Business Intelligence, Data Nerds, Data Science, IT Management, Learning New Things

#Mindmaps for Making Sense of Data

Continual Improvement

I have had a little time in between all the data quality issues so I am practicing the mind-maps.

When i was student, I used mind-maps, use-cases and information models to make notes. I am a connection kind of person, I learn by connecting one thing to the other. I am reading through Donald J. Wheeler’s, Making sense of data. To understand it all, I will need all the mind-maps I can make.

In the 1st chapter, Mr. Wheeler presents quite briefly the Continual Improvement approach.

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Business Intelligence, Data Nerds, Data Science, IT Management

Data Quality is Our Day Job

Data quality should be everyone’s job. And it is in the business intelligence department. It is not a glamorous dress up, smile and relax profession. It is a sweaty, bloody and teary frustrating grind. Some days, it is stimulating and fun!

50% of working hrs — BI resources are in hidden data factories. Hunting for data, finding and correcting errors, and searching for confirmatory sources for data they don’t trust.

60% of working hrs — Data scientists spend cleaning and organizing data.

75% — Total cost associated with hidden data factories in simple operations, where resources are (1) Verifying/validating data quality or (2) Resolving data quality issues.

Friday Afternoon MeasurementThere are those who have successfully tried the Friday Afternoon Measurement – Spending Friday afternoons measuring the data quality for the previous week. This also helps to qualify and quantify the activities that will be needed to improve data quality the following week.

The One in Ten Rule is also a good method/tool to measure data quality.

You cannot change that which you do not know or understand.

Business Intelligence, Data Nerds, Data Science, IT Management, Learning New Things

To Enjoy Data and BI, Learn to Learn and Don’t be afraid of Change

“The illiterate of the 21st century will not be those who cannot read and write, but those who cannot learn, unlearn, and relearn.” — Alvin Toffler

BI and Data Science are the artistic, creative side of finding truths in unexpected nooks and crannies of organizations and societies. Finding links and interpreting the messages being received from the different interconnections and integrations between systems to ensure a holistic view of the world and of organizations. If you want to enjoy working with data, learn to learn and unlearn everything all the time. Because what you know today, what data tells you this minute, may change in two hours.

AI and LearningAI is changing our answer to “what will you be when you grow up?” The answer is not always nurse, pilot, accountant or all the cute things we said when we were children! In this age, you can say AI manager, Robot Designer or Machine Learner without blinking an eye. It is our future and imagine it! We need to imagine it positively in order to embrace our future so we can manage it.

No fear, even the AI-predictions may change, and the course of our lives will be altered. That is OK. We will learn whatever comes.

Don’t waste energy on trying to control the change, you cannot control the change, because it is out of our small hands. Change may happen because data and statistics can be manipulated or due to the fact that our virtual, online realities are changing every hour. A post on twitter may change the sales predictions your sales team made yesterday. An online AD today may change your expansion vision for 2018.

 

 

Business Intelligence, Data Nerds, Data Science, IT Management

Business Intelligence for beginners

Are you a beginner and want to know the difference between Business Intelligence and data Science?

I have a colleague who worked in Business Intelligence in the 90s. They tell me that they worked in the IT department and their role was Business Intelligence. BI was a role. A one man show. Nowadays, Business Intelligence is a department, a function, an organization. With teams of experts in different roles.

If you are starting out, below are a few tips for a solid start:

  1. Learning the 20% of the skills that you will need 80% of the time. You do this by building a foundation in data analysis. Read some books about data and analytics. Wheeler’s Making Sense of Data is a good start. Listen to Pods about data and analytics for example @ Ted’s.
  2. Learn a code language for Data Handling. ETL and SQL are a good starting point. If you are feeling  challenged and motivated, get up to speed on the basics of R programming or visit DataCamp for free.Or some BI language/tool of your choice.
  3. Lastly, learn the different tasks that need to be performed in a well functioning Business Intelligence/Analytics department. Infrastructure, Architecture, Best Practices, Innovation, Communication etc. If you want to work with data, you will need to learn how to work with other data handlers because data is a traveler. It stops at different stations, picks up new attributes, and then continues on its journey towards you. You will need to understand the road traveled by the data that is coming towards you and the hands that are handling this data for you to be able to deliver sane, reliable data.

Don’t think math. Although you will need some math, you will do very well with minimal math. What you need to understand is logical business processes and their resulting data points.

 

BI Tools, Business Intelligence, Graphic User Interface Design, IT Management

An Introduction to Business Intelligence

Gartner defines Business intelligence (BI) as:

An umbrella term that includes the applications, infrastructure and tools, and best practices that enable access to and analysis of information to improve and optimize decisions and performance.

It is an OK definition. I like OLAP’s definition better:

The term Business Intelligence (BI) refers to technologies, applications and practices for the collection, integration, analysis, and presentation of business information. The purpose of Business Intelligence is to support better business decision making. Essentially, Business Intelligence systems are data-driven Decision Support Systems (DSS).

Finance Online and other more revenue focused organizations go into details and lists about what constitutes Business Intelligence. For example:

  1. Data mining
  2. Online analytical processing
  3. Querying
  4. Reporting

A break down of Business Intelligence (BI) is made at Investopedia:

Business intelligence grew out of the conviction that managers with inaccurate or incomplete information will tend, on average, to make worse decisions than if they had better information. Creators of financial models will recognize this as a “garbage in, garbage out problem.” Business intelligence is meant to solve that problem by bringing in the most current data that is ideally presented in a dashboard of quick metrics designed to support better decisions.

As you can see, Business Intelligence is both simple and complex in all the ways it should be.

I would like to suggest that:

Business intelligence (BI) is growing out of the knowledge that decision makers with selective/subjective, inaccurate, reliable, manipulated or incomplete information tend, on average, to make catastrophic decisions. This, as compared to better decisions made based on accurate, reliable, non-manipulated and complete information from multiple objective sources.

In decision making, as in Business Intelligence, the keywords are “Crap in, Crap out.” Therefore, BI’s main goal should be to reduce the challenges faced in Decision Making by bringing in the most current data – accurate, reliable, non-manipulated and complete – from multiple objective sources.

To make this data available and fathomable for decision makers, it should ideally be visualized in user friendly presentation tools chosen with great consideration for the specific decision maker’s needs and technical maturity.

To explain what I mean, I will give the example of the yearly/quarterly comparison reports for BI tools. Every new report announces the latest “best” tool in different categories.

The only message missing from the comparison report is:cropped-db-image.png

Please note before you purchase this fantastic BI tool: It doesn’t matter how fantastic the GUI (Graphic User Interface) is, in BI & analytics, if the data is crap, the decision maker will not be jubilant about the GUI.

One of the lessons I have learnt working with BI is that, the frustration caused by unreliable data, overrides the pleasure of a beautiful GUI, every single time.