Every business uses some kind of analytic tool to help them analyze business data and extract commercially relevant information that they can use to improve and increase performance. Analytic tools were originally made to evaluate and discover areas of the business that are actually doing what they are being paid for—and some not living up to their hype.
Analytic tools produce a lot of differing information that need to be processed carefully lest one goes inferring about data which may or may not at all be affecting the current state of your business. Therefore, choosing the right tool to use and when to use them may prove a bit daunting of a task with the wide variety of tools available today.
This article is written to serve as a guide to aid your decision in choosing the right tools that works for your business. Another section of the article deals mostly with Web analytic tools to help you stay competitive amidst the fast and ever-changing world of technology and, more importantly, the Internet.
Stripping it down to basics, analytic tools are business application software used in measuring and improving business operations performance. They are applications used to collect data regarding the factors involved in the operation of the business, allowing business owners to make informed decisions based on information obtained from data miners.
Only a handful of business owners ever take time to use analytic tools in their daily business operations, much less apply their results to improve their businesses. It is undeniable to say that analytic tools are undervalued at this point in time. However, an emerging and growing number of businesses have adopted different types of models to analyze data, proving successful in applying the results of their findings.
There are various ways of processing data, and we will be discussing a few traditional means and a few newer methods that provide us a great deal of information needed to keep a business in stride and even stay ahead of the competition.
Note these categories of tools are in no way arranged via importance or chronology, but should be taken into consideration in relevance to its application to your business.
Us humans are visual creatures by nature and primarily rely on our senses, particularly sight, which takes precedence over all other senses. (The speed of light travels the fastest, which almost always instantly serves as basis for any perceived information.) Therefore, by creating a visual display such as a graph or Gantt chart to represent data, it becomes easier to analyze the data and spot patterns. This way, we are able to see the big picture rather than focus on the individual sets of data, making it particularly effective in analyzing huge volumes of data or information.
Business experiments, A/B testing, and experimental design techniques are used in validity testing. It is simply an application of the experiments we usually conduct at school. A two-group setup, where one group is the control group (doing everything the usual way) and the other will be the experimental group (the group where any experimental changes are applied). Data is gathered from both and compared. This technique is extensively used where you are trying to decide between two or more possible options.
Regression analysis aims to establish the cause and effect between data variables. It is useful in determining if a known variable has a direct effect on the other. This technique is used in testing inferences or hypotheses connecting two or more variables that may or may not have a cause-effect relationship.
This is one of many statistical techniques used to find a relation between two separate variables and gauging the strength of that relationship. This technique is quite useful in establishing a relationship between variables assumed to be having effects with each other.
Data collected over uniformly spaced intervals is called “time series data.” This technique of time series analysis or forecasting primarily deals with such data. It explores the collected data to determine particular characteristics that may affect changes over a time period and predict effects based on collected history data.
Data mining is often used in connection to extensive amounts of data sets (commonly referred as “big data”). Such data sets are analyzed for commercial relevance suggestions, patterns and relationships to improve performance of a business.
Also called as total return analysis is a process of analyzing the possibility of future events giving consideration to alternate possible results. This is often used when having uncertainty in taking a decision or course of action.
Text analytics is extracting value from large quantities of text data. It helps you get information, recognize patterns, tag and annotate information, assess sentiment and for predictive analytics.
Image analytics is a technique that uses information and understanding from images or photographs. It relies on pattern recognition, signal processing, and digital geometry. This technique is commonly used for facial recognition in relation to security purposes.
Sentiment analysis is also known as “opinion mining,” which is intent on getting subjective opinion from either text, video, or audio data. The primary goal for this technique is to obtain a measure of attitude of an individual or group in connection to a topic or context. Most use this technique in recognizing stakeholder or shareholder opinion.
As it clearly indicates, video analytics extracts information and understanding from video footage. It is in part image analytics but with an additional a measure on behavior in relation to time-tracking (since videos often have time stamps). From this information, for example, a business can directly connect customer behavior in terms of the times when a customer visits and what a customer does when they visit your store.
Commonly known as “speech analytics,” this uses information from audio recordings or conversation. This technique makes use of analysis from topics such as actual words or phrases used and the degree of emotional content of the conversation. Voice analytics are commonly used in the contact center environment, where recurring technical issues or complaints will be identified and analyzed.
Linear programming—also called “linear optimization”—involves defining the best outcome based on a number of constraints with the use of a linear mathematical model. With the use of minimum and maximum conditions, linear optimization helps solve problems in relation to constraints, such as time, raw materials, etc. It provides the best combination of minimal resource use for maximum profit.
Using computerized simulations of random variables, the Monte Carlo simulation problem-solving and risk-assessment technique estimates outcome and risk probability. It is particularly useful for understanding implications of a course of action.
Cohort analysis is the study of the behavior of a group over time. This is highly important in knowing more about the behavioral patterns and influential decision-making behaviors of a group such as your stakeholders, customers, and your employees.
In dealing with a large number of variables, factor analysis is used to determine data reduction and detection of structure. It helps in trimming down the number of variables within your data, making it easier and more useful in understanding inter-relationships among a number of variables.
The process of analyzing mathematical modeling that makes up a neural network is called neural network analysis. In this technique, a computer program is modeled after a human brain processing large amounts of data, essentially identifying patterns the way we do. This technique is commonly useful when large amounts of data is involved.
Meta analytics involves the study of previous studies regarding a topic in order to identify patterns, trends, or relationships with existing literature or study results. It is commonly used to obtain understanding with a certain topic without doing any of the research yourself. Don’t flip—by “literature,” we mean the research papers surrounding your industry or business.
There exists numerous other analytical tools that can be used in a business. Your business is still entirely dependent on how you use the information you have obtained from the different analytic tools. Each business is different so each business has its own unique approach. It is still up to you which methods of data acquisition and analytics you use to make a difference.
Having discussed the more standard approach for analytic tools in the business, let us shift our attention to the quickly progressing and ever-changing software analytic tools used in our modern day. Software analytic tools are vital tools used in analyzing a business. In the past, software analytic tools was commonly referred to as SAS or “statistical analysis system.”
One of the first and most popular commercial analytic tools was SAS. This statistical analysis system software refers to programming language used by commercially paid-for analytic tools such as MS Excel.
As the name implies, SAS was first developed to address statistical analysis, but was developed to do more than just statistics. It now ranges from data management, data operations statistics, operations research, quality control, and etc. Many corporations and businesses use these software and one can imagine no company, institution, or business that can afford to ignore using them.
SAS still continues to be the most used in the industry, largely due to flexibility in pricing. The SAS analytic tool is a versatile and easy-to-use analytic tool which in recent years has seen a significant increase in modules.
Who on this planet—barring the Apple purists—does not use Excel? Any individual in the business of analytics surely has used and is familiar with Excel or at the least Google Spreadsheets. Excel is heavy duty yet basic. Simply put, it is for the ambitious masses.
Tableau users specially like its great visualizations and dashboards. Tableau is effectively better than Excel in terms of visualization and data handling. It is specially useful in making interactivity with the data and visuals.
Much like Tableau, Qlikview is great when it comes to data visualization. It is reported to be slightly faster than Tableau and provides better flexibility for more advanced users as compared to Tableau’s slightly user friendly interface.
Splunk started off use as processor of data machine log files which in recent times has become more than just that. It also boasts of better visualization options along with a Web interface making it easier to use.
An open source analytic tool refers to analytic software using a source code that anyone can inspect, modify, and improve. The source code refers to the part of the software that most users do not get to see. It is the code used by programmers in changing pieces of a software that controls how a program or application works. Open source allows programmers to access the program’s source code and can make improvements by either adding features or fixing parts of the program.
With more and more businesses switching over to open source software due to the growing price of commercially available tools along with the flexibility that an open source software offers, demand has been steadily increasing for open source analytic tools in use for business.
Commercially available analytic tools are still viable and very much usable tools in the business. However, most businesses cannot utilize all the functions that a commercial analytic tool has to offer. In this regard, it would just mean as a waste of money on their part. Due largely to this, open source analytic tools have been steadily gaining support with all major businesses which gave optimal use of the software and most come for free.
R. Probably the most popular analytics tool today, R has marginally gained more users for companies as compared to SAS. R is more robust and flexible and handles data sets a lot better, topping the industry due to versatility in applications. Much of R’s success is mainly due to the fact that it seamlessly integrates to most big data platforms.
Python. Python is a favorite among programmers due to the language being easy to learn. And with advancements and developments to analytical and statistical libraries, Python is becoming the tool of choice for many, offering a wide coverage of statistical and mathematical functions. Quite a deadly combo if anyone should ask.
Apache Storm. When moving data or channeling data in via continuous stream, Apache Storm is the big data tool of choice.
Apache Spark. Spark is specially targeted on working with large volumes of unstructured data. Spark is best used for static data and provides easy integration with a lot of platforms. Spark also features its own learning library best used in analytics.
PIG and HIVE. PIG and HIVE make integral tools for writing specific queries. PIG and HIVE behave like SQL, which is a language used in communicating with databases or largely used for database management. Most companies working with big data use specific platforms that both PIG and HIVE easily integrate into.
The demand for open source tools has only been going up as more and more platforms are switching to open source programming via the open source initiative.
For most companies, it is essential to have Web presence to measure up to the level of any competition. Web analytic tools exist to understand website performance, customer service levels, and in understanding competition. The following list accounts for some of the most popular and easy-to-use tools among Web tools. They are in no way the definitive best, but should rack up points to ease of use and integration ease into multiple platforms available.