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Originally published on August 29, 2017

Stan Garfield

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49th in a series of 50 Knowledge Management Components (Slide 63 in KM 102)

Definitions

  • Analytics: discovery and communication of meaningful patterns in data and text
  • Business Intelligence (BI): the ability for an organization to take all its capabilities and convert them into knowledge; includes data mining, data visualization, big data, databases, data warehouses, and data lakes
  • Text analytics: analyzing unstructured text, extracting relevant information, and transforming it into useful business intelligence
  • Data mining: finding anomalies, patterns, and correlations within large data sets to predict outcomes
  • Data visualization: any effort to help people understand the significance of data by placing it in a visual context; patterns, trends and correlations that might go undetected in text-based data can be exposed and recognized easier with data visualization software
  • Big data: extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions
  • Databases: collections of information organized for easy access, management, and updating
  • Data warehouses: copies of transaction data specifically structured for querying and reporting
  • Data lakes: storage repository that holds a vast amount of raw data in its native format, including structured, semi-structured, and unstructured data; the data structure and requirements are not defined until the data is needed
  • Data science: an interdisciplinary field about scientific methods, processes, and systems to extract insights from data in various forms, either structured or unstructured; a concept to unify statistics, data analysis and their related methods in order to understand and analyze actual phenomena with data; employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science, in particular from machine learning, classification, cluster analysis, data mining, databases, and visualization

Uses and Benefits

Analytics and business intelligence can enable making good decisions, acting efficiently, optimizing processes, inventing and innovating, communicating effectively, influencing customer buying, and improving business performance. Here are examples of each:

  • Making good decisions: Decide on the optimal locations for stores, restaurants, manufacturing plants, and other business sites.
  • Acting efficiently: Compare alternative courses of action and their expected impact, and use the results to take the best action.
  • Optimizing processes: Use data visualization to show the bottlenecks in the current process and take steps to eliminate them.
  • Inventing and innovating: Sports teams develop innovative strategies, for example, shifting infielders for each batter in baseball based on their past tendencies.
  • Communicating effectively: Visually convey details of the current state and the desired future state to support the management of change.
  • Influencing customer purchases: When customers shop online, suggest other products to buy — for example, customers who bought this book also bought these other books.
  • Improving business performance: Decide when and where to invest, divest, merge, acquire, and maintain the status quo.

Analytics and BI in KM Strategy and Knowledge Flow

KM Strategy: Analyze

Reviewing collected information can reveal patterns, trends, or tendencies that can be exploited, expanded, or corrected. Distilling data to extract the essence leads to discovering new ideas and learning how to improve.

Knowledge Flow: Discovery

In most organizations there are information systems, transaction processing applications, and databases that are used to run the business. There is data captured in these systems that can be used to distill trends, answer queries, and support decision making. And this can be done without the need to capture data redundantly. For example, if customer purchase information is entered into the order processing system, it can be fed to a data warehouse for use by all departments.

Insights

1. What Is Data Mining? by Oracle

Data mining is the practice of automatically searching large stores of data to discover patterns and trends that go beyond simple analysis. Data mining uses sophisticated mathematical algorithms to segment the data and evaluate the probability of future events. Data mining is also known as Knowledge Discovery in Data (KDD).

The key properties of data mining are:

  • Automatic discovery of patterns
  • Prediction of likely outcomes
  • Creation of actionable information
  • Focus on large data sets and databases

Data mining can answer questions that cannot be addressed through simple query and reporting techniques.

2. Dave Snowden on KM and big data/analytics — interview with David J. Pauleen

Analytics in the form of algorithms are imperfect and can only to a small extent capture the reasoning and analytical capabilities of people. For this reason, while big data/analytics can be useful, they are limited and must be used in conjunction with human knowledge and reasoning.

3. The whole big data idea is being over hyped by Dave Snowden

Too many of the big data guys are just assuming that information will come to them from the misty mountains of the Internet that they can process without human engagement to reveal all that is needed. The simple act of putting human sensors into the system escapes them. Solutions for nerds, by nerds that will dumb us all down to the intelligence of nerds.

4. Visualizing knowledge by Daniel Rasmus

Much has been made recently of knowledge discovery through visualization. As databases get connected and one database becomes a correlated data set or even metadata for another set of data, relationships, even meaning, can be found through visualizations that would take many lines of code and endless SQL queries.

But not all knowledge fits neatly into rows and columns. Knowledge includes metadata, contexts, relationships and other attributes that may be difficult to represent in columns, or even text. Even the richness of video fails because it is really but an enhanced narrative. In any narrative, the writer may make references, generate allusions and link to other ideas. But in most forms, connecting the dots back to the mental model falls to the person receiving the experience. Visual knowledge representation attempts to make those connections part of the experience, to explicitly, and visually, connect ideas. And with software, those links can be dynamically explored unlike any linear narrative.

Modern knowledge visualization tools may have started as simple drawing tools, but they have evolved into collaborative environments that help teams share information effectively, develop shared mental models and provide context in a way no other tool can.

5. Knowledge Management and business intelligence by Nick Milton

Business intelligence is the gathering and supply of business related data and information which can then be used for either supporting decisions, forecasting future events or discovering trends within a set of information.

Knowledge management is about the development of the know-how that allows people to make decisions, based on these (and other) data. It’s what enables an organization to know what to do with the intelligence.

6. Big Data and KM — different but complementary by Nick Milton

Data in itself does not lead to action, without the knowledge being applied. You manage the data itself through data management techniques, and you manage the knowledge itself through knowledge management techniques, and the two together give massively powerful actionable results. Big Data and KM should work hand in hand, but not be treated as the same thing.

7. Big Data, Knowledge, and Hurricanes by Nick Milton

Walmart are winning on three counts. They have plenty of data, they analyze this data to derive information, and they have knowledge (based on experience and codified in best practice) that allows them to take the correct actions. It is that knowledge that allows them to know what to do with the information they receive.

8. The People of the Petabyte by Venkatesh Rao

Here are just a few examples of title/label wars that I’ve heard mentioned.

  • Data mining vs. machine learning: One speaker mentioned that people who used to go by the title data miner are now offended by the term and prefer to be called machine learning experts. There are differences in substance and connotations, but for whatever mysterious reason, the stock of the latter term appears to be appreciating. Those who switched titles early enough would have benefited while those who were a little less alert have left themselves open to the charge of band-wagonism.
  • BI/DW vs. Big Data: On the industry side of the fence, many business intelligence and data warehousing veterans are smartly repackaging themselves as Big Data people. Again, there is an underlying tension. In this case, a generational tension between mid-career, middle-management types who want to find roles in the new game, and a younger set trying to differentiate itself by defining the new game in more exclusive ways. Is it the same old game or a new game? Certainly there are new technological elements that everybody acknowledges, but the significance of those elements depends on whether you are a veteran or a fresh young type.
  • Analysts vs. Analytics: People who pulled data, crunched it, and turned it into presentations used to be called analysts. Now those who wrangle real-time data streams and steward processing pipelines that feed live dashboards call themselves analytics experts. It is a similar skillset, but a different mindset. Again, there is a faultline with simmering tension.

The fact that people have converged on a strange truce — calling everybody a data scientist — is interesting.

9. How to Integrate Data and Analytics into Every Part of Your Organization by Carl Carande, Paul Lipinski, and Traci Gusher

The National Basketball Association is a good example of an organization that is making the most of its Data and Analytics (D&A) function, applying it in scheduling to reduce expenses, for example, reducing the need for teams to fly from city to city for games on back-to-back nights. For the 2016–2017 season, thousands of constraints needed to be taken into account related to travel, player fatigue, ticket revenue, arena availability, and three major television networks. With 30 teams and 1,230 games in a regular season stretching from October into April, trillions of scheduling options were possible.

The league used D&A to arrive at a schedule that:

  • reduced the number of games teams played on consecutive nights by 8.4%
  • reduced instances of teams playing four games in five days by 26%
  • reduced instances of teams playing five games in seven days by 19%
  • increased the number of consecutive games teams played without traveling by 23%
  • allowed each team to appear on one of the league’s premier TV networks at least once, a success that had not been achieved in the league in any prior year

10. SIKM Leaders Discussion on KM and BI

a. Alice MacGillivray

I would say that BI greatly impacted KM but not the reverse. We did use a few home grown KM-like techniques in the BI project (similar to Action Reviews and Peer Assists), but the BI work was the catalyst for people from different fields and organizations sitting down and talking in depth when they hadn’t before. We didn’t map social networks, but the changes in knowledge flow were dramatic.

Instead of a sort of contradictory, one-upmanship, zero sum kind of process for land management decisions, the ad hoc reporting from the BI tool (Oracle Discoverer at that time) prompted people to:

  • share new knowledge
  • agree or disagree with the increasingly refined priority lists generated through BI
  • justify decisions
  • collaborate in their implementation

b. Matt Moore

What should link KM and BI is a concern with improved decision making. However, KM often ends up being simply about implementing SharePoint and collecting some documents. BI ends up being about producing reports based on operational data (e.g., pulled from an SAP system) or dashboards based on the same. Now document storage and report generation are reasonable things to do, but they do not necessarily lead to better decision making.

c. Murray Jennex

I agree that KM and BI are about decision making, and that KM strategy should focus on supporting decision making. I also believe that BI is really a subset of KM. To be honest I think all of the “new” initiatives such as BI and CI (customer intelligence) are just different ways of packaging KM.

Resources

  1. SIKM Leaders Community Threads — Analytics — Data Science
  2. LinkedIn Topics: Analytics — Business Intelligence — Big Data
  3. SlideShare: Analytics — Business Intelligence — Big Data
  4. Knowledge Management and Business Intelligence by Richard Herschel
  5. Why Visualize Data? by Vasavi Ayalasomayajula
  6. Data Visualization Tools
  7. International Journal of Data Mining & Knowledge Management Process
  8. Warehousing Data: The Data Warehouse, Data Mining, and OLAP
  9. Knowledge management and business intelligence by Azmi Taufik
  10. Diving into the Data Lake with Hadoop: The 5 Things You Need to Know by Keen Hahn
  11. APQC: Data and Analytics and Analytics and Big Data and Business Intelligence
  12. Shawn Callahan: Helping Big Data Scientists be Storytellers and The role of stories in data storytelling
  13. KMWorld: Big Data and Business Intelligence
  14. Big opportunities in small data by Art Murray and Paul Pruett
  15. Too much information: Better health from big data by Jessica Hamzelou
  16. Matt Moore: Big data for information managers and Visualization may be the point where the quantitative & the qualitative meet
  17. Content Analytics and IBM Watson by Sean Fox
  18. Social networking with text mining and analytics for KM by Ken Martin
  19. Search Analytics: Understanding the Long Tail by Lee Romero
  20. KMWorld 2013 Keynote — Human data v Big Data by Dave Snowden
  21. Tom Davenport

Software

  1. Top Business Intelligence Software Products by Capterra
  2. Top Big Data Software Products by Capterra
  3. Top Data Visualization Software Products by Capterra
  4. Top Database Management Software Products by Capterra
  5. Business Intelligence Tools by Software Advice
  6. The Best Self-Service Business Intelligence (BI) Tools by Pam Baker
  7. Top Embedded Analytics Business Intelligence Software
  8. 10 Cloud Analytics & BI Platforms For Business by Doug Henschen
  9. Understanding BI analytics tools and their benefits by Rick Sherman
  10. The best business intelligence (BI) software by Zapier
  11. Best Business Intelligence Software by G2
  12. Top Business Intelligence software tools by Passioned Group

Training

  1. edX: Knowledge Management and Big Data in Business
  2. Coursera Specializations

Books

  1. Analytics
  2. Text Analytics
  3. Business Intelligence
  4. Data Warehouses
  5. Data Lakes
  6. Big Data
  7. Databases
  8. Data Mining
  9. Data Visualization
  10. Competing on Analytics: Updated, with a New Introduction: The New Science of Winning by Tom Davenport and Jeanne Harris
  11. Keeping Up with the Quants: Your Guide to Understanding and Using Analytics by Tom Davenport and Jinho Kim
  12. Analytics at Work: Smarter Decisions, Better Results by Tom Davenport, Jeanne Harris, and Robert Morison
  13. Big Data at Work: Dispelling the Myths, Uncovering the Opportunities by Tom Davenport
  14. Intelligent Knowledge: A Study beyond Data Mining by Yong Shi, Lingling Zhang, Yingjie Tian, and Xingsen Li
  15. Data Warehousing and Data Mining: Implementing Strategic Knowledge Management by Elliot King
  16. A Research Agenda for Knowledge Management and Analytics edited by Jay Liebowitz

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Stan Garfield

Written by Stan Garfield

2.2K Followers

Knowledge Management Author and Speaker, Founder of SIKM Leaders Community, Community Evangelist, Knowledge Manager https://sites.google.com/site/stangarfield/