connectors are available. The platform supports webhooks and R integrations.
Scalability	Both Tableau and Power BI can handle large data volumes. Power BI allows data models of up to 400 GB.	Columnar storage accelerates data retrieval. Its Cloud and Online versions have a model storage limit of 100GB.
Customer Support	Microsoft and Salesforce offer excellent support for the two products, respectively.

SAS/STAT vs. IBM SPSS

Features	SAS/STAT	IBM SPSS
Ease of Use	SAS/STAT is more sophisticated, and new users may need training.	IBM SPSS is user-friendly and accessible, thanks to its no-code interface.
Integration Capabilities	SAS Integration Technologies combines an intelligence server with content delivery modules. These include a stored process server and a publishing framework.	IBM SPSS has R, Python, Microsoft .NET and Java integration plugins. It reads and writes data to ASCII files, spreadsheets and statistical software. APIs enable cloud system integration.
Scalability	SAS/STAT has 90 built-in statistical procedures for small and large datasets. The system lets you analyze incomplete data.	IBM SPSS supports big data volumes with massively parallel algorithms. It blends and aggregates large datasets using scalable servers.
Customer Support	SAS offers excellent support with responsive and knowledgeable teams.	IBM provides live phone and email support with training and certification.

Minitab vs. TIBCO Data Science

Features	Minitab	TIBCO Data Science
Ease of Use	Minitab is click-based and easy to use, thanks to the Minitab Assistant.	TIBCO’s Team Studio is designed for no-code workflow building.
Integration Capabilities	Minitab Connect is a cloud tool for data integration from diverse sources.	Spotfire is extensible and customizable, with embedding via a JavaScript API.
Scalability	According to online user reviews, it handles large datasets capably.	TIBCO Data Science Team Studio has over 44 new operators for the manufacturing domain. You can run Spark models directly within Team Studio using ModelOps.
Customer Support	Support is accessible, responsive and helpful.	Support is available from the Contact Us page on the vendor’s website. Training modules are available as support threads on the vendor’s support portal.

IBM Decision Optimization vs. RapidMiner

Features	IBM Decision Optimization	RapidMiner
Ease of Use	A Studio module enables the deployment of optimization models. You can build applications using the Optimization Center.	RapidMiner has a no-code, user-friendly interface.
Integration Capabilities	It integrates with Watson Studio to combine machine learning with optimization. The solution blends in with IBM’s Planning Analytics platform.	RapidMiner integrates with Python, R, Java, Groovy, SparkR and PySpark. It connects to Hadoop, Spark, Excel, social media, email, cloud systems and databases.
Scalability	The CPLEX Optimization Studio allows deploying optimization models. Additionally, you can build scenario management and visualization applications.	Being performant, Rapidminer conducts data prep and ETL within the database. A visual designer enables drag-and-drop workflow authoring, and the platform scales efficiently with Hadoop.
Customer Support	IBM Cloud documentation and Chat with Watson are available to Lite and Trial account users. Phone support is available for subscribers.	The vendor’s documentation portal provides community access, guides, and videos.

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Software Selection Strategy

Selecting a data analytics platform is a tightrope walk between budgetary considerations and your requirements.

Assess your needs and limitations internally and ask questions from stakeholders to gauge their expectations.

• Who will use the solution, and what are their technical skills?
• What tradeoffs are you willing to make?
• Does the solution connect to your preferred sources?
• What integration options are available?

Factor in the following overheads when calculating the total cost of ownership.

• Data migration
• Customization
• Additional features
• Plugins
• User accounts per license

Make an informed decision by supplementing your research with helpful resources. Follow our Lean Selection Methodology for systematic software selection.

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Next Steps

Awareness of the type of analytics is the first step — data literacy is the next. Skilled and knowledgeable workers are the mainstay of a successful business.

Establish a data culture at your workplace. Become a data evangelist, and highlight wins to amplify the message that data holds the key to winning opportunities and growth.

It’s time for a change if you’re struggling to make it work with a data analytics tool that doesn’t quite measure up. Get our free, interactive requirements checklist template to easily sort and list your preferred data analytics software features.

Which analytics techniques do you use at work? What’s your take on AI and machine learning for decision support? Let us know in the comments.