Analytics and business intelligence can enable making good decisions, acting efficiently, optimizing processes, inventing and innovating, communicating effectively, influencing people, and improving business performance.
This post will define the key terms, list the key benefits, and connect them to KM strategy and knowledge flow.
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.
Content from Lucidea
Please enjoy Stan’s additional blog posts offering advice and insights drawn from many years as a KM practitioner. You may also want to download a copy of his book, Proven Practices for Implementing a Knowledge Management Program, from Lucidea Press. And learn about Lucidea’s Presto and SydneyEnterprise with KM capabilities to support successful knowledge curation and sharing.
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KM thought leaders; JoAnn Girard works on many knowledge intensive issues including information anxiety, enterprise dementia, organizational memory
KM thought leaders; Susan Feldman was known for her expertise in search, text analytics, and cognitive computing.
KM thought leaders; Lilia Efimova is passionate about technology, understanding how people work, social media, changing workplace, knowledge, learning.
KM thought leaders; Amy Edmondson is a thought leader in organizational learning, cross-boundary teaming, innovation, eliminating fear in organizations