Home > Articles > Business & Management

  • Print
  • + Share This
This chapter is from the book

A Longitudinal View of Analytics

Although the buzz about it is relatively recent, analytics isn’t new. It’s possible to find references to corporate analytics as far back as the 1940s, during the World War II era, when more effective methods were needed to maximize output with limited resources. Many optimization and simulation techniques were developed then. Analytical techniques have been used in business for a very long time. One example is the time and motion studies initiated by Frederick Winslow Taylor in the late 19th century. Then Henry Ford measured pacing of assembly lines, which led to mass-production initiatives. Analytics began to command more attention in the late 1960s, when computers were used in decision support systems. Since then, analytics has evolved with the development of enterprise resource planning (ERP) systems, data warehouses, and a wide variety of other hardware and software tools and applications.

The timeline in Figure 1.2 shows the terminology used to describe analytics since the 1970s. During the early days of analytics, prior to the 1970s, data was often obtained from the domain experts using manual processes (i.e., interviews and surveys) to build mathematical or knowledge-based models to solve constraint optimization problems. The idea was to do the best with limited resources. Such decision support models were typically called operations research (OR). The problems that were too complex to solve optimally (using linear or non-linear mathematical programming techniques) were tackled using heuristic methods such as simulation models.

Figure 1.2

Figure 1.2 A Longitudinal View of the Evolution of Analytics

In the 1970s, in addition to the mature OR models that were being used in many industries and government systems, a new and exciting line of models had emerged: rule-based expert systems (ESs). These systems promised to capture experts’ knowledge in a format that computers could process (via a collection of if–then rules) so that they could be used for consultation much the same way that one would use domain experts to identify a structured problem and to prescribe the most probable solution. ESs allowed scarce expertise to be made available where and when needed, using an “intelligent” decision support system. During the 1970s, businesses also began to create routine reports to inform decision makers (managers) about what had happened in the previous period (e.g., day, week, month, quarter). Although it was useful to know what had happened in the past, managers needed more than this: They needed a variety of reports at different levels of granularity to better understand and address changing needs and challenges of the business.

The 1980s saw a significant change in the way organizations captured business-related data. The old practice had been to have multiple disjointed information systems tailored to capture transactional data of different organizational units or functions (e.g., accounting, marketing and sales, finance, manufacturing). In the 1980s, these systems were integrated as enterprise-level information systems that we now commonly call ERP systems. The old mostly sequential and nonstandardized data representation schemas were replaced by relational database management (RDBM) systems. These systems made it possible to improve the capture and storage of data, as well as the relationships between organizational data fields while significantly reducing the replication of information. The need for RDBM and ERP system emerged when data integrity and consistency became an issue, significantly hindering the effectiveness of business practices. With ERP, all the data from every corner of the enterprise is collected and integrated into a consistent schema so that every part of the organization has access to the single version of the truth when and where needed. In addition to the emergence of ERP systems—or perhaps because of these systems—business reporting became an on-demand, as-needed business practice. Decision makers could decide when they needed to or wanted to create specialized reports to investigate organizational problems and opportunities.

In the 1990s, the need for more versatile reporting led to the development of executive information systems (decision support systems designed and developed specifically for executives and their decision-making needs). These systems were designed as graphical dashboards and scorecards so that they could serve as visually appealing displays while focusing on the most important factors for decision makers to keep track of—the key performance indicators. In order to make this highly versatile reporting possible while keeping the transactional integrity of the business information systems intact, it was necessary to create a middle data tier—known as a data warehouse (DW)—as a repository to specifically support business reporting and decision making. In a very short time, most large to medium-size businesses adopted data warehousing as their platform for enterprise-wide decision making. The dashboards and scorecards got their data from a data warehouse, and by doing so, they were not hindering the efficiency of the business transaction systems—mostly referred to as enterprise resource planning (ERP) systems.

In the 2000s the DW-driven decision support systems began to be called business intelligence systems. As the amount of longitudinal data accumulated in the DWs increased, so did the capabilities of hardware and software to keep up with the rapidly changing and evolving needs of the decision makers. Because of the globalized competitive marketplace, decision makers needed current information in a very digestible format to address business problems and to take advantage of market opportunities in a timely manner. Because the data in a DW is updated periodically, it does not reflect the latest information. In order to elevate this information latency problem, DW vendors developed a system to update the data more frequently, which led to the terms real-time data warehousing and, more realistically, right-time data warehousing, which differs from the former by adopting a data refreshing policy based on the needed freshness of the data items (i.e., not all data items need to be refreshed in real time). Data warehouses are very large and feature rich, and it became necessary to “mine” the corporate data to “discover” new and useful knowledge nuggets to improve business processes and practices—hence the terms data mining and text mining. With the increasing volumes and varieties of data, the needs for more storage and more processing power emerged. While large corporations had the means to tackle this problem, small to medium-size companies needed financially more manageable business models. This need led to service-oriented architecture and software and infrastructure-as-a-service analytics business models. Smaller companies therefore gained access to analytics capabilities on an as-needed basis and paid only for what they used, as opposed to investing in financially prohibitive hardware and software resources.

In the 2010s we are seeing yet another paradigm shift in the way that data is captured and used. Largely because of the widespread use of the Internet, new data-generation mediums have emerged. Of all the new data sources (e.g., RFID tags, digital energy meters, clickstream Web logs, smart home devices, wearable health monitoring equipment), perhaps the most interesting and challenging is social networking/social media. This unstructured data is rich in information content, but analysis of such data sources poses significant challenges to computational systems, from both software and hardware perspectives. Recently, the term Big Data has been coined to highlight the challenges that these new data streams have brought upon us. Many advancements in both hardware (e.g., massively parallel processing with very large computational memory and highly parallel multiprocessor computing systems) and software/algorithms (e.g., Hadoop with MapReduce and NoSQL) have been developed to address the challenges of Big Data.

It’s hard to predict what the next decade will bring and what the new analytics-related terms will be. The time between new paradigm shifts in information systems and particularly in analytics has been shrinking, and this trend will continue for the foreseeable future. Even though analytics is not new, the explosion in its popularity is very new. Thanks to the recent explosion in Big Data, ways to collect and store this data, and intuitive software tools, data and data-driven insight are more accessible to business professionals than ever before. Therefore, in the midst of global competition, there is a huge opportunity to make better managerial decisions by using data and analytics to increase revenue while decreasing costs by building better products, improving customer experience, and catching fraud before it happens, improving customer engagement through targeting and customization—all with the power of analytics and data. More and more companies are now preparing their employees with the know-how of business analytics to drive effectiveness and efficiency in their day-to-day decision-making processes.

  • + Share This
  • 🔖 Save To Your Account

InformIT Promotional Mailings & Special Offers

I would like to receive exclusive offers and hear about products from InformIT and its family of brands. I can unsubscribe at any time.


Pearson Education, Inc., 221 River Street, Hoboken, New Jersey 07030, (Pearson) presents this site to provide information about products and services that can be purchased through this site.

This privacy notice provides an overview of our commitment to privacy and describes how we collect, protect, use and share personal information collected through this site. Please note that other Pearson websites and online products and services have their own separate privacy policies.

Collection and Use of Information

To conduct business and deliver products and services, Pearson collects and uses personal information in several ways in connection with this site, including:

Questions and Inquiries

For inquiries and questions, we collect the inquiry or question, together with name, contact details (email address, phone number and mailing address) and any other additional information voluntarily submitted to us through a Contact Us form or an email. We use this information to address the inquiry and respond to the question.

Online Store

For orders and purchases placed through our online store on this site, we collect order details, name, institution name and address (if applicable), email address, phone number, shipping and billing addresses, credit/debit card information, shipping options and any instructions. We use this information to complete transactions, fulfill orders, communicate with individuals placing orders or visiting the online store, and for related purposes.


Pearson may offer opportunities to provide feedback or participate in surveys, including surveys evaluating Pearson products, services or sites. Participation is voluntary. Pearson collects information requested in the survey questions and uses the information to evaluate, support, maintain and improve products, services or sites, develop new products and services, conduct educational research and for other purposes specified in the survey.

Contests and Drawings

Occasionally, we may sponsor a contest or drawing. Participation is optional. Pearson collects name, contact information and other information specified on the entry form for the contest or drawing to conduct the contest or drawing. Pearson may collect additional personal information from the winners of a contest or drawing in order to award the prize and for tax reporting purposes, as required by law.


If you have elected to receive email newsletters or promotional mailings and special offers but want to unsubscribe, simply email information@informit.com.

Service Announcements

On rare occasions it is necessary to send out a strictly service related announcement. For instance, if our service is temporarily suspended for maintenance we might send users an email. Generally, users may not opt-out of these communications, though they can deactivate their account information. However, these communications are not promotional in nature.

Customer Service

We communicate with users on a regular basis to provide requested services and in regard to issues relating to their account we reply via email or phone in accordance with the users' wishes when a user submits their information through our Contact Us form.

Other Collection and Use of Information

Application and System Logs

Pearson automatically collects log data to help ensure the delivery, availability and security of this site. Log data may include technical information about how a user or visitor connected to this site, such as browser type, type of computer/device, operating system, internet service provider and IP address. We use this information for support purposes and to monitor the health of the site, identify problems, improve service, detect unauthorized access and fraudulent activity, prevent and respond to security incidents and appropriately scale computing resources.

Web Analytics

Pearson may use third party web trend analytical services, including Google Analytics, to collect visitor information, such as IP addresses, browser types, referring pages, pages visited and time spent on a particular site. While these analytical services collect and report information on an anonymous basis, they may use cookies to gather web trend information. The information gathered may enable Pearson (but not the third party web trend services) to link information with application and system log data. Pearson uses this information for system administration and to identify problems, improve service, detect unauthorized access and fraudulent activity, prevent and respond to security incidents, appropriately scale computing resources and otherwise support and deliver this site and its services.

Cookies and Related Technologies

This site uses cookies and similar technologies to personalize content, measure traffic patterns, control security, track use and access of information on this site, and provide interest-based messages and advertising. Users can manage and block the use of cookies through their browser. Disabling or blocking certain cookies may limit the functionality of this site.

Do Not Track

This site currently does not respond to Do Not Track signals.


Pearson uses appropriate physical, administrative and technical security measures to protect personal information from unauthorized access, use and disclosure.


This site is not directed to children under the age of 13.


Pearson may send or direct marketing communications to users, provided that

  • Pearson will not use personal information collected or processed as a K-12 school service provider for the purpose of directed or targeted advertising.
  • Such marketing is consistent with applicable law and Pearson's legal obligations.
  • Pearson will not knowingly direct or send marketing communications to an individual who has expressed a preference not to receive marketing.
  • Where required by applicable law, express or implied consent to marketing exists and has not been withdrawn.

Pearson may provide personal information to a third party service provider on a restricted basis to provide marketing solely on behalf of Pearson or an affiliate or customer for whom Pearson is a service provider. Marketing preferences may be changed at any time.

Correcting/Updating Personal Information

If a user's personally identifiable information changes (such as your postal address or email address), we provide a way to correct or update that user's personal data provided to us. This can be done on the Account page. If a user no longer desires our service and desires to delete his or her account, please contact us at customer-service@informit.com and we will process the deletion of a user's account.


Users can always make an informed choice as to whether they should proceed with certain services offered by InformIT. If you choose to remove yourself from our mailing list(s) simply visit the following page and uncheck any communication you no longer want to receive: www.informit.com/u.aspx.

Sale of Personal Information

Pearson does not rent or sell personal information in exchange for any payment of money.

While Pearson does not sell personal information, as defined in Nevada law, Nevada residents may email a request for no sale of their personal information to NevadaDesignatedRequest@pearson.com.

Supplemental Privacy Statement for California Residents

California residents should read our Supplemental privacy statement for California residents in conjunction with this Privacy Notice. The Supplemental privacy statement for California residents explains Pearson's commitment to comply with California law and applies to personal information of California residents collected in connection with this site and the Services.

Sharing and Disclosure

Pearson may disclose personal information, as follows:

  • As required by law.
  • With the consent of the individual (or their parent, if the individual is a minor)
  • In response to a subpoena, court order or legal process, to the extent permitted or required by law
  • To protect the security and safety of individuals, data, assets and systems, consistent with applicable law
  • In connection the sale, joint venture or other transfer of some or all of its company or assets, subject to the provisions of this Privacy Notice
  • To investigate or address actual or suspected fraud or other illegal activities
  • To exercise its legal rights, including enforcement of the Terms of Use for this site or another contract
  • To affiliated Pearson companies and other companies and organizations who perform work for Pearson and are obligated to protect the privacy of personal information consistent with this Privacy Notice
  • To a school, organization, company or government agency, where Pearson collects or processes the personal information in a school setting or on behalf of such organization, company or government agency.


This web site contains links to other sites. Please be aware that we are not responsible for the privacy practices of such other sites. We encourage our users to be aware when they leave our site and to read the privacy statements of each and every web site that collects Personal Information. This privacy statement applies solely to information collected by this web site.

Requests and Contact

Please contact us about this Privacy Notice or if you have any requests or questions relating to the privacy of your personal information.

Changes to this Privacy Notice

We may revise this Privacy Notice through an updated posting. We will identify the effective date of the revision in the posting. Often, updates are made to provide greater clarity or to comply with changes in regulatory requirements. If the updates involve material changes to the collection, protection, use or disclosure of Personal Information, Pearson will provide notice of the change through a conspicuous notice on this site or other appropriate way. Continued use of the site after the effective date of a posted revision evidences acceptance. Please contact us if you have questions or concerns about the Privacy Notice or any objection to any revisions.

Last Update: November 17, 2020