The Business Of Software

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Made for sharing. Download files for later. Send to friends and colleagues. Modify, remix, and reuse (just remember to cite OCW as the source.) Learn more at. Course Features. Course Description This subject is a seminar-style course aimed at anyone who is interested in founding a software company or working for a software company or company that uses software technology extensively as a senior manager, developer, or product/program manager. It is also appropriate for people interested in the industry or in working as an industry analyst.

Many of the issues we discuss are highly relevant for companies whose businesses are heavily dependent on software, such as in e-business or financial services, or embedded software for industrial applications.

TechnologyAdvice Guide to Business Intelligence Software Updated: Dec. 7th, 2018 What is Business Intelligence Software?

Business intelligence software is a set of tools used by companies to retrieve, analyze, and transform data into meaningful information. Examples of business intelligence tools include data visualization, data warehousing, dashboards, and reporting. Covering a range of technologies, business intelligence (BI) loosely refers to tools that retrieve, analyze, and transform data into meaningful information that helps businesses make more intelligent decisions. In contrast to competitive intelligence, pulls from internal data that the business produces, rather than from outside sources.

The term business intelligence started being used sometime around the late 1950s, and grew from a set of technologies called decision support systems. It’s fitting to consider business intelligence in relation to decision support systems, because that’s exactly what business intelligence does: it helps businesses gain a competitive edge by supporting and improving their decisions with relevant, insightful information. The rise in popularity of BI software is closely linked to the rise of “ Big Data.” As technology has progressed and more activities have shifted to the Internet, it has become possible to track and compile behavioral data like never before.

And not just human data, but market data, environmental data, and more. By 2018, it’s predicted that big data will be. To make informed choices, businesses need to base their decisions on evidence. The mountains of data that businesses - not to mention their customers - are producing contain evidence of purchasing patterns and market trends. And thus, business intelligence was born. As mentioned before, BI has been around for a while, usually in the form of quarterly or yearly reports.

However, the business intelligence we’re referring to happens at light speed, and can help a company choose a course of action in a matter of minutes. In the Information Age, everyone produces data. Walmart handles more than 1 million customer transactions per hour.

IDC estimates that by 2020, online business-to-business (B2B) and business-to-consumer (B2C) transactions. Answers and insight live inside that data, waiting to be uncovered. The businesses that harness that intelligence first, will gain a competitive advantage by predicting customer behavior, forecasting market trends, and outsmarting their rivals.

BI software interprets a sea of quantifiable customer and business actions and returns queries based on patterns in the data. BI comes in many forms, and spans many different types of technology.

For the purpose of this guide, we’ll look at three main areas to which BI can be applied, and examine the tools used for each. How Big Data is Managed Data lives in a number of systems throughout an organization. For example, large enterprises could have information about their customers in their (CRM) application, and have financial data in their (ERP) application. The most common first step in utilizing BI is often taking an inventory of all the data your business produces. BI Vendor Overview Best BI Software (By Category) Self-Service Data Visualization Data Warehousing BI Platforms Data Warehouses Business intelligence combines disparate data sources into one database by building a data warehouse. Data warehouses act as a central repository for data to be queried and analyzed by other BI applications. Using the extract, transform, and load method, data warehouses aggregate data from across an organization and make it easier for other applications to quickly access them.

Analytics and reporting tools can still function without data warehouses, but running reports through CRM software, or even point of sale (POS) software not only limits the focus of the intelligence, it also negatively affects the performance of those applications. Also, the data in these systems exist in different formats, making it exceptionally difficult to draw conclusions and identify patterns without restructuring the data into a common format and housing it in a common area. Data are stored in a data warehouse in dimensions and facts. Facts represent numbers for a specific action, likes the sales of a widget. Dimensions give context to facts by adding dates and locations. For instance, dimensions could break apart the sales of a widget by months or years, making queries easier to perform.

Data Marts Essentially simpler, narrower versions of data warehouses, data marts focus on a specific subset of data instead of storing data from across the entire company. This could be data that are used frequently, or by only one department. Data marts are cheaper to implement than data warehouses and could provide non-IT staff with a better user experience by limiting the complexity of the database.

Extract, Transform and Load (ETL) Named for the process by which data is transferred into a data warehouse, ETL applications are for normalizing data in a central location. ETL software can be included with data warehouse software or be purchased as an add-on application. Let’s examine each letter in ETL: Extract: Often the most difficult aspect of the process, the degree of success by which data are extracted from their source systems - ERP or CRM systems for example - influences the success of the rest of the process.

Often data are unstructured, meaning they aren’t formatted well for fitting into rows and columns, which makes it more difficult to analysis once it’s been stored in a data warehouse. Tagging unstructured data with metadata – information about the author, type of content, and so on – can help make it more easily found once it’s been extracted. Transform: To prepare the data for storage in the data warehouse, the second stage of ETL applies rules to incoming data in order to “clean” or normalize it. For analyses to work properly, data must exist in the same format - think apples to apples - or else the queries won’t be accurate.

Load: Now that the data have been extracted from their source systems and normalized through the transform phase, it’s ready to be loaded into the central database, mostly commonly the data warehouse. Load frequencies will vary by organization.

Some businesses may enter new data on a weekly basis while others will do it every day. Hadoop A very popular data storage framework, Hadoop is an infrastructure for storing and processing large sets of data. Though Hadoop stores data, it does so in a contrasting manner to a traditional data warehouse. Hadoop uses a cluster system – Hadoop Distributed File System or HDFS – that allows users to store files in multiple servers. Hadoop’s infrastructure provides an excellent framework for businesses that a great number of data as well as very large data files. Due to its cluster framework, Hadoop can also act as a backup mechanism: if one server goes down, businesses don’t lose access to all of their data. However, Hadoop isn’t well suited for ad hoc queries like normal data warehouses, and it can be quite complex for users who aren’t familiar with javascript.

Which Business Intelligence software is right for your business?. Analyzing Big Data Regardless of whether businesses choose to store their data in a data warehouse or run queries on the source system, the analysis part of business intelligence is what produces the insight that makes the entire field so appealing. Analytics technologies vary in terms of complexity, but the general method of combining large amounts of normalized data to identify patterns remains consistent across platforms. Data Mining Also known as “data discovery,” data mining involves automated and semi-automated analyses of sometimes large sets of data to uncover patterns and inconsistencies.

Common correlations drawn from data mining include grouping specific sets of data, finding outliers in data, and drawing connections or dependencies from disparate data sets. Data mining often uncovers the patterns used in more complex analyses, like predictive modeling, which makes it an essential part of the BI process. Indeed, it could be argued that all the “intelligence” in business intelligence is derived from data mining. Of the standard processes performed by data mining, association rule learning presents the greatest benefit. By examining data to draw dependencies and construct correlations, the association rule can help businesses better understand the way customers interact with their website or even what factors influence their purchasing behavior.

Association rule learning was originally introduced to uncover connections between purchase data recorded in point of sale systems at supermarkets. For example, if a customer bought ketchup and cheese, association rules would likely uncover that that customer was purchasing hamburger meat as well. While this is a simplistic example, it works to illustrate a type of analysis that now connects incredibly complex chains of events, and helps users find correlations that would have stayed hidden otherwise. Predictive Analytics Perhaps one of the most exciting aspects of BI, predictive analytics applications function as an advanced subset of data mining. As the name suggestions, predictive analytics forecast future events based on current and historical data. By drawing connections between data sets, these software applications predict the likelihood of future events, which can lead to a huge competitive advantage for businesses.

Predictive analysis involves very detailed modeling, and even ventures into the realm of machine learning, where software actually learns from past events to predict future consequences. For our purposes, let’s focus on the three main forms of predictive analysis: Predictive: The most well-known segment of predictive analytics, this type of software does what its name implies: it predicts, particularly in reference to a single element. Predictive models search for correlations between a particular unit of measurement and at least one or more features pertaining to that unit. The goal is to find the same correlation across different data sets.

Descriptive: Whereas predictive modeling searches for a single correlation between a unit and its features – in order to predict the likelihood of a customer switching insurance providers for example – descriptive modeling seeks to reduce data into manageable sizes and groupings. Descriptive analytics works well for helping to summarize information, such as unique page views or social media mentions. Decision: Decision analytics take into account all the factors related to a particular decision. Decision analytics predict the cascading effect a particular action will have across all the variables involved in making that decision. In other words, decision analytics gives businesses the concrete info they need to take action.

MapReduce The processing arm of the Hadoop framework, MapReduce processes data in its storage location rather than transporting the data across a server to the location of the processing software. MapReduce then only transfers the finished analysis, which are much smaller files than the large datasets MapReduce is analysis, back to the software location for reporting. And because Hadoop works as a cluster system, MapReduce is able to to analyse data across multiple servers. Text Analytics Synonymous with text mining, text analytics software combs unstructured data to find patterns hidden within large sets of text data. This type of data is usually difficult to analyze with traditional mining methods. Text analytics are particularly interesting for businesses that work with social media. Using the right software, a business can set up a rule for the software to track certain words or phrases – a business’s name for example – to find patterns in how they’re being mentioned.

The Different Types of Data Data comes in three main forms: structured, semistructured, and unstructured. Unstructured data is the most common, and includes text documents and other types of files that don’t have an easily readable formats (for a computer at least).

It’s widely accepted that the vast majority of data that businesses produce -comes in an unstructured form. Unstructured data can’t be stored in rows or columns, which makes it impossible for traditional data mining software to analyze.

However, utilizing this data is often crucial to figuring out how to move forward. With so much data stored in unstructured form, text analytics should be a key consideration when trying to find the best business intelligence software. Reporting The previous two applications dealt with the mechanics of business intelligence. How business data are stored, and how these data are refined into meaningful intelligence. Business intelligence reporting focuses on the presentation of these findings. Online Analytical Processing Most often used with multidimensional databases, online analytical processing (OLAP) enables users to query data warehouses and create reports that view data from multiple perspectives, say by monthly sales or by number of transactions for a particular item. OLAP allows users to interact with data in three ways: consolidation, drill-down, and slicing and dicing.

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Consolidation gathers data from multiple dimensions and helps users anticipate trends. Contrastingly, drill-down navigates down into more specific areas of analyses.

Finally, OLAP’s slice and dice functionality lets BI professionals exclude and include certain data in their analysis. Data Visualization One of the more popular trends in BI, allows companies to graphically display the results of data mining or other analytics.

The Business Of Software Pdf

As part of a broader shift towards better BI usability, the data visualization UX may become a larger factor in the software purchasing decision. Dashboards Another, albeit narrower, form of data visualization, dashboard functionality refers to the interface that represents specific analyses. Dashboard software is another segment of business intelligence software that’s growing in popularity due to demand for better BI interfaces. State of the Market The state of business intelligence is changing.

The Business Of Software Conference

Far from a misunderstood buzzword, BI is being implemented in a number of different organizations to great effect. In:. 57 percent of respondents had standardized one or more BI applications throughout their business. 38 percent reported that they are not using business intelligence in their organization at all.

89 percent see big data and BI as an opportunity. 11 percent see BI as a problem.