Tag: knowledge management

Business Intelligence: Predictions

The future of …

  • Data mining:

o    Web structure mining (studying the web structure of web pages) and web usage analysis (studying the usage of web pages) will become more prominent in the future.  Victor and Rex (2016) stated that web mining differs from traditional data mining by scale (web information is much larger in number, making 10M web pages seem like it’s too small), access (web information is mostly public, whereas traditional data could be private), and structure (web pages have unstructured, and semi-structured data, whereas traditional data mining, has some explicit level of structure).  The structure of a website can contain: Page Rank, Page number, Damping factor, Number of pages, out-links, in-links, etc.  Your page is considered an authoritative piece if there are many in-links, or it can be considered a hub if it has many out-links, and this helps define page rank and structure of the website (Victor & Rex, 2016).  But, page rank is too trivial of calculation.  One day we will be able to not only know a page rank of a website, but learn its domain authority, page authority, and domain validity, which will help define how much value a particular site can bring to the person.  If Google were to adopt these measures, we could see

  • Data mining’s link to knowledge management (KM):

o    A move towards the away from KM tools and tool set to seeing knowledge as being embedded into as many processes and people as possible (Ferguson, 2016). KM relies on sharing, and as we move away from tools, processes will be setup to allow this sharing to happen.  Sharing occurs more frequently with an increase in interactive and social environments (Ferguson, 2016).  Thus, internal corporate social media platforms may become the central data warehouse, hosting all kinds of knowledge.  The issue and further research need to go into this, is how do we more people engaged on a new social media platform to eventually enable knowledge sharing. Currently, forums, YouTube, and blogs are inviting, highly inclusive environments that share knowledge, like how to solve a particular issue (evident by YouTube video tutorials).  In my opinion, these social platforms or methods of sharing, show the need for a more social, inclusive, and interactive environment needs to be for knowledge sharing to happen more organically.

o    IBM (2013), shows us a glimpse of how knowledge management from veteran police officers, crime data stored in a crime data warehouse, the power of IBM data mining, can be to identifying criminals.  Mostly criminals commit similar crimes with similar patterns and motives.  The IBM tools augment officer’s knowledge, by narrowing down a list of possible suspects of crime down to about 20 people and ranking them on how likely the suspects committed this new crime.  This has been used in Miami-Dade County, the 7th largest county in the US, and a tool like this will become more widespread with time.

  • Business Intelligence (BI) program and strategy:

o    Potential applications of BI and strategy will go into the health care industry.  Thanks to ObamaCare (not being political here), there will be more data coming in due to an increase in patients having coverage, thus more chances to integrate: hospital data, insurance data, doctor diagnosis, patient care, patient flow, research data, financial data, etc. into a data warehouse to run analytics on the data to create beneficial data-driven decisions (Yeoh, & Popovič, 2016; Topaloglou & Barone, 2015).

o    Potential applications of BI and strategy will affect supply chain management.  The Boeing Dreamliner 787, has outsourced 30% of its parts and components, and that is different to the current Boeing 747 which is only 5% outsourced (Yeoh, & Popovič, 2016).  As more and more companies increase their outsourcing percentages for their product mix, the more crucial is capturing data on fault tolerances on each of those outsourced parts to make sure they are up to regulation standards and provide sufficient reliability, utility, and warranty to the end customer.  This is where tons of money and R&D will be spent on in the next few years.

References

  • Ferguson, J. E. (2016). Inclusive perspectives or in-depth learning? A longitudinal case study of past debates and future directions in knowledge management for development. Journal of Knowledge Management, 20(1).
  • IBM (2013). Miami-Dade Police Department: New patterns offer breakthroughs for cold cases. Smarter Planet Leadership Series.  Retrieved from http://www.ibm.com/smarterplanet/global/files/us__en_us__leadership__miami_dade.pdf
  • Topaloglou, T., & Barone, D. (2015) Lessons from a Hospital Business Intelligence Implementation. Retrieved from http://www.idi.ntnu.no/~krogstie/test/ceur/paper2.pdf
  • Victor, S. P., & Rex, M. M. X. (2016). Analytical Implementation of Web Structure Mining Using Data Analysis in Educational Domain. International Journal of Applied Engineering Research, 11(4), 2552-2556.
  • Yeoh, W., & Popovič, A. (2016). Extending the understanding of critical success factors for implementing business intelligence systems. Journal of the Association for Information Science and Technology, 67(1), 134-147.

Business Intelligence: Corporate Planning

Corporate Planning

The main difference between business planning and corporate planning is the actors.  They both are defining strategies that will help the meet the business goals and objectives.  However, business planning is describing how the business will do it, through focusing on business operations, marketing, and products and services (Smith, n.d).  Meanwhile, corporate planning is describing how the employees will do it, through focusing on staff responsibilities and procedures (Smith, n.d.).  Smith (n.d.) implied that corporate planning will succeed if it is aligned with the company’s strategy and missions, drawing on the strengths and improving on its weaknesses. A successful and realistic corporate and business plan can help the company succeed.  Business Intelligence can help in creating these plans.  In order to make the right plans, we must make better decisions that help the company out, and data-driven decisions (through Business Intelligence).  Business Intelligence, will help provide answers to questions much faster and quite easily, make better use of the corporate time, and finally aid in making improvements for the future (Carter, Farmer, & Siegel, 2014).

A small, medium, or large organization deals with planning differently, so BI solutions are not a one-size-fits-all.  Small companies have the freedom, creativity, motivation, and flexibility that large companies lack (McNurlin, Sprague, & Bui, 2008).  Large companies have the economies of scales and knowledge that small companies do not (McNurlin et al., 2008).  Large companies are beginning to advocate centralized corporate planning yet decentralized execution, which is a similar structure of a medium size company (McNurlin et al., 2008).  Thus, medium size companies have the benefits of both large and small companies, but also the disadvantages of both.  Unfortunately, a huge drawback on large organizations is a fear of collaboration and tightly holding onto their proprietary information (Carter et al., 2014). The issues of holding tightly to proprietary information and lack of collaboration is not conducive for a solid Knowledge Management nor Business Intelligence plan.

Business Intelligence

Business Intelligence uses data to create information that helps with data-driven decisions, which can be especially important for corporate planning.  Thus, we can reap the benefits of Business Intelligence to make data-driven decisions, if we balance the needs of the company, corporate vision, and the size of the company to help in choosing which models the company should use.  A centralized model is when one team in the entire corporation owns all the data and provides all the needed analytical services (Minelli, Chambers, & Dhiraj, 2013).  A decentralized model of Business Intelligence is where each business function owns its data infrastructure and a team of data scientists (Minelli et al., 2013).  Finally, Minelli et al. (2013) defined that a federated model is where each function is allowed to access the data to make data-driven decisions, but also ensures that it is aligned to a centralized data infrastructure.

Knowledge Management

McNurlin et al. (2008), defines knowledge management as managing the transition between two states of knowledge, tacit (information that is privately kept in one’s mind) and explicit knowledge (information that is made public, which is articulated and codified). We need to discover the key people who have the key knowledge, which will aid in knowledge sharing to help benefit the company.  Knowledge management can rely on technology to be captured and share appropriately such that it can be used to sustain the individual and sustain the business performance (McNurlin et al., 2008).

Knowledge management can also include domain knowledge (knowledge of a particular field or subject).  The inclusion of domain knowledge into a data mining, which is a component of Business Intelligence System has aided in pruning association rules to help extract meaningful data to aid in developing data-driven decisions (Cristina, Garcia, Ferraz, & Vivacqua, 2009).  In this particular study, engineers helped to build a domain understanding to interpret the results as well as steer the search of specific if-then rules, which helped to find more significant patterns in the data (Cristina et al. 2009).

The addition of domain experts helped captured tacit knowledge and transformed it into explicit knowledge, which was then used to find significant patterns in the data that was collected and mined through.  This eventually leads to a more manageable set of information with high significance to the company to which data-driven decisions can be made to support the corporate planning. Thus, knowledge management can be an integral part of Business Intelligence.  Finally, Business Intelligence uses data to create information that when introduced with experience of the employees (through knowledge management) it can then create explicit knowledge, which can provide more meaningful data-driven decisions than if one were to focus on a Business Intelligence Systems alone.

The effectiveness of capturing and adding domain knowledge into a company’s Business Intelligence System depends on the quality of employees in the company and their willingness to share that knowledge.  At the end of the day, a corporate plan that focuses on staff responsibilities and procedures revolving both in Business Intelligence and Knowledge Management will gain more insights and a higher return on investment that will eventually feed back into the corporate and business plans.

References

  • Carter, K. B., Farmer, D., & Siegel C., (2014). Actionable Intelligence: A Guide to Delivering Business Results with Big Data Fast!. John Wiley & Sons P&T. VitalBook file.
  • Cristina, A., Garcia, B., Ferraz, I., & Vivacqua, A. S. (2009). From data to knowledge mining. http://doi.org/10.1017/S089006040900016X
  • McNurlin, B., Sprague, R., Bui, T. (2008). Information Systems Management, 8th Edition. Pearson Learning Solutions. VitalBook file.
  • Minelli, M., Chambers, M., and Dhiraj A. (2013). Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today’s Businesses. John Wiley & Sons P&T. VitalBook file.
  • Smith, C. (n.d.) The difference between business planning and corporate planning. Small Business Chron. Retrieved from http://smallbusiness.chron.com/differences-between-business-planning-corporate-planning-882.html

Business Intelligence: Effectiveness

Non-profit Hospitals are in a constant state of trying to improve their services and drive down costs. Thus, one of the ways they do this is by turning to Lean Six Sigma techniques and IT to identify opportunities to save money and improve the overall patient experience. Six Sigma relies on data/measurements to determine opportunities for continuous improvements, thus aiding in the hospitals goals, a Business Intelligence (BI) program was developed (Topaloglou & Barone, 2015).

Key Components of the structure

For an effective BI program the responsible people/stakeholders (Actors) are identified, so we define who is responsible for setting the business strategies (Goals).  The strategy must be supported by the right business processes (Objects), and the right people must be assigned as accountable for that process.  Each of these processes has to be measured (Indicators) to inform the right people/stakeholders on how the business strategy is doing.  All of this is a document in a key document (called AGIO), which is essentially a data definition dictionary that happens to be a common core solution (Topaloglou & Barone, 2015).  This means that there is one set of variables names and definitions.

Implementation of the above structure has to take into account the multi-level business and their needs.  Once the implementation is completed and buy off from all other stakeholders has occurred, that is when the business can experience its benefits.  Benefits are: end users can make strategic data based decisions and act on them, a shift in attitudes towards the use and usefulness of information, perception of data scientist from developers to problem solvers, data is an immediate action, continuous improvement is a byproduct of the BI system, real-time views with data details drill down features enabling more data-driven decisions and actions, the development of meaningful dashboards that support business queries, etc. (Topaloglou & Barone, 2015).

Knowledge management systems fit into the structure

“Healthcare delivery is a distributed process,” where patients can receive care from family doctors, clinicians, ER staff,  specialists, acute care, etc. (Topaloglou & Barone, 2015).  Each of these people involved in healthcare delivery have vital knowledge about the patient that needs to be captured and transferred correctly; thus hospital reports help capture that knowledge.  Knowledge also lies with how the patient flows in and out of sections in the hospital, and executives need to see metrics on how all of these systems work together.  Generating a knowledge management distributed database system (KMDBS), aids in tying all this data together from all these different sources to provide the best care for patients, identify areas for continual improvements, and provides this in a neat little portal (and dashboards) for ease of use and ease of knowledge extraction (Topaloglou & Barone, 2015).  The goal is to unify all the knowledge from multiple sources into one system, coming up with a common core set of definitions, variables, and metrics.  The common core set of definitions, variables, and metrics are done so that everyone can understand the data in the KMDBS, and look up information if there are any questions.  The development team took this into account and after meeting with different business levels, the solution that was developed in-house provided all staff a system which used their collective knowledge to draw out key metrics that would aid them in data-driven decisions for continuous improvement on the services they provide to their patients.

1 example

Topaloglou & Barone, (2015) present the following example below:

  • Actor: Emergency Department Manger
  • Goal: Reduce the percentage of patients leaving without being seen
  • Indicator: Percentage of patients left without being seen
  • Object: Physician initial assessment process

 

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