Analytics and Business Intelligence
49th in a series of?50 Knowledge Management Components?(Slide 63 in?KM 102)
Definitions
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:
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:
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.
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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.
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:
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:
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.
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