A few basic observations

Having good data in your data repositories does not guarantee listing good information on your reports.  However, if that data is bad, one can guarantee that the information listed on your reports will also be bad, no matter how good those reports look.

As bad data inevitably leads to bad decisions, it is much more important, and urgent, to have good data than to have a lot of data.  Quantity without quality is just a waste of resources.

In the long run, it is much easier, cheaper and more efficient to ensure that only good data makes it into your information system, than to deal with the consequences of bad decisions or actions based on bad data.

Little precious information can be obtained from data that you do not fully understand or that is not organized properly, no matter how much data you have.  The sheer amount of data often obscures the important information that is required for making smart decisions.

The quantity of data matters only if the good data, i.e., the data that will be of use to the business, can be easily separated from the other data, i.e., the noise.  And there’s a lot of noise out there.

In our information age where data has become the new lifeblood of capitalism, many organizations feel that they have to collect always more data, and use tons of data to achieve their missions and to stay competitive.

It is true that having lots of data facilitates the production of better models for artificial intelligence, machine learning, and data science.

But, instead of collecting as much data as possible hoping that it will help, would it not be more appropriate to ask ourselves the following important question:  Which data is really worth capturing and managing to help us achieve our business objectives?

There is a tendency to want more and more of the valuable resource called data, whether it benefits us or not.  However, more data does not necessarily produce more business value.

The more scattered your data is, the harder it is to use that data to produce information that can be used for decision-making purposes.  The more integrated your data is, the more integrated your entire business is.

Even if your data is not currently all integrated into one central repository, you need to know how it could be, in order to understand your business better and make the right decisions at the right time.

As the volume of data at our disposal keeps growing, so does the need to integrate that data into data repositories that are optimally organized to produce the information that we need.  Failure to do that integration increases the risk of drowning in a sea of data.

The more redundant and disparate are your data assets, the more they are likely to be of poor quality.

Organizations are working with so much data from so many disparate sources and in so many different formats, that it is creating a tremendous cognitive burden on their knowledge workers.

The management of an organization’s data assets should be an horizontal function of that organization, much like the management of that organization’s other assets (human, financial and material).  Most organizations do not have an holistic picture of their data assets, displaying all of the characteristics of those assets (contents, uses, statuses, owners, users, etc.).

Having data of quality is a necessity if you want to keep your organization out of trouble.

Improving the quality of our data should not be considered as a cost exercise, but rather as an investment.

Most organizations suffer from a disorganized data layer that grew by ad hoc accretion, data store by data store.

It is not easy to manage data assets that are stored in data silos that were developed independently of each other.  When data is stored and maintained in separate silos, there is no single version of the truth.  It should be the goal of every organization to integrate its data assets, not only with a simplified and unifying technology platform, but also with a simplified and unifying design of its data structures.

When data is entered in different data repositories, in different ways by different people using different systems, the task of consolidating that data to produce a global view of an organization’s business can be overwhelming, if not impossible.

All technologies impact our lives, which impact can be either positive or negative, depending on our uses of those technologies.  Information technology is no different than the other technologies in that regard, except for the fact that it is mostly badly used when we consider the not so good state of the data that we manipulate with that technology.

Here are two things that grow in parallel: technological complexity and the need for simplicity.

Here are three key questions that, in most organizations, have “no” or “do not know” for an answer:

1. Do we know and understand all of the data that we have?
2. Do we use all of the data that we have?
3. Do we have all of the data that we need?

In order to manage data as a valuable asset, the answer to the above three questions should be “yes”.

Information technology offers a host of tools to manage our data.  However, any tool, as good as it may be, cannot by itself guarantee a job well done.  For our data to be managed in an optimal manner and to be quickly and easily convertible into useful information, whatever the technology used, that data must first be defined, structured and organized in an optimal manner.

Independently of the information technology used, the gathering, collating, curating, and parsing of data will always be functions that are vital to the success of the organizations relying on that data.

For development work to be really productive, it is much more important that it be based on a good specification of the requirements than it be done with the best tools available.

To manage data is to apply order to that data, and, as with anything else that needs to be managed, to apply a constant influx of energy and effort to the maintenance of that order.

Data by itself has no value.  It’s what you do with that data that counts.  Data is merely the main raw material used in the production of useful information.

Data must be protected and used responsibly.

Without accurate, valid and reliable sources of data, a data warehouse is a waste of time and money.

An organization’s information system is essential to its good performance.  Without the right information at the right time and at the right place, an organization cannot hope to achieve optimum performance.  In our increasingly competitive world, this may even be a question of survival.

The construction of an organization’s information system is a huge project when we consider all that it includes, and how much it costs, in terms of software, hardware, procedural and organizational changes, technical support, staff training, etc.

It is a known fact that most of our information systems do not meet all of our expectations and always end up costing us more than expected to develop, modify or use.  It is, however, a less known fact that one of the main causes of this deplorable state of affairs is a deficient specification of the functions, components and characteristics of those systems.

Any good information system should be constructed on the basis of specifications that are both complete and concise, both detailed and clear, and usable by users of the system as well as by its developers and its administrators.

In the absence of proper specifications, an information system inevitably ends up being plagued by incompatibility problems among its various components, and by numerous cases of duplicate, inconsistent or inaccurate data.  This results in costly mistakes, in high usage, maintenance and support costs, and in a high level of dissatisfaction among the users and administrators of the system.

Organizations need to take a personalized approach to creating or finding the right solution that fits their needs.  Sometimes, leveraging information technology they already use can be more advantageous than trying the one that is the most up-to-date or the most popular.

A solution that is simple to think about is not necessarily a solution that is simple to execute.

The earlier a problem is detected, the less it costs to fix.

Business managers want and need information systems that are better integrated with their business processes and that give them better access to relevant business information.

As the decisions and actions made by managers can only be as good as the business data that they are based on, it can be very profitable to ensure that those managers’ information systems manage their data in an optimal manner.

The systems development community is more inclined to admire an information system for its technical excellence rather than for the way it supports a business domain.  Information technology should support, not drive, your information management solutions.

More than earth-shattering technology, what is required is smarter applications of existing technology.

Technology is a double-edged sword:  it can be an enormous time saver, but it can also be an enormous time waster if things aren’t working well or if people don’t know how to make the best use of the technology that is provided to them.

The information technology industry is very good at inventing new terms for old concepts (examples: cloud computing, data governance, metadata, data mining, business intelligence, quality assurance).

Work on the cosmetic aspects of software applications has become more fashionable than work on those applications’ data bases, thanks to new opportunities brought about by advances in graphical environments.

The more support a software application requires, the more broken it is.

Your software applications should be easy to use to allow their users to focus on the real aspects of their work.  This is how information technology can really help to increase the productivity of your entire organization.

The effectiveness of an information system is not limited to the technologies it uses.  One must not forget the non-technological components of information systems, such as the involvement and training of their users.

We are not productive when we use information technology to automate organizations, methods or procedures that are inefficient.  To make the most of these technologies, we must change our ways.  For some of our sectors of economic activity, it is not just a matter of productivity, it’s a matter of survival!

To take full advantage of information technology, organizations must adapt their business processes to maximize their use of that technology.

It is a fact that there is often a huge gap between the information management solutions being used and the requirements of the people and organizations using those solutions.  Many articles have been written on that subject and most of them concur in saying that a deficient specification of those requirements has a lot to do with the size of that gap.

Information systems must be aligned perfectly with the business domains that they support in order to generate a positive return on investment.

That alignment is only possible if all the components of those business domains are well defined and documented for computerization purposes as well as for communication purposes, which is what a good business domain model allows us to do.

The documentation accompanying an information system is an important component of that information system.  Not only does it allow us to communicate what that information system is all about, but it is also essential for purposes of controlling and monitoring the development, operation and maintenance of that information system.  Unfortunately, in most organizations, that documentation is either incomplete, out of date, or totally missing.

In order to evaluate and mitigate the impact of changes to the components of an information system on the quality of the data managed by that system, one must have access to complete and reliable documentation.

Data must be supported by good documentation to be meaningful to its users.  Without that good documentation, data can be misinterpreted or, worse, can be misused and lead to catastrophic results.

It is impossible to manage data appropriately when one does not know what the data to be managed is, or what it means, or why it is worth managing.

Misunderstood data or incomplete data requirements can impede the successful outcome of any information systems project.

A complete and accurate model of a business domain should be produced and be used to guide and manage all systems development and improvement efforts.

When we model and analyze the information system, present or future, of an organization, we have a unique opportunity to streamline the business processes of that organization, because that modelling and analysis exercise generally results in an in-depth understanding of the organization’s business domain, in the identification of improvements to apply to the organization’s business processes, and in the identification of solutions to problems that impede the execution of those processes.

It is often said that, in the business world, good exchanges and good relations between the various actors of that world (suppliers, customers, employees, subcontractors, governments, etc) are crucial to the success of business endeavours.  We could say something similar for the world of information systems: good exchanges of data and good relationships between the various components of an information system are crucial to its proper functioning.  We must therefore understand very well those exchanges of data and inter-component relationships before changing anything in such a system, otherwise, any change could have a negative impact on its functioning.

A model of an organization’s information system that is organization-wide, i.e., that is fully comprehensive in terms of its coverage of that organization’s informational and functional requirements, would be of great help in guiding that organization’s projects aimed at making it more effective (doing the right thing) and more efficient (doing it right) in its use of information technology.

The projects that we are talking about here include the development of data warehouses and business intelligence solutions, the integration of existing data bases and software applications, the acquisition and implementation of software suites, and the development and implementation of new customized solutions using information technology.

Most of the time, though, a comprehensive model of an organization’s information system is not available to guide those projects, which is one of the main reasons for those projects’ low success rate.

Modelling one’s data assets is essential to the understanding those assets’ meanings, interrelationships and rules.

It is not just a question of having the most comprehensive view of all the available data, but also of having the best possible understanding of what that data means in the context of one’s business.

Most of the processes comprised in an information system act on data.  They do not vary much from an information system to another, except for the data structures that they operate on and the rules that they implement while operating on those data structures.  Whatever these structures may be, they are the ones determining the nature and complexity of the processes required to manage them.

A functional design can only be as good as the data base design on which it is based.  In other words, if the data base design is not well thought out or is flawed, the corresponding information processes are doomed to be flawed or, at best, inefficient.

Data are what ties the processes together, allowing them to communicate and cooperate.  Clearly then, data structures must be the foundation on which processes are built.  Just like a house can only be as solid as its foundation, information processes can only be as solid as the data structures underlying them.

If the structure of your data base does not match the way some people want to have access to the information, the use of your system will be frustrating for those people and may lead to wrong decisions being taken.

Data can be useful only if it is accessible and usable.

Data is the key to unlocking insight and developing intelligent solutions across every sector.

In most large organizations, one often has to overcome multiple operational hurdles just to get some kind of access to the data that one needs to do one’s job.

A lot of professionals trained in using data are spending most of their time just trying to find that data.

Using an object-oriented view of an information system, the data should be at the center of the whole system and the processes should be wrapped around the data.  The data structures must then be designed and built before the processes.

If the data base is well designed and put at the very center of the whole information system, the latter has a lot more chances of working smoothly and of being easily modifiable.  The user interface and technology interface of such an information system could be reused in other information systems by wrapping them around different data.

A direct corollary to the above theory stipulates that if you design and develop all components of a new information system from scratch, you are reinventing the information processing wheel or, at least, parts of it.

The application by application approach to the development of information systems, not only creates information silos, it also creates redundancies and inconsistencies in business data and rules.  This results in information systems that are overly complex and insufficiently useful, in terms of the solutions that they provide.

In the end, the real value of an information system is in the information that it can generate from the data that it manages.  To increase that value, one must then work on the data, before anything else.