Learn More About Statistical Analysis Software
What is Statistical Analysis Software?
Statistical analytics tools inform data-driven decisions, whether that be in a research or business context. Enterprises have started to recognize the importance of accessing and combining all business-critical data to get a better understanding of how a company performs. Connecting disparate systems to make data consumable is a complex process, but once done, businesses are enabled to utilize statistical analytics solutions along with other data analysis techniques. This helps them visualize and interpret the data in an easily digestible manner. A business cannot transform into a digitally native enterprise until it uses its data to make intelligent decisions. These data-driven decisions provide an important edge and help separate a business from its competitors.
Medium and large companies are facing unprecedented challenges when managing and analyzing data. Some of these challenges are the exponential growth of the volume of enterprise data and its increasing complexity caused by new technologies like social media and the internet of things (IoT). To address these challenges, companies need to gather and analyze large volumes of data, both structured and unstructured, from different sources. Companies need to find trends and patterns in historical data while identifying future opportunities and risks. They also cannot afford to spend a lot of time analyzing data which loses its relevance quickly. Advanced statistical procedures and visualization provide a robust, user-friendly, and integrated platform to understand business data and solve complex business and research problems.
What Types of Statistical Analysis Software Exist?
Statistical analysis comes in many different flavors with each type being appropriate or beneficial for different use cases. Although the methods are many, the ultimate goal of the analysis is broken into three types: descriptive, prescriptive, and predictive. Descriptive analysis simply looks at the data as it is, but does not refer to or recommend any future outcome. Prescriptive analysis uses historical data to recommend a particular outcome. Finally, predictive analysis uses historical data to predict future data or outcomes. Apart from these broad methods, statistical analysis software can perform the following types of analysis:
Regression analysis
This type of analysis allows for conducting various regression methods such as ordinary least squares (OLS), weighted least squares (WLS), or generalized linear model (GLM).
Predictive analytics
Predictive analytics mines and analyzes historical data patterns to predict future outcomes by extracting information from data sets to determine patterns and trends. Using a range of statistical analysis and algorithms, analysts use predictive analytics products to build decision models which business managers use to plan for the best possible outcome.
Survival analysis
This type of analysis allows for the evaluation of durations, events, and reliability in relation to statistical analysis.
Time series analysis
Time series analysis allows users to identify patterns within massive, continuous time series data sets to perform reporting, forecasting, and predictive analysis.
Bayesian analysis
This method of statistical inference allows one to combine prior information about a statistical parameter with evidence from information contained in a sample to guide the statistical inference process.
Decision trees
One common type of classification model is a decision tree. These models predict a number of possible outcomes based on a variety of inputs. For example, if a sales team builds $1 million in pipeline, they can close $100,000 in revenue, but if they build $10 million in pipeline, they should be able to close $1 million in revenue.
What are the Common Features of Statistical Analysis Software?
Statistical analysis tools allow users to mine and analyze structured or unstructured data. Through the process of analysis, data sets and visualizations are created from the compiled data.
Data preparation: To analyze the data, it must be properly cleaned and should be of high quality. This preparation consists of deduplication, cleansing, and appending the data for statistical analysis. Not all analysis is achieved at the surface. Instead, robust statistical analysis tools mine data from databases and prepare it for analysis.
Data sampling: Data sampling allows users to select samples of data for defined procedures.
Statistical modeling: Statistical modeling involves creating a mathematical model that embodies a set of statistical assumptions concerning the creation of sample data which may be used to get a better idea about the makeup and distribution of the data.
Hypothesis testing: Statistical analysis tools often provide hypothesis testing features to ensure the analysis is consistent with the data and correct based on predetermined factors. This helps the researcher, data scientist, or statistician evaluate the outcome based on their initial hypothesis.
Data visualization: In addition to numerical inputs and outputs, statistical analysis software frequently allows the user to visualize results by means of graphs, charts, and reports. This not only helps the end user better understand them but also aids with communicating these results with the broader company.
What are the Benefits of Statistical Analysis Software?
Irrespective of the type of complex data manipulation or analysis being performed, statistical analysis tools enable statisticians or business professionals to find correlation, regression, analysis of variance, and more with ease. With these insights in hand, businesses can make sense of their data and make informed decisions based on it. These tools help identify duplicates and unusual cases that may arise during data cleaning and data curation and get detailed data analysis reports.
Manage large volumes of data: Statistical analysis software gives one the ability to easily prepare, blend, and analyze company data using a repeatable workflow, then deploy and share analytics at scale for deeper insights.
Gain insight into company data: Through data mining and statistical analysis, especially when coupled with other technology such as machine learning, data professionals are able to gain insights into data.
Better understand potential outcomes and scenarios: With statistical analysis, especially of the predictive variety, businesses better understand how different variables will affect them and are able to plan accordingly.
Who Uses Statistical Analysis Software?
Data analysts: Depending on the complexity of the software, it is likely that analysts will be required. They can help set up the requisite queries, dashboards, and notebooks for other employees and teams. They can create complex queries inside the platforms to gather a deeper understanding of business-critical data.
Consultants: Businesses do not always have the luxury to build, develop, and optimize their own analytics solutions. Some businesses opt to employ external consultants, such as business intelligence (BI) consulting providers. These providers seek to understand a business and its goals, interpret data, and offer advice to ensure goals are met. BI consultants frequently have industry-specific knowledge alongside their technical backgrounds, with experience in healthcare, business, and other fields.
What are the Alternatives to Statistical Analysis Software?
Alternatives to statistical analysis software can replace this type of software, either partially or completely:
Analytics platforms: Analytics platforms might include statistical analysis, but are broader-focused tools that facilitate the following five elements: data preparation, data modeling, data blending, data visualization, and insights delivery.
Big data analytics software: Big data analytics software connects to Hadoop or proprietary Hadoop distributions to better understand structured and unstructured data. These same data sources may be important for data scientists who are tasked with building predictive models.
Log analysis software: Businesses that are focused on log data might benefit from deploying log analysis software, which is used to analyze log data from applications and systems. It should be kept in mind that this software is much more limited in terms of data types and data sources to which it can be connected to. However, since log analysis software focuses on logs, it frequently provides more granular details around log-related data.
Stream analytics software: When one is looking for tools specifically geared toward analyzing data in real time, stream analytics software is a go-to solution. These tools help users analyze data in transfer through APIs, between applications, and more. This software can be helpful with IoT data, which one frequently wants to analyze in real time.
Predictive analytics software: Broad-purpose statistical analysis software allows businesses to conduct various forms of analysis, such as prescriptive, descriptive, and predictive. Businesses that are focused on looking at their past and present data to predict future outcomes can use predictive analytics software for a more fine-tuned solution.
Text analysis software: Statistical analysis software is focused on structured or numerical data, allowing users to drill down and dig into numbers to inform business decisions. If the user is looking to focus on unstructured or text data, text analysis solutions are the best bet. These tools help users quickly understand and pull sentiment analysis, key phrases, themes, and other insights from unstructured text data.
Software Related to Statistical Analysis Software
Related solutions that can be used together with statistical analysis software include:
Data warehouse software: Most companies have a large number of disparate data sources, so to best integrate all their data, they implement a data warehouse. Data warehouses can house data from multiple databases and business applications, which allows BI and analytics tools to pull all company data from a single repository. This organization is critical to the quality of the data that is ingested by analytics software.
Data preparation software: A key solution necessary for easy data analysis is a data preparation tool and other related data management tools. These solutions allow users to discover, combine, clean, and enrich data for simple analysis. Data preparation tools are often used by IT teams or data analysts tasked with using BI tools. Some BI platforms offer data preparation features, but businesses with a wide range of data sources often opt for a dedicated preparation tool.
Machine learning software: Machine learning algorithms are a key component of building effective statistical models. Many machine learning algorithms are built to provide recommendations or suggestions which is particularly relevant for predictive analytics.
Challenges with Statistical Analysis Software
Software solutions can come with their own set of challenges.
Need for skilled employees: Statistical analysis software is not necessarily simple. Often, these tools require a dedicated administrator to help implement the solution and assist others with adoption. However, there is a shortage of skilled data scientists and analysts that are equipped to set up such solutions. Additionally, those same data scientists will be tasked with deriving actionable insights from within the data.
Without people skilled in these areas, businesses cannot effectively leverage the tools or their data. Even the self-service tools, which are to be used by the average business user, require someone to help deploy them. Companies can turn to vendor support teams or third-party consultants to assist if they are unable to bring someone in house.
Data organization: To get the most of analytics solutions, that data needs to be organized. This means that databases should be set up correctly and integrated properly. This may require building a data warehouse, which can store data from a variety of applications and databases in a central location.
Businesses may need to purchase a dedicated data preparation software as well to ensure that data is joined and is clean for the analytics solution to consume in the right way. In the context of big data, a company might want to specifically consider big data processing and distribution software. This often requires a skilled data analyst, IT employee, or an outside consultant to help ensure data quality is at its finest for easy analysis.
User adoption: It is not always easy to transform a business into a data-driven company. Particularly at more established companies that have done things the same way for years, it is not simple to force analytics tools upon employees, especially if there are ways for them to avoid it. If there are other options, such as spreadsheets or existing tools that employees can use instead of analytics software, they will most likely go that route. However, if managers and leaders ensure that analytics tools are a necessity in an employee’s day to day, then adoption rates will increase.
Which Companies Should Buy Statistical Analysis Software?
In the digital age, data is ubiquitous. Whether it is higher education or the healthcare industry, it is likely that a lot of data is involved. Statistical analysis software tools are typically used by data scientists and mathematicians, but it provides industry-specific features. These may be features tailored to scientific research, cost modeling, or health science.
Higher education: Users from the higher education industry use statistical analysis tools to analyze data, obtain insights quickly, and find relevant trends in data.
Market research firms: Researchers turn to statistical analysis to solve tough research problems. They are able to use these tools to dig into their research findings and find historical trends. With statistical analysis software, market researchers are enabled to handle a large volume of data in multiple formats.
Health care: For health care professionals, time isn’t just money—it also means the difference between life and death. These workers look to statistical analysis tools to better understand health-related data and to improve efficiencies in hospitals, manage and contain diseases, and much more.
How to Buy Statistical Analysis Software
Requirements Gathering (RFI/RFP) for Statistical Analysis Software
If a company is just starting out on their analytics journey, g2.com can help in selecting the best software for the particular company and use case. Since the particular solution might vary based on company size and industry, G2 is a great place to sort and filter reviews based on these criteria, along with many more.
The variety, volume, and velocity of data are vast. Therefore, users should think about how the particular solution fits their particular needs, as well as their future needs as they accumulate more data.
To find the right solution, buyers should determine pain points and jot them down. These should be used to help create a checklist of criteria. Additionally, the buyer must determine the number of employees who will need to use this software, as this drives the number of licenses they are likely to buy.
Taking a holistic overview of the business and identifying pain points can help the team springboard into creating a checklist of criteria. The checklist serves as a detailed guide that includes both necessary and nice-to-have features including budget, features, number of users, integrations, security requirements, cloud or on-premises solutions, and more.
Depending on the scope of the deployment, it might be helpful to produce a request for information (RFI), a one-page list with a few bullet points describing what is needed from a statistical analysis software.
Compare Statistical Analysis Software Products
Create a long list
From meeting the business functionality needs to implementation, vendor evaluations are an essential part of the software buying process. For ease of comparison after all demos are complete, it helps to prepare a consistent list of questions regarding specific needs and concerns to ask each vendor.
Create a short list
From the long list of vendors, it is helpful to narrow down the list of vendors and come up with a shorter list of contenders, preferably no more than three to five. With this list in hand, businesses can produce a matrix to compare the features and pricing of the various solutions.
Conduct demos
To ensure the comparison is thoroughgoing, the user should demo each solution on the shortlist with the same use case and data sets. This will allow the business to evaluate like for like and see how each vendor stacks up against the competition.
Selection of Statistical Analysis Software
Choose a selection team
As statistical analysis software is all about the data, the user must make sure that the selection process is data driven as well. The selection team should compare notes and facts and figures which they noted during the process, such as time to insight, number of visualizations, and availability of advanced analytics capabilities.
Negotiation
Just because something is written on a company’s pricing page, does not mean it is not negotiable (although some companies will not budge). It is imperative to open up a conversation regarding pricing and licensing. For example, the vendor may be willing to give a discount for multi-year contracts or for recommending the product to others.
Final decision
After this stage, and before going all in, it is recommended to roll out a test run or pilot program to test adoption with a small sample size of users. If the tool is well used and well received, the buyer can be confident that the selection was correct. If not, it might be time to go back to the drawing board.
What Does Statistical Analysis Software Cost?
Businesses decide to deploy statistical analysis software with the goal of deriving some degree of a return on investment (ROI).
Return on Investment (ROI)
As they are looking to recoup their losses that they spent on the software, it is critical to understand the costs associated with it. This software is typically billed per user, which is sometimes tiered depending on the company size. More users will typically translate into more licenses, which means more money.
Users must consider how much is spent and compare that to what is gained, both in terms of efficiency as well as revenue. Therefore, businesses can compare processes between pre- and post-deployment of the software to better understand how processes have been improved and how much time has been saved. They can even produce a case study (either for internal or external purposes) to demonstrate the gains they have seen from their use of the statistical analysis tool.
Implementation of Statistical Analysis Software
How is Statistical Analysis Software Implemented?
Implementation differs drastically depending on the complexity and scale of the data. In organizations with vast amounts of data in disparate sources (e.g., applications, databases, etc.), it is often wise to utilize an external party, whether that be an implementation specialist from the vendor or a third-party consultancy. With vast experience, they can help businesses understand how to connect and consolidate their data sources and how to use the software efficiently and effectively.
Who is Responsible for Statistical Analysis Software Implementation?
It may require a lot of people, or many teams, to properly deploy an analytics platform. This is because data can cut across teams and functions. As a result, it is rare that one person or even one team has a full understanding of all of a company’s data assets. With a cross-functional team in place, a business can piece together its data and begin the journey of analytics, starting with proper data preparation and management.
Statistical Analysis Software Trends
Self service
As with other types of analytics tools (such as analytics platforms), there is an increasing trend for software to be of the self-service nature. This means that non professionals should be able to use the tool easily with little to no IT support for setting it up. With drag-and-drop interfaces or highly customizable setups, average business users are being empowered by statistical analysis capabilities.
Augmented analytics
Artificial intelligence and machine learning are making inroads across most industries and business use cases, with statistics being no exception. With machine learning powering statistical analysis, users are able to discover data, determine the best type of analysis to deploy for a particular data set or problem, and more. With augmented analytics, analysts can get a better understanding of their data, allowing them to drill in and dive deeper into trends and patterns. For example, the software might alert the user to an anomaly in the data which they can look into in order to rectify the issue or inform relevant individuals about it.
