29 May 2015

Knowledge Management: Keeping Current or the Quest to Lifelong Learning for IT Professionals


    The pace with which technologies and the business changes becomes faster and faster. If 5-10 years back a vendor needed 3-5 years before coming with a new edition of a product, nowadays each 1-2 years a new edition is released. The release cycles become shorter and shorter, vendors having to keep up with the changing technological trends. Changing trends allow other vendors to enter the market with new products, increasing thus the competition and the need for responsiveness from other vendors. On one side the new tools/editions bring new functionality which mainly address technical and business requirements. On the other side existing tools functionality gets deprecated and superset by other. Knowledge doesn’t resume only to the use of tools, but also in the methodologies, procedures, best practices or processes used to make most of the respective products. Evermore, the value of some tools increases when mixed, flexible infrastructures relying on the right mix of tools working together.

    For an IT person keeping current with the advances in technologies is a major requirement. First of all because knowing modern technologies is a ticket for a good and/or better paid job. Secondly because many organizations try to incorporate in their IT infrastructure modern tools that would allow them increase the ROI and achieve further benefits. Thirdly because, as I’d like to believe, most of the IT professionals are eager to learn new things, keep up with the novelty. Being an adept of the continuous learning philosophy is also a way to keep the brain challenged, other type of challenge than the one we meet in daily tasks.

Knowledge Sources

    Face-to-face or computer-based trainings (CBTs) are the old-fashioned ways of keeping up-to-date with the advances in technologies though paradoxically not all organizations afford to train their IT employees. Despite of affordable CBTs, face-to-face trainings are quite expensive for the average IT person, therefore the IT professional has to reorient himself to other sources of knowledge. Fortunately many important Vendors like Microsoft or IBM provide in one form or another through Knowledge Bases (KB), tutorials, forums, presentations and Blogs a wide range of resources that could be used for learning. Similar resources exist also from similar parties, directly or indirectly interested in growing the knowledge pool.

    Nowadays reading a book or following a course it isn’t anymore a requirement for learning a subject. Blogs, tutorials, articles and other types of similar material can help more. Through their subject-oriented focus, they can bring some clarity in a small unit of time. Often they come with references to further materials, bring fresh perspectives, and are months or even years ahead books or courses. Important professionals in the field can be followed on blogs, Twitter, LinkedIn, You Tube and other social media platforms. Seeing in what topics they are interested in, how they code, what they think, maybe how they think, some even share their expertize ad-hoc when asked, all of this can help an IT professional considerably if he knows how to take advantage of these modern facilities.

    MOOCs start to approach IT topics, and further topics that can become handy for an IT professional. Most of them are free or a small fee is required for some of them, especially if participants’ identity needs to be verified. Such courses are a valuable resource of information. The participant can see how such a course is structured, what topics are approached, and what’s the minimal knowledge base required; the material is almost the same as in a normal university course, and in the end it’s not the piece of paper with the testimonial that’s important, but the change in perspective we obtained by taking the course. In addition the MOOC participant can interact with people with similar hobbies, collaborate with them on projects, and why not, something useful can come out of it. Through MOOCs or direct Vendor initiatives, free or freeware versions of software is available. Sometimes the whole functionality is available for personal use. The professional is therefore no more dependent on the software he can use only at work. New possibilities open for the person who wants to learn.

Maximizing the Knowledge Value

    Despite the considerable numbers of knowledge resources, for an IT professional the most important part of his experience comes from hand-on experience acquired on the job. If the knowledge is not rooted in hand-on experience, his knowledge remains purely theoretical, with minimal value. Therefore in order to maximize the value of his learning, an IT professional has to attempt using his knowledge as much and soon as possible in praxis. One way to increase the value of experience is to be involved in projects dealing with new technologies or challenges that would allow a professional to further extend his knowledge base. Sometimes we can choose such projects or gain exposure to the technologies, though other times no such opportunities can be sized or identified.

    Probably an IT professional can use in his daily duties 10-30% of what he learned. This percentage can be however increased by involving himself in other types of personal or collective (open source or work) projects. This would allow exploring the subjects from other perspective. Considering that many projects involve overtime, many professionals have also a rich personal life, it looks difficult to do that, though not impossible.

    Even if not on a regular basis achievable, a professional can allocate 1-3 hours on a weekly basis from his working time for learning something new. It can be something that would help directly or indirectly his organization, though sometimes it pays off to learn technologies that have nothing to do with the actual job. Somebody may argue that the respective hours are not “billable”, are a waste of time and other resources, that the technologies are not available, that there’s lot of due tasks, etc. With a little benevolence and with the right argumentation also such criticism can be silenced. The arguments can be for example based on the fact that a skilled professional can be with time more productive, a small investment in knowledge can have later a bigger benefit for both parties – employee and employer. An older study was showing that when IT professionals was given some freedom to approach personal projects at work, and use some time for their own benefit, the value they bring for an organization increased. There are companies like Google who made from this type of work a philosophy.

    A professional can also allocate 1-3 hours from his free time while commuting or other similar activities. Reading something before going to bed or as relaxation after work can prove to be a good shut-down for the brain from the daily problems. Where there’s interest in learning something new a person will find the time, no matter how busy his schedule is. It’s important however to do that on a regular basis, and with time the hours and knowledge accumulate.

    It’s also important to have a focused effort that will bring some kind of benefit. Learning just for the sake of learning brings little value on investment for a person if it’s not adequately focused. For sure it’s interesting and fun to browse through different topics, it’s even recommended to do so occasionally, though on the long run if a person wants to increase the value of his knowledge, he needs somehow to focus the knowledge within a given direction and apply that knowledge.

    Direction we obtain by choosing a career or learning path, and focusing on the direct or indirect related topics that belong to that path. Focusing on the subjects related to a career path allows us to build our knowledge further on existing knowledge, understanding a topic fully. On the other side focusing on other areas of applicability not directly linked with our professional work can broaden our perspective by looking at one topic from another’s topic perspective. This can be achieved for example by joining the knowledge base of a hobby we have with the one of our professional work. In certain configurations new opportunities for joint growth can be identified.

    The value of knowledge increases primarily when it’s used in day-to-day scenarios (a form of learning by doing). It would be useful for example for a professional to start a project that can bring some kind of benefit. It can be something simple like building a web page or a full website, an application that processes data, a solution based on a mix of technologies, etc. Such a project would allow simulating to some degree day-to-day situations, when the professional is forced to used and question some aspects, to deal with some situations that can’t be found in textbook or other learning material. If such a project can bring a material benefit, the value of knowledge increases even more.

    Another way to integrate the accumulated knowledge is through blogging and problem-solving. Topic or problem-oriented blogging can allow externalizing a person’s knowledge (aka tacit knowledge), putting knowledge in new contexts into a small focused unit of work, doing some research and see how other think about the same topic/problem, getting feedback, correcting or improving some aspects. It’s also a way of documenting the various problems identified while learning or performing a task. Blogging helps a person to improve his writing communication skills, his vocabulary and with a little more effort can be also a visit card for his professional experience.

    Trying to apply new knowledge in hand-on trainings, tutorials or by writing a few lines of code to test functionality and its applicability, same as structuring new learned material into notes in the form of text or knowledge maps (e.g. concept maps, mind maps, causal maps, diagrams, etc.) allow learners to actively learn the new concepts, increasing overall material’s retention. Even if notes and knowledge maps don’t apply the learned material directly, they offer a new way of structuring the content and resources for further enrichment and review. Applied individually, but especially when combined, the different types of active learning help as well maximize the value of knowledge with a minimum of effort.


    The bottom line – given the fast pace with which new technologies enter the market and the business environment evolves, an IT professional has to keep himself up-to-date with nowadays technologies. He has now more means than ever to do that – affordable computer-based training, tutorials, blogs, articles, videos, forums, studies, MOOC and other type of learning material allow IT professionals to approach a wide range of topics. Through active, focused, sustainable and hand-on learning we can maximize the value of knowledge, and in the end depends of each of us how we use the available resources to make most of our learning experience.

08 May 2015

Data Analytics: Data Analytics (Definitions)

"Business Intelligence procedures and techniques for exploration and analysis of data to discover and identify meaningful information and trends." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"Analytics is the systematic analysis of large databases to solve problems and make informed decisions." (John R Schermerhorn Jr, "Management" 12th Ed., 2012)

"Procedures and techniques for exploration and analysis of data to discover and identify new and meaningful information and trends." (Craig S Mullins, "Database Administration", 2012)

"A data-driven process that creates insight. These processes incorporate a wide variety of techniques and may include manual analysis, reporting, predictive models, time-series models, or optimization models." (Evan Stubbs, "Delivering Business Analytics: Practical Guidelines for Best Practice", 2013)

"A suite of technical solutions that uses mathematical and statistical methods. The solutions are applied to data to generate insight to help organizations understand historical business performance as well as forecast and plan for future decisions." (Jim Davis & Aiman Zeid, "Business Transformation", 2014) 

"Analytics is the discovery and communication of meaningful patterns in data." (Elaine Biech, "ASTD Handbook" 2nd Ed., 2014) 

"The business intelligence and analytics technologies that are grounded mostly in data mining and statistical analysis." (Xiuli He, "Supply Chain Analytics: Challenges and Opportunities", 2014)

"Data analytics refers to qualitative and quantitative techniques and processes used to enhance productivity and business gain." (Piyush K Shukla & Madhuvan Dixit, "Big Data: An Emerging Field of Data Engineering", 2015)

"The act of extracting and communicating meaningful information among the data sets." (Hamid R Arabnia et al, "Application of Big Data for National Security", 2015) 

"A broad term that includes quantitative analysis of data and building quantitative models. Analytics is the science of analysis and discovery. Analysis may process data from a data warehouse, may result in building model-driven DSS, or may occur in a special study using statistical or data mining software. In general, analytics refers to quantitative analysis and manipulation of data." (Daniel J Power & Ciara Heavin, "Decision Support, Analytics, and Business Intelligence" 3rd Ed., 2017)

"A scientific and systematic approach to examine raw data in order to draw valid conclusions about them. Data are extracted and structured, and qualitative and quantitative techniques are used to identify and analyze patterns." (Lesley S J Farmer, "Data Analytics for Strategic Management: Getting the Right Data", 2017)

"Techniques used to identify patterns in data sets. Qualitative and quantitative techniques are employed to derive meaning that may be valuable and could result in a positive business gain for an organization." (Daniel J Power & Ciara Heavin, "Decision Support, Analytics, and Business Intelligence" 3rd Ed., 2017)

"The discovery, interpretation, and communication of meaningful patterns in data to inform decision making and improve performance." (Jonathan Ferrar et al, "The Power of People: Learn How Successful Organizations Use Workforce Analytics To Improve Business Performance", 2017)

"Analytics refers to quantitative and statistical analysis and manipulation of data to derive meaning. Analytics is a broad umbrella term that includes business analytics and data analytics." (Daniel J. Power & Ciara Heavin, "Data-Based Decision Making and Digital Transformation", 2018)

"Involves drawing insights from the data including big data. Analytics uses simple to advanced tools depending upon the objectives. Analytics may involve visual display of data (charts and graphs), descriptive statistics, making predictions, forecasting future outcomes, or optimizing business processes." (Amar Sahay, "Business Analytics" Vol. I, 2018)

"Is the science of examining raw data with the purpose of drawing actionable information from it, data analytics is used to allow companies and organization to make better business decisions and in the sciences to verify or disprove existing theories." (Dennis C Guster, "Scalable Data Warehouse Architecture: A Higher Education Case Study", 2018)

"Data analytics is a process that examines, clears, converts and models data to explore useful information, draws conclusions and supports decision making." (A Aylin Tokuç, "Management of Big Data Projects: PMI Approach for Success", 2019)

"A rapidly emerging field of information science arising from the explosion of data generated by many Internet based applications and services. Data analytics embodies a sequential process of descriptive, diagnostic, predictive and prescriptive analytics. Each type has a different purpose and requires different techniques to gain meaningful outcomes. The latter two often employ machine learning to gain valuable insights and directional guidance in decision making, such as in self-driving automobiles." (Darrold L Cordes et al, "Transforming Urban Slums: Pathway to Functionally Intelligent Cities in Developing Countries", 2021)

"Discovery, interpretation, and communication of meaningful patterns in data; and the process of applying those patterns towards effective decision making." (Francisco S Gutierres & Pedro M Gome, "The Integrated Tourism Analysis Platform (ITAP) for Tourism Destination Management", 2021)

"The science of extracting meaningful information continuously with the assistance of specialized system for finding patterns to get feasible solutions." (Selvan C & S  R Balasundaram, "Data Analysis in Context-Based Statistical Modeling in Predictive Analytics", 2021)

"Analytics encompasses the discovery, interpretation, and communication of meaningful patterns in data. It relies on the simultaneous application of statistics, computer programming and operations research to quantify performance and is particularly valuable in areas with large amounts of recorded information. The goal of this exercise is to guide decision-making based on the business context. The analytics flow comprises descriptive, diagnostic, predictive analytics and eventually prescriptive steps." (Accenture)

"Data Analytics describes the end-to-end process by which data is cleaned, inspected and modeled. The objective is to discover useful and actionable information that supports decision-making." (Accenture)

"Data analytics enables organizations to analyze all their data (real-time, historical, unstructured, structured, qualitative) to identify patterns and generate insights to inform and, in some cases, automate decisions, connecting intelligence and action." (Tibco) [source]

"Data analytics is a set of technologies and practices that reveal meaning hidden in raw data." (Xplenty) [source]

"Data and analytics is the management of data for all uses (operational and analytical) and the analysis of data to drive business processes and improve business outcomes through more effective decision making and enhanced customer experiences." (Gartner)

"Data analytics (DA) is the process of examining data sets in order to draw conclusions about the information they contain, increasingly with the aid of specialized systems and software." (Techtarget) [source]

"Data analytics is the process of querying and interrogating data in the pursuit of valuable insight and information." (snowflake) [source]

"Data analytics is the pursuit of extracting meaning from raw data using specialized computer systems. These systems transform, organize, and model the data to draw conclusions and identify patterns." (Informatica) [source]

"Data analytics refers to the use of processes and technology to combine and examine datasets, identify meaningful patterns, correlations, and trends in them, and most importantly, extract valuable insights." (Qlik) [source]

"The discovery, interpretation, and communication of meaningful patterns in data. They are essentially the backbone of any data-driven decision making." (Insight Software)

"The process and techniques for the exploration and analysis of business data to discover and identify new and meaningful information and trends that allow for analysis to take place."(Information Management)
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