Updated : Jul 01, 2022 in Uncategorized

Information management

While this article can’t touch on every facet of the discipline, the best practices and common challenges, advice from experts, and discussion of the value of data and importance of security should give you a better idea of what IM is and why it’s important.

Information Management Strategies: From Punch Cards to Data Warehouses, and Looking to the Future with Big Data and AI

In a nutshell, information management (IM) is making sure that the right people have the right information at the right time. But there’s a lot that goes into making that happen: Data needs to be processed, contextualized, tagged, and analyzed in order to become useful information. This article discusses information management in a business environment and its background, reviews best practices, and examines how raw data becomes information. Plus, you’ll hear from experts about planning and strategy to set up an IM program.

In the 1970s, information management began to emerge from data management as virtual media began to overtake physical media (punch cards, magnetic tapes, paper, etc.). As PCs started to replace mainframes as the primary computing platform in the 80s, and as networked systems came to prominence in the 90s, information management came into its own.

The definition of information management is constantly evolving as the technology, ideas, and business needs change. IM can encompass a cycle of organizational activities: gathering data, analyzing, categorizing, contextualizing, and archiving (and in some cases, deleting it), in order to support a business’ needs. This means that data and information have a lifecycle: It’s useful for a period of time, but at some point it’s no longer valuable.

Like any other business practice, IM incorporates general management concepts, such as planning, controlling, and execution. Information management also includes data management and its associated activities. Data management is the development and implementation of tools and policies that allow data to progress from stage to stage during its lifecycle.

Four Components of Information Mangement

IM is often confused with content management or knowledge management. While all three processes are related, and there is some overlap, they do have some differences. Content management deals with data (blocks of text, images, videos, and more) a website uses, and the covers to organize and display the data (e.g. XML tags or HTML coding). Knowledge management is similar to library science, and deals with information for training and education, as well as knowledge and expertise transfer, and passing on lessons learned.

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Information Body of Knowledge

Data as a Product
In the same way a company produces something like nuts and bolts, one company department (like IT) can produce data that other departments (like finance or marketing) or another business treat like a product or service. With this frame of mind, the providing entity will see the receiving entity as a customer and therefore may be more responsive to their needs.

Stefan Haase

Stefan Haase, Director at Whitecap Consulting in Leeds, UK, explains,
“Information Management is a multi-faceted discipline that centres on data compliance. An organization accesses, creates, distributes, manages, stores, protects, and secures a wide variety of information which requires strong data governance, access management, and data protection.”

How to create an information management system

1. Identify information requirements

The first step when creating an information management system is to identify information requirements. This can be in the form of an internal study or company-wide survey to determine the scope of the system in relation to the business, its operations, stakeholders and regulatory requirements. A simple way to achieve this step is to ask employees and management the amount and type of information they need to perform their duties.

2. Outline objectives

For the system to be successful, the organization needs to define its objectives in the form of guidelines or protocols that will guide implementation. Consider the overall management principles that will serve as a user manual when the system becomes operational.

3. Determine information sources

Organizations can collect information from diverse sources, including employees, internal departments, competitor research, market intelligence and regulatory agencies. The objectives of the system often determine the sources of information.

4. Determine collection and classification methods

Once you have determined the sources of information, the next step is to identify methods of collecting and classifying the information. This involves outlining the amount of information collection and the frequency, location and time. For classification, determine which information is quantitative, qualitative, technical, demographic, financial, legal and other categories. This step also involves the storage of current information and archiving when it becomes obsolete.

5. Determine dissemination method

6. Perform a cost-benefit analysis

The cost of an information management system will include expenses for setting up the infrastructure, training staff, daily operations and maintenance. An effective information management system will deliver benefits that outweigh the costs.

7. Implement and evaluate

If the cost-benefit analysis is positive, you’ll begin setting up the system and providing training and operational guidelines. You want employees who use the system to improve their productivity and efficiency at work.

You should assess the performance of the system after some months to determine how it’s met objectives, including ensuring that the benefits continue to outweigh costs. Shorter periods for recording and retrieving information and increased use of data for decision-making are also signs the system is working.

8. Maintain and improve

An evaluation will show how to improve the system’s effectiveness and also provides an opportunity to upgrade infrastructure and retrain staff. Continuous improvements can contribute positively to the company’s ability to achieve short- and long-term goals.

What does information management mean in Office 365?

In recent years, Office 365 (now merged into the wider ‘Microsoft 365’ ecosystem) has risen to be the dominant platform for enterprise collaboration, personal productivity, intranets, and much more besides. This provides an impressively broad landscape of individual products, sitting on a common architecture and platform.

While Office 365 has delivered tremendous improvements in functionality and capability, it has also highlighted the challenges outlined in this article. With documents moving into the cloud, the question of how to structure and search them becomes critical. Equally, the new tools expose organisations to new and more pervasive risks, including privacy breaches, and data loss.

Drawing on the 10 principles outlined in this article, Step Two has developed a new business-first methodology for Office 365. This uses the approach of Office 365 ‘waves’ to ensure meaningful adoption (principle 2), and it runs multiple initiatives in parallel (principle 5). The methodology also offers both a tactical and strategic way forward, with the tactical approach very much focused on generating early ‘wins’ (principle 10).


Information management

Maeve Cummings, Co-author of Management Information Systems for the Information Age and Professor of Accounting & Computer Information Systems at Pittsburg State University in Pittsburg, Kansas, explains how MIS functions in academia. “[Management information systems is] the study of computers and computing in a business environment. Computer science focuses on the machine while information systems, or management information systems, focuses on how IT can support the strategy and operation of organizations,” she explains.

Information management

Information Management

Information management is the entire range of technical, operational, and social functions of a system that is used to handle information. Individuals, social networks of individuals, organizations, businesses, and governments all engage in some form of information management. They organize the information they use to communicate, to record history, and to share, store, and create meaning from information. Information management affects the organization of information, the access to information, and the ways in which users can interact with the information. Information management further includes policies that affect the availability and uses of information.

Information management is usually influenced by a strategy or framework that guides the planning, application, and uses of that information. A university, for example, needs a framework for their information systems to accommodate instantaneous changes in student enrollment, across-the-board changes in tuition fees and balances, occasional changes in faculty data, and so forth. Information management strategies influence the relationships between information, technology, and the larger social or organizational information, and have an impact on the functions of information and information systems. Some agencies and businesses do nothing but manage information.

The social measurement of information management is becoming extremely important in this information age, because all organizations employ information in some manner, using and providing some amount of information as a central function of the operation of the organization at some level. Many businesses have embraced electronic commerce (e-commerce) and many governments around the world are providing information and services through electronic government (e-government) web sites. Within these business and government organizations, information has technical, operational, and social roles. Most organizations use information technologies and information systems to provide or deliver information to service populations, such as patrons, customers, or citizens. The increasing usage of information by a diverse range of organizations heightens the importance of measuring information management, as the way in which an organization manages its information affects the organization and the organization’s service population.

The need for information management excellence

The complexity of (enterprise) information management often turns out to be a problem. A big problem for executives who don’t always see the essence as information management tends to be dealt with outside of the ‘business’ part of the organization and in silos such as Enterprise Content Management. Even if we understand its importance, as many do, seeing the importance of information (management) is not the same as de facto seizing the benefits.

Moreover, as executives we already have so much going on. As if it isn’t enough that we have to digitally transform our processes and businesses, deal with increasing competition and economic challenges, respond to the demands of changing customers and workers, and have to meet short-term, mid-term and long-term business goals, we now also need to understand the essence of this complex end-to-end matter of information management and even how it plays a role in areas such as CX (customer experience) optimization and innovation. And it gets worse: understanding how to make sense of all this information, moving towards a more intelligent information management approach and being smart about content and content management isn’t even enough anymore. Data is nothing less than a core asset and information is rapidly becoming a very tangible and even disruptive one.

Recent Developments and The Future of Management Information Systems

In the future, many of the same forces that will change the larger world will affect MIS, but some will have a greater impact than others. MIS experts weigh-in on the topic and what we can expect going forward:

One big area of development in information technology is artificial intelligence (AI), which goes far beyond robots that control production (for example, in the automobile industry). Machines are becoming smarter in that they can learn how to solve problems. One such system is a neural network, which is used to alert you that your credit card may have been used unlawfully. These neural networks form a pattern of your spending and based on that, they flag purchases that are out of character, which is when you’re notified or your credit card is frozen, depending on the situation. Such developments undoubtedly affect MIS, but they also affect the culture, the law, medicine, military defense, etc.

With so much big data being collected and analyzed nowadays, there will be a great need for legal minds to help sort through the various issues of what should and should not be legal from a privacy point of view. Also, with the budding field of computer-aided mind reading, still very much in its infancy, the issue of what society is allowed to do with that information will be crucial. For example, if you can read a person’s mind to determine whether that person is lying or not, would that be considered evidence or testimony? The law protects people from incriminating themselves (i.e. testimony). However, evidence, such as blood and hair samples may be taken without the consent of the accused. So which is mind-reading? The most interesting part of this business is that it is constantly changing and becoming more powerful. That is also the most alarming part of it.

  • Artificial Intelligence: Narrow AI (AI for specific tasks) is now pervasive in many organizations. Advances in machine learning and deep learning are making narrow AI much more valuable to all of us. Think instantaneous translation, autonomous vehicles, robots, digital manufacturing (3D printing), etc. MIS departments must try to keep up with these advances and decide how narrow AI can be used in their organizations.
  • The Internet of Things (IoT): The rapid increase of placing sensors on all objects (animate and inanimate) is leading to a sense-and-respond environment. MIS employees should perform the SWOT analysis on IoT for their organizations. A well-publicized example of IoT is General Electric and its Predix operating system.
  • Blockchain: Distributed-ledger technology is now being used in a large number of areas. Again, MIS employees must keep up with this technology and see how it impacts their organizations.
  • Financial Technology (FinTech): If your organization is in the financial sector, your MIS employees had better be closely watching start-up FinTech companies. These companies are planning on disrupting the traditional financial sector.
  • Quantum Computing: As Moore’s Law begins to slow as we reach the physical limits on how many integrated circuits we can place on a chip, a new paradigm is emerging called quantum computing. Classical computing uses bits, which are either a “0” or a “1.” Quantum computing uses quantum bits (qubits). Unlike classical bits, qubits can store much more information than just 1 or 0 because they can exist in any superposition of these values. Quantum computing is in its very early days, but its potential can provide a dramatic increase in computing speeds. For example, scientists are hoping to be able to accurately model the climate. Another application lies in the field of information security.


Information management

Actions speak louder than words. The first project is the single best (and perhaps only) opportunity to set the organisation on the right path towards better information management practices and technologies. The first project must therefore be chosen according to its ability to act as a ‘catalyst’ for further organisational and cultural changes.

What is Product Information Management (PIM)?

Product information management (PIM) is the process of managing and enriching product information and related digital assets across different teams to provide an engaging product experience and successfully sell the product across multiple sales and marketing channels. A PIM solution is a master-data fueled, process-driven application for collaborating on product content. It serves as a single, central platform to collect, manage, govern and enrich product information and content and distribute it to marketing, sales, and eCommerce channels.

A PIM solution is a master-data fueled, process-driven application for collaborating on product content. It serves as a single, central platform to collect, manage, govern and enrich product information and content and distribute it to marketing, sales, and eCommerce channels.

Why Do I Need Product Information Management (PIM)?

Whether you are selling B2B or B2C, delivering engaging product experiences is a key success factor for your business. Your customers expect to have rich, relevant, and trusted product information across channels and digital touchpoints so they can make an informed purchase decision.

But quite often, product information is incomplete, fragmented, and inconsistent across different applications, siloed systems, business units, and channels. Teams like digital marketing, merchandising, product management, or eCommerce cannot get a complete view of all product information. They spend too much time manually managing product data or exchanging emails about product content, both internally and with external groups like suppliers or creative agencies.

Companies that are not able to manage and collaborate on their product information effectively miss opportunities, lose revenue and market share, but more importantly, they struggle to deliver engaging product experiences that are required to build brand loyalty.

PIM helps standardize the increasingly complex demands of product content. It allows you to easily syndicates trusted, enriched and high-quality product information to sales and marketing channels. With the right PIM strategy and a consistent, accessible source for product information, you can speed up workflows and time to market; quickly identify and assess product data quality issues; capitalization on emerging market opportunities; and ultimately deliver a positive product experience to customers.

The 2020 IDC PIM for Commerce MarketScape notes, “In the digital economy, both B2C and B2B customers are demanding more compelling, frictionless, and personalized commerce experiences. Retailers, manufacturers, and brands must deliver accurate product data to all their distribution channels to optimize the story being told around their products. IDC believes the importance of product information management (PIM) will only grow as organizations seek to forge stronger customer relationships within both the creation and loyalty loops via engaging product stories.”

Ten principles

Organisations are very complex environments in which to deliver concrete solutions. As outlined above, there are many challengesto be overcome when planning and implementing information management projects.

All of these approaches will fail, as they are attempting to convert a complex set of needs and problems into simple (even simplistic) solutions. The hope is that the complexity can be limited or avoided when planning and deploying solutions.

In practice, however, there is no way of avoiding the inherent complexities within organisations. New approaches to information management must therefore be found that recognise (and manage) this complexity.

Instead, successful information management is underpinned by strong leadership that defines a clear direction (principle 6). Many small activities should then be planned to address in parallel the many needs and issues (principle 5).

Principle 2: focus on adoption

In all these cases, the challenge is to gain sufficient adoption to ensure that required information is captured in the system. Without a critical mass of usage, corporate repositories will not contain enough information to be useful.

This presents a considerable change management challenge for information management projects. In practice, it means that projects must be carefully designed from the outset to ensure that sufficient adoption is gained.

In practice, this means taking holistic and nuanced approaches to designing and delivering solutions. In the context of Office 365, for example, new capabilities can be delivered via Office 365 ‘waves’, which bundle together people, process and technology elements, all driven by a clear purpose and outcome.


Data is the "life blood" of an organization, for as it flows between systems, databases, processes, and departments, it carries with it the ability to make the organization smarter and more effective. The highest performing organizations pay close attention to the data asset, not as an afterthought but rather as a core part of defining, designing, and constructing their systems and databases. Data is essential to making well-informed decisions that guide and measure the achievement of the organizational strategy. For example, an organization may analyze data to determine the optimal enforcement actions that reduce non-compliant behavior. Similarly, data is also at the heart of the business processes. An organization may enhance a process to catch fraudulent activities by including historical risk-related data. Over time, this type of process improvement can result in material savings. Even a single execution of a business process can translate into substantial benefits, such as using data patterns to stop a terrorist at a border or filtering a cyber attack.

How an organization uses and manages the data is just as important as the mechanisms used to bring it into the environment. Having the right data of appropriate quality enables the organization to perform processes well and to determine which processes have the greatest impact. These fundamental objectives leverage data by transforming it into useful information. The highest performing organizations ensure that their data assets are accessible to the processes and individuals who need it, are of sufficient quality and timeliness, and are protected against misuse and abuse. Successfully leveraging data and information assets does not happen by itself; it requires proactive data management by applying specific disciplines, policies, and competencies throughout the life of the data. Similar to systems, data goes through a life cycle. Figure 1 presents the key phases of the data life cycle.

Effective data management through all of the data life-cycle phases is the foundation for reliable information. Data may have different uses at different times and require different management handling in the life-cycle phases. For instance, an organization may consider critical data required for discovery as very valuable during a key event, but when the event is over, the information diminishes in value quickly (e.g., data collected for predicting the weather).

Data may typically have a longer lifespan than the project that creates it. Though the funding period formally defines the lifespan of most projects, the resultant data may be available for many years afterward. If an organization manages and preserves the data properly, the data is available for use well into the future, increasing the investment made in generating it by increasing visibility and usefulness. The time spent in planning and implementing effective data management pays dividends far in excess of its investment costs.

Data without context has no value; data that consumers never use is worthless, also. The value of data is in the information it contains and uses. Extracting information and providing it in an appropriate format may be summarized as data analysis and reporting. However, data analysis and reporting encompasses several overlapping disciplines, among them statistical analysis, data mining, predictive analysis, artificial intelligence, and business intelligence. IDM has an appreciation for these disciplines and may use the same tools and incorporate some of these disciplines. The common ground among all of these disciplines and IDM is making good use of data.

Knowledge Required

  • Big data: Big data describes the exponential growth and availability of data, both structured and unstructured. Its characteristics are volume, velocity, and variety. It may be difficult to manage with traditional tools, it may move too fast, or it may exceed current enterprise processing capacity. Big data applications contributed to the growth of NoSQL databases.
  • Operational data: Operational environments provide core transactional capabilities (i.e., processing applications, claims, payments, etc.) that typically work with a DBMS. For structured data, Relational DBMS, or RDBMS, are used mostly.
  • Data exchange: Organizations use data exchanges and data exchange standards to share information with internal or external parties. Standardizing exchange formats and metadata minimizes impacts to both the sending and receiving systems and reduces cost and delivery time. A related discipline is master data management (MDM). An example is a vendor list. The U.S. Treasury requires specific information identifying contractors before the federal government reimburses them. Most federal agencies use this centrally collected list. Exchange, transform, and load (ETL) tools typically support these types of data exchange activities. ETL tools manipulate data and move it from one database environment to another.
  • Data warehouses [1]: The integration of similar and disparate data from across organizational, functional, and system boundaries can create new data assets. The organizations can use the new data to ensure consistent analysis and reporting and to enhance the information needed for decision making. Data may be structured, unstructured, or both. Business intelligence has become a recognized discipline. It takes advantage of data warehouses (or similar large data consolidation) to generate business performance management, and reporting.
  • Data mining and knowledge discovery: Mining applications explore the patterns within data to discover new insight and predictive models. An organization may use specialized software that applies advanced statistics, neural net processing, graphical visualization, and other advanced analytical techniques against targeted extracts of data. In addition, tools may evaluate continuously streaming data within operational sources.
  • Database management [2]: Knowledge in this discipline requires specific training related to a specific DBMS and being certified. A certified database management professional (CDMP) is responsible for the installation, configuration, and maintenance of a DBMS (e.g., storage requirements, backup and recovery), as well as database design, implementation, monitoring, integrity, performance, and security of the data in the DBMS.
  • Data architecture: A data architect is responsible for the overall data requirements of an organization, its data architecture and data models, and the design of the databases and data integration solutions that support the organization. The data structure must meet business requirements and regulations. Good communication and knowledge of the business must be part of the data architect’s arsenal. A specialized area in data architecture is the role of the data steward. The data steward is usually responsible for a specific area of data such as one or more master data.

What is in it for me?Conveying the importance of information and data management to federal executives is the most common challenge that an SE will encounter. Most organizations focus their time and energy on application development and the technical infrastructure. For information systems, at best this approach leads to delays in implementation and, at worst, data is not trusted and system failures occur. The organization needs to coordinate data and IT staff with the business staff to align strategy and improvement initiatives. The best approach for long-term success is to initiate a program that gradually addresses the multifaceted challenges of data management.

An effective data management program begins with identifying core principles and collaborative activities that form the foundation for providing efficient, effective, and sustainable data. The organization should interweave the following core principles throughout all of the data management activities:

  • Data collected is timely, accurate, relevant, and cost-effective.
  • Data efforts are cost-efficient and purposeful, and they minimize redundancy and respondent burden.
  • Data is used to inform, monitor, and continuously improve policies and programs.
  • Data activities seek the highest quality of data and data collection methodologies and use.
  • Data activities are coordinated within the organization, maximizing the standardization of data and sharing across programs.
  • Partnerships and collaboration with all stakeholders are cultivated to support common goals and objectives around data activities.
  • Activities related to the collection and use of data is consistent with applicable confidentiality, privacy, and other laws, regulations, and relevant authorities.
  • Data activities adhere to appropriate guidance issued by the organization, its advisory bodies, and other relevant authorities.